From f9e97050e4537f776dd501d5e6545fa5d83fed1d Mon Sep 17 00:00:00 2001 From: jmgirard Date: Thu, 24 Oct 2024 23:04:36 -0500 Subject: [PATCH] update vignettes --- R/ssm_analysis.R | 6 +- R/ssm_visualization.R | 17 +- README.Rmd | 33 +- README.md | 165 +++- _pkgdown.yml | 4 +- .../articles/intermediate-ssm-analysis.html | 732 ++++++++++-------- .../figure-html/general_plot-1.png | Bin 145065 -> 145092 bytes .../figure-html/group_contrast_plot-1.png | Bin 48875 -> 67912 bytes .../figure-html/measure_contrast_plot-1.png | Bin 52931 -> 69823 bytes .../figure-html/model_plot-1.png | Bin 111658 -> 67967 bytes .../introduction-to-ssm-analysis.html | 211 ++--- .../figure-html/plot1-1.png | Bin 107163 -> 106666 bytes docs/dev/articles/using-instruments.html | 325 ++++---- docs/dev/index.html | 159 +++- docs/dev/news/index.html | 7 +- docs/dev/pkgdown.yml | 2 +- docs/dev/reference/anchors.html | 1 + docs/dev/reference/html_render.html | 1 - docs/dev/reference/iis64.html | 4 +- docs/dev/reference/index.html | 14 +- docs/dev/reference/instrument.html | 5 +- docs/dev/reference/ipsatize.html | 238 +++++- docs/dev/reference/items.html | 1 + docs/dev/reference/norms.html | 1 + docs/dev/reference/scales.html | 1 + docs/dev/reference/score.html | 118 ++- docs/dev/reference/ssm_analyze.html | 309 ++++---- docs/dev/reference/ssm_parameters.html | 47 +- docs/dev/reference/ssm_plot.html | 16 +- docs/dev/reference/ssm_plot_contrast.html | 31 +- docs/dev/reference/ssm_score.html | 15 +- docs/dev/reference/ssm_table.html | 98 ++- docs/dev/sitemap.xml | 6 + man/figures/README-plot2-1.png | Bin 0 -> 8076 bytes vignettes/intermediate-ssm-analysis.Rmd | 160 ++-- vignettes/introduction-to-ssm-analysis.Rmd | 67 +- vignettes/using-instruments.Rmd | 46 +- 37 files changed, 1740 insertions(+), 1100 deletions(-) create mode 100644 man/figures/README-plot2-1.png diff --git a/R/ssm_analysis.R b/R/ssm_analysis.R index 7980ce04..f5f9a711 100644 --- a/R/ssm_analysis.R +++ b/R/ssm_analysis.R @@ -307,7 +307,7 @@ ssm_analyze_corrs <- function(data, scales, angles, measures, grouping, # Get names of measures (using labels if provided) if (is.null(measures_labels)) { - measure_labels <- measures_names + measures_labels <- measures_names } # Calculate observed correlation scores @@ -322,7 +322,7 @@ ssm_analyze_corrs <- function(data, scales, angles, measures, grouping, } scores <- as.data.frame(scores) Group <- rep(group_levels, each = n_measures) - Measure <- rep(measure_labels, times = n_groups) + Measure <- rep(measures_labels, times = n_groups) if (contrast && is.null(grouping)) { Group <- c(Group, Group[[1]]) Measure <- c(Measure, paste0(Measure[[2]], " - ", Measure[[1]])) @@ -363,7 +363,7 @@ ssm_analyze_corrs <- function(data, scales, angles, measures, grouping, ) Group <- rep(group_levels, each = n_measures) - Measure <- rep(measure_labels, times = n_groups) + Measure <- rep(measures_labels, times = n_groups) if (contrast && is.null(grouping)) { Group <- c(Group, Group[[1]]) Measure <- c(Measure, paste0(Measure[[2]], " - ", Measure[[1]])) diff --git a/R/ssm_visualization.R b/R/ssm_visualization.R index 84f7cf0c..c7867247 100644 --- a/R/ssm_visualization.R +++ b/R/ssm_visualization.R @@ -291,7 +291,7 @@ ssm_plot_contrast <- function(.ssm_object, drop_xy = FALSE, ) + ggplot2::geom_errorbar( ggplot2::aes(x = "1", ymin = lci, ymax = uci), - linewidth = linesize, width = 0.1 + linewidth = linesize, width = 0.15 ) + ggplot2::geom_point( ggplot2::aes(x = "1", y = Difference, fill = sig), @@ -300,7 +300,7 @@ ssm_plot_contrast <- function(.ssm_object, drop_xy = FALSE, shape = 21 ) + ggplot2::scale_fill_manual( - "Signficant", + "Significant", values = c("TRUE" = sig_color, "FALSE" = ns_color) ) + ggplot2::labs(y = paste0("Contrast (", res$label, ")")) + @@ -453,19 +453,6 @@ ssm_table <- function(.ssm_object, caption = NULL, no = "Profile" ) - # Add delta symbol to column names if results are contrasts - if (.ssm_object$details$contrast) { - colnames(table_df) <- c( - "Contrast", - "Δ Elevation", - "Δ X Value", - "Δ Y Value", - "Δ Amplitude", - "Δ Displacement", - "Δ Fit" - ) - } - # Drop the x and y columns if requested if (drop_xy) { table_df <- table_df[, -c(3, 4)] diff --git a/README.Rmd b/README.Rmd index 07c9537e..2680aa77 100644 --- a/README.Rmd +++ b/README.Rmd @@ -41,6 +41,8 @@ devtools::install_github("jmgirard/circumplex") ## Usage +### Example 1 + ```{r analysis1} data("jz2017") results <- ssm_analyze( @@ -54,11 +56,11 @@ summary(results) ``` ``` r -ssm_table(results, xy = FALSE) +ssm_table(results, drop_xy = TRUE) ``` ```{r table1, echo = FALSE, results = "asis"} -ssm_table(results, xy = FALSE, render = FALSE) %>% +ssm_table(results, drop_xy = TRUE, render = FALSE) %>% knitr::kable(caption = circumplex:::dcaption(results)) %>% kableExtra::kable_styling(full_width = TRUE, font_size = 12) ``` @@ -67,6 +69,33 @@ ssm_table(results, xy = FALSE, render = FALSE) %>% ssm_plot(results) ``` +### Example 2 + +```{r analysis2} +results2 <- ssm_analyze( + data = jz2017, + scales = PANO(), + angles = octants(), + grouping = "Gender", + contrast = TRUE +) +summary(results2) +``` + +``` r +ssm_table(results2, drop_xy = TRUE) +``` + +```{r table2, echo = FALSE, results = "asis"} +ssm_table(results2, drop_xy = TRUE, render = FALSE) %>% + knitr::kable(caption = circumplex:::dcaption(results)) %>% + kableExtra::kable_styling(full_width = TRUE, font_size = 12) +``` + +```{r plot2} +ssm_plot(results2) +``` + ## Code of Conduct Please note that the 'circumplex' project is released with a [Contributor Code of Conduct](https://circumplex.jmgirard.com/CODE_OF_CONDUCT.html). diff --git a/README.md b/README.md index 3d75a99b..47b1a590 100644 --- a/README.md +++ b/README.md @@ -38,21 +38,18 @@ devtools::install_github("jmgirard/circumplex") ## Usage +### Example 1 + ``` r data("jz2017") results <- ssm_analyze( - .data = jz2017, - scales = c(PA, BC, DE, FG, HI, JK, LM, NO), + data = jz2017, + scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), angles = c(90, 135, 180, 225, 270, 315, 360, 45), - measures = c(NARPD, ASPD), + measures = c("NARPD", "ASPD"), measures_labels = c("Narcissistic PD", "Antisocial PD") ) summary(results) -#> Call: -#> ssm_analyze(.data = jz2017, scales = c(PA, BC, DE, FG, HI, JK, -#> LM, NO), angles = c(90, 135, 180, 225, 270, 315, 360, 45), -#> measures = c(NARPD, ASPD), measures_labels = c("Narcissistic PD", -#> "Antisocial PD")) #> #> Statistical Basis: Correlation Scores #> Bootstrap Resamples: 2000 @@ -60,7 +57,9 @@ summary(results) #> Listwise Deletion: TRUE #> Scale Displacements: 90 135 180 225 270 315 360 45 #> -#> Profile [Narcissistic PD]: +#> +#> # Profile [Narcissistic PD]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.202 0.169 0.238 #> X-Value -0.062 -0.094 -0.029 @@ -69,7 +68,9 @@ summary(results) #> Displacement 108.967 98.633 118.537 #> Model Fit 0.957 #> -#> Profile [Antisocial PD]: +#> +#> # Profile [Antisocial PD]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.124 0.087 0.158 #> X-Value -0.099 -0.133 -0.064 @@ -80,10 +81,10 @@ summary(results) ``` ``` r -ssm_table(results, xy = FALSE) +ssm_table(results, drop_xy = TRUE) ``` - +
@@ -150,6 +151,146 @@ ssm_plot(results) ![](man/figures/README-plot-1.png) +### Example 2 + +``` r +results2 <- ssm_analyze( + data = jz2017, + scales = PANO(), + angles = octants(), + grouping = "Gender", + contrast = TRUE +) +summary(results2) +#> +#> Statistical Basis: Mean Scores +#> Bootstrap Resamples: 2000 +#> Confidence Level: 0.95 +#> Listwise Deletion: TRUE +#> Scale Displacements: 90 135 180 225 270 315 360 45 +#> +#> +#> # Profile [Female]: +#> +#> Estimate Lower CI Upper CI +#> Elevation 0.946 0.909 0.984 +#> X-Value 0.459 0.421 0.498 +#> Y-Value -0.310 -0.355 -0.267 +#> Amplitude 0.554 0.511 0.598 +#> Displacement 325.963 322.182 330.063 +#> Model Fit 0.889 +#> +#> +#> # Profile [Male]: +#> +#> Estimate Lower CI Upper CI +#> Elevation 0.884 0.843 0.923 +#> X-Value 0.227 0.192 0.262 +#> Y-Value -0.186 -0.225 -0.148 +#> Amplitude 0.294 0.257 0.329 +#> Displacement 320.685 313.462 327.843 +#> Model Fit 0.824 +#> +#> +#> # Contrast [Male - Female]: +#> +#> Estimate Lower CI Upper CI +#> Δ Elevation -0.062 -0.118 -0.005 +#> Δ X-Value -0.232 -0.283 -0.181 +#> Δ Y-Value 0.124 0.067 0.183 +#> Δ Amplitude -0.261 -0.318 -0.202 +#> Δ Displacement -5.278 -13.353 2.755 +#> Δ Model Fit -0.066 +``` + +``` r +ssm_table(results2, drop_xy = TRUE) +``` + +
Correlation-based Structural Summary Statistics with 95% CIs
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Correlation-based Structural Summary Statistics with 95% CIs +
+Contrast + +Elevation + +Amplitude + +Displacement + +Fit +
+Female + +0.95 (0.91, 0.98) + +0.55 (0.51, 0.60) + +326.0 (322.2, 330.1) + +0.889 +
+Male + +0.88 (0.84, 0.92) + +0.29 (0.26, 0.33) + +320.7 (313.5, 327.8) + +0.824 +
+Male - Female + +-0.06 (-0.12, -0.01) + +-0.26 (-0.32, -0.20) + +-5.3 (-13.4, 2.8) + +-0.066 +
+ +``` r +ssm_plot(results2) +``` + +![](man/figures/README-plot2-1.png) + ## Code of Conduct Please note that the ‘circumplex’ project is released with a diff --git a/_pkgdown.yml b/_pkgdown.yml index 05d195df..16b9b09a 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -30,7 +30,7 @@ reference: contents: - ipsatize - score - - standardize + - norm_standardize - title: Primary SSM Functions desc: Functions for implementing the Structural Summary Method contents: @@ -42,7 +42,6 @@ reference: - title: Secondary SSM Functions desc: Functions for customizing SSM results contents: - - ssm_append - ssm_plot_circle - ssm_plot_contrast - ssm_plot_scores @@ -53,6 +52,7 @@ reference: - octants - poles - quadrants + - PANO - title: Example Data contents: - aw2009 diff --git a/docs/dev/articles/intermediate-ssm-analysis.html b/docs/dev/articles/intermediate-ssm-analysis.html index 92e5b32d..97af22ad 100644 --- a/docs/dev/articles/intermediate-ssm-analysis.html +++ b/docs/dev/articles/intermediate-ssm-analysis.html @@ -107,16 +107,16 @@

Generalizing to multiple means argument to the function call.

Generalizing to multiple measures @@ -248,16 +243,19 @@

Generalizing to multiple measuresmeasures argument instead of a single variable. This can be done by wrapping the variable names or column numbers with the -c() or, if they are adjacent columns, with the -: shortcut. The package functions were written to analyze -all measures and groups within a single bootstrap, so adding additional -measures and groups should still be fast.

-
-results2 <- ssm_analyze(jz2017, PA:NO, octants(), measures = c(NARPD, ASPD))
+c() or, if they are adjacent columns, with the column
+numbers and the : shortcut. The package functions were
+written to analyze all measures and groups within a single
+bootstrapping, so adding additional measures and groups should still be
+fast.

+ -
+
 ssm_table(results2)
@@ -296,10 +298,10 @@

Generalizing to multiple measures

-X-Value +X.Value -Y-Value +Y.Value Amplitude @@ -360,7 +362,7 @@

Generalizing to multiple measures

-
+
 ssm_plot(results2)

@@ -371,13 +373,16 @@

Generalizing to m groups. As you might expect, this requires providing both the measures and grouping arguments to the same function call. Again, any number of measures and groups is possible. The -profiles in such an analysis will be named GROUP_MEASURE as below.

-
-results3 <- ssm_analyze(jz2017, PA:NO, octants(), grouping = Gender, measures = PARPD:SZTPD)
+profiles in such an analysis will be named “Measure: Group” as
+below.

+
+results3 <- ssm_analyze(
+  data = jz2017, 
+  scales = PANO(), 
+  angles = octants(), 
+  grouping = "Gender", 
+  measures = 10:12)
 summary(results3)
-#> Call:
-#> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), 
-#>     measures = PARPD:SZTPD, grouping = Gender)
 #> 
 #> Statistical Basis:    Correlation Scores 
 #> Bootstrap Resamples:  2000 
@@ -385,7 +390,9 @@ 

Generalizing to m #> Listwise Deletion: TRUE #> Scale Displacements: 90 135 180 225 270 315 360 45 #> -#> Profile [Female_PARPD]: +#> +#> # Profile [PARPD: Female]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.262 0.220 0.305 #> X-Value -0.146 -0.192 -0.100 @@ -394,7 +401,9 @@

Generalizing to m #> Displacement 140.524 126.580 155.129 #> Model Fit 0.870 #> -#> Profile [Female_SCZPD]: +#> +#> # Profile [SCZPD: Female]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.177 0.130 0.218 #> X-Value -0.230 -0.285 -0.173 @@ -403,7 +412,9 @@

Generalizing to m #> Displacement 191.613 178.684 208.113 #> Model Fit 0.845 #> -#> Profile [Female_SZTPD]: +#> +#> # Profile [SZTPD: Female]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.237 0.194 0.280 #> X-Value -0.130 -0.175 -0.087 @@ -412,7 +423,9 @@

Generalizing to m #> Displacement 172.870 151.247 198.040 #> Model Fit 0.812 #> -#> Profile [Male_PARPD]: +#> +#> # Profile [PARPD: Male]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.243 0.193 0.288 #> X-Value -0.035 -0.080 0.015 @@ -421,7 +434,9 @@

Generalizing to m #> Displacement 106.881 82.677 129.996 #> Model Fit 0.648 #> -#> Profile [Male_SCZPD]: +#> +#> # Profile [SCZPD: Male]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.204 0.145 0.260 #> X-Value -0.178 -0.220 -0.136 @@ -430,7 +445,9 @@

Generalizing to m #> Displacement 185.351 167.551 203.091 #> Model Fit 0.917 #> -#> Profile [Male_SZTPD]: +#> +#> # Profile [SZTPD: Male]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.240 0.186 0.293 #> X-Value -0.037 -0.084 0.011 @@ -438,7 +455,7 @@

Generalizing to m #> Amplitude 0.047 0.013 0.101 #> Displacement 142.345 68.178 218.702 #> Model Fit 0.424

-
+
 ssm_table(results3)
@@ -452,10 +469,10 @@

Generalizing to m Elevation

-X-Value +X.Value -Y-Value +Y.Value Amplitude @@ -470,7 +487,7 @@

Generalizing to m

-Female_PARPD +PARPD: Female 0.26 (0.22, 0.30) @@ -493,7 +510,7 @@

Generalizing to m

-Female_SCZPD +SCZPD: Female 0.18 (0.13, 0.22) @@ -516,7 +533,7 @@

Generalizing to m

-Female_SZTPD +SZTPD: Female 0.24 (0.19, 0.28) @@ -539,7 +556,7 @@

Generalizing to m

-Male_PARPD +PARPD: Male 0.24 (0.19, 0.29) @@ -562,7 +579,7 @@

Generalizing to m

-Male_SCZPD +SCZPD: Male 0.20 (0.15, 0.26) @@ -585,7 +602,7 @@

Generalizing to m

-Male_SZTPD +SZTPD: Male 0.24 (0.19, 0.29) @@ -608,41 +625,46 @@

Generalizing to m

-
+
 ssm_plot(results3)

+

Note that the borders for the “PARDPD: Male” and “SZTPD: Male” blocks +are dashed instead of solid. This indicates that these profiles have low +fit (i.e., +R2<.7R^2<.7). +We could alternatively hide these such profiles by adding +lowfit = FALSE as below (noting that the color guide may +change as profiles are dropped).

+
+ssm_plot(results3, lowfit = FALSE)
+

2. Contrast analyses

-

The final argument to master is contrast, which allows -us to compare two groups or measures. The value of this argument must be -one of the following strings: “none” for no contrast (default), “model” -for a model contrast, or “test” for a parameter contrast. A model -contrast can be useful for mean-based analyses and first generates -scores for each group (or measure), then calculates the difference -between these scores, and finally estimates SSM parameters for these -difference scores. A parameter contrast is typically what is desired for -inferential comparison and first generates scores for each group (or -measure), then estimates SSM parameters for these scores, and finally -calculates the difference between the parameters. To keep the code -simpler and to discourage “fishing expeditions,” only two groups or +

The final argument to master in this vignette is +contrast, which allows us to compare two groups or +measures. Setting this argument to TRUE will first generate scores for +each group (or measure), then estimate SSM parameters for these scores, +and finally calculate the difference between the parameters. To keep the +code simpler and to discourage “fishing expeditions,” only two groups or measures can be compared at a time.

Contrasts between groups’ means

To compare the mean profiles for females and males, we can start with the same syntax we had before and then add a contrast -argument. For the sake of illustration, we will use a model contrast -here, but for some purposes, a parameter contrast might be more -appropriate.

+argument.

-results4 <- ssm_analyze(jz2017s, PA:NO, octants(), grouping = Gender, contrast = "model")
+results4 <- ssm_analyze(
+  data = jz2017, 
+  scales = PANO(), 
+  angles = octants(), 
+  grouping = "Gender", 
+  contrast = TRUE
+)
 summary(results4)
-#> Call:
-#> ssm_analyze(.data = jz2017s, scales = PA:NO, angles = octants(), 
-#>     grouping = Gender, contrast = "model")
 #> 
 #> Statistical Basis:    Mean Scores 
 #> Bootstrap Resamples:  2000 
@@ -650,22 +672,43 @@ 

Contrasts between groups’ means#> Listwise Deletion: TRUE #> Scale Displacements: 90 135 180 225 270 315 360 45 #> -#> Contrast [Male - Female]: +#> +#> # Profile [Female]: +#> #> Estimate Lower CI Upper CI -#> Elevation -0.062 -0.119 -0.004 -#> X-Value -0.232 -0.285 -0.181 -#> Y-Value 0.124 0.066 0.183 -#> Amplitude 0.263 0.209 0.321 -#> Displacement 151.858 141.234 164.387 -#> Model Fit 0.855

-

Note that the profile blocks we are used to have been replaced by a -contrast block. By default, the contrast is made by subtracting the -first level of the grouping variable from the second level (e.g., Male - -Female). This provides an indication of the direction of the contrast. -We can again generate a table and figure to display the results. Note -that, because we used a model contrast, the figure will be a circular -figure and not a contrast figure which we will see below. However, we -would get a contrast figure if we used a parameter contrast.

+#> Elevation 0.946 0.908 0.984 +#> X-Value 0.459 0.419 0.498 +#> Y-Value -0.310 -0.353 -0.269 +#> Amplitude 0.554 0.510 0.599 +#> Displacement 325.963 322.251 329.698 +#> Model Fit 0.889 +#> +#> +#> # Profile [Male]: +#> +#> Estimate Lower CI Upper CI +#> Elevation 0.884 0.841 0.925 +#> X-Value 0.227 0.191 0.261 +#> Y-Value -0.186 -0.225 -0.148 +#> Amplitude 0.294 0.256 0.331 +#> Displacement 320.685 313.293 327.938 +#> Model Fit 0.824 +#> +#> +#> # Contrast [Male - Female]: +#> +#> Estimate Lower CI Upper CI +#> Δ Elevation -0.062 -0.119 -0.004 +#> Δ X-Value -0.232 -0.285 -0.181 +#> Δ Y-Value 0.124 0.066 0.183 +#> Δ Amplitude -0.261 -0.318 -0.203 +#> Δ Displacement -5.278 -13.627 2.178 +#> Δ Model Fit -0.066
+

Note that we have the profile blocks for each group as well as a +contrast block. The contrast is made by subtracting the first level of +the grouping variable from the second level (e.g., Male - Female). This +provides an indication of the direction of the contrast. We can again +generate a table and figure to display the results.

 ssm_table(results4)
@@ -680,10 +723,10 @@

Contrasts between groups’ means

- + + + + + + + + + + + + + + + + + + + + @@ -709,15 +799,16 @@

Contrasts between groups’ means

- + +
-X-Value +X.Value -Y-Value +Y.Value Amplitude @@ -695,7 +738,54 @@

Contrasts between groups’ means

+Female + +0.95 (0.91, 0.98) + +0.46 (0.42, 0.50) + +-0.31 (-0.35, -0.27) + +0.55 (0.51, 0.60) + +326.0 (322.3, 329.7) + +0.889 +
+Male + +0.88 (0.84, 0.93) + +0.23 (0.19, 0.26) + +-0.19 (-0.22, -0.15) + +0.29 (0.26, 0.33) + +320.7 (313.3, 327.9) + +0.824 +
Male - Female -0.26 (0.21, 0.32) +-0.26 (-0.32, -0.20) -151.9 (141.2, 164.4) +-5.3 (-13.6, 2.2) -0.855 +-0.066
 ssm_plot(results4)
@@ -731,12 +822,14 @@

Contrasts between measures in a g containing measures. Here we will use a parameter contrast to see what they look like.

-results5 <- ssm_analyze(jz2017, PA:NO, octants(), measures = c(NARPD, ASPD),
-  contrast = "test")
+results5 <- ssm_analyze(
+  data = jz2017, 
+  scales = PANO(), 
+  angles = octants(), 
+  measures = c("NARPD", "ASPD"),
+  contrast = TRUE
+)
 summary(results5)
-#> Call:
-#> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), 
-#>     measures = c(NARPD, ASPD), contrast = "test")
 #> 
 #> Statistical Basis:    Correlation Scores 
 #> Bootstrap Resamples:  2000 
@@ -744,14 +837,38 @@ 

Contrasts between measures in a g #> Listwise Deletion: TRUE #> Scale Displacements: 90 135 180 225 270 315 360 45 #> -#> Contrast [ASPD - NARPD]: +#> +#> # Profile [NARPD]: +#> #> Estimate Lower CI Upper CI -#> Elevation -0.079 -0.114 -0.043 -#> X-Value -0.037 -0.074 0.002 -#> Y-Value 0.024 -0.014 0.061 -#> Amplitude 0.037 -0.002 0.075 -#> Displacement 6.960 -3.391 17.383 -#> Model Fit 0.007

+#> Elevation 0.202 0.169 0.233 +#> X-Value -0.062 -0.097 -0.029 +#> Y-Value 0.179 0.144 0.212 +#> Amplitude 0.189 0.154 0.226 +#> Displacement 108.967 99.122 118.902 +#> Model Fit 0.957 +#> +#> +#> # Profile [ASPD]: +#> +#> Estimate Lower CI Upper CI +#> Elevation 0.124 0.091 0.159 +#> X-Value -0.099 -0.135 -0.065 +#> Y-Value 0.203 0.166 0.237 +#> Amplitude 0.226 0.190 0.265 +#> Displacement 115.927 107.571 124.311 +#> Model Fit 0.964 +#> +#> +#> # Contrast [ASPD - NARPD]: +#> +#> Estimate Lower CI Upper CI +#> Δ Elevation -0.079 -0.114 -0.043 +#> Δ X-Value -0.037 -0.074 0.002 +#> Δ Y-Value 0.024 -0.014 0.061 +#> Δ Amplitude 0.037 -0.002 0.075 +#> Δ Displacement 6.960 -3.391 17.383 +#> Δ Model Fit 0.007

 ssm_table(results5)
@@ -763,25 +880,72 @@

Contrasts between measures in a g Contrast

- + + + + + + + + + + + + + + + + + + + + @@ -803,7 +967,8 @@

Contrasts between measures in a g

- + +
-&Delta; Elevation +Elevation -&Delta; X-Value +X.Value -&Delta; Y-Value +Y.Value -&Delta; Amplitude +Amplitude -&Delta; Displacement +Displacement -&Delta; Fit +Fit
+NARPD + +0.20 (0.17, 0.23) + +-0.06 (-0.10, -0.03) + +0.18 (0.14, 0.21) + +0.19 (0.15, 0.23) + +109.0 (99.1, 118.9) + +0.957 +
+ASPD + +0.12 (0.09, 0.16) + +-0.10 (-0.13, -0.06) + +0.20 (0.17, 0.24) + +0.23 (0.19, 0.26) + +115.9 (107.6, 124.3) + +0.964 +
ASPD - NARPD 0.007
 ssm_plot(results5)
@@ -823,15 +988,18 @@

Contrasts between groups’ corre of contrast. In this case, we need to ensure that we are providing only a single measure and a grouping variable with just two levels (as again only two things can be contrasted at a time). Note that -the contrast name in this case will take the form of MEASURE: GROUP2 - -GROUP1.

+the contrast name in this case will take the form of “Measure: Group2 - +Group1”.

-results6 <- ssm_analyze(jz2017, PA:NO, octants(), measures = BORPD, 
-  grouping = Gender, contrast = "test")
+results6 <- ssm_analyze(
+  data = jz2017, 
+  scales = PANO(), 
+  angles = octants(), 
+  measures = "BORPD", 
+  grouping = "Gender", 
+  contrast = TRUE
+)
 summary(results6)
-#> Call:
-#> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), 
-#>     measures = BORPD, grouping = Gender, contrast = "test")
 #> 
 #> Statistical Basis:    Correlation Scores 
 #> Bootstrap Resamples:  2000 
@@ -839,14 +1007,38 @@ 

Contrasts between groups’ corre #> Listwise Deletion: TRUE #> Scale Displacements: 90 135 180 225 270 315 360 45 #> -#> Contrast [BORPD: Male - Female]: +#> +#> # Profile [BORPD: Female]: +#> +#> Estimate Lower CI Upper CI +#> Elevation 0.251 0.206 0.293 +#> X-Value -0.068 -0.114 -0.021 +#> Y-Value 0.126 0.064 0.184 +#> Amplitude 0.143 0.084 0.203 +#> Displacement 118.247 99.973 138.946 +#> Model Fit 0.947 +#> +#> +#> # Profile [BORPD: Male]: +#> #> Estimate Lower CI Upper CI -#> Elevation 0.069 0.000 0.136 -#> X-Value 0.080 0.017 0.146 -#> Y-Value -0.072 -0.152 0.010 -#> Amplitude -0.088 -0.160 -0.003 -#> Displacement -40.880 -105.533 19.259 -#> Model Fit -0.447

+#> Elevation 0.320 0.267 0.370 +#> X-Value 0.012 -0.034 0.058 +#> Y-Value 0.054 0.004 0.106 +#> Amplitude 0.055 0.016 0.109 +#> Displacement 77.367 12.983 133.991 +#> Model Fit 0.500 +#> +#> +#> # Contrast [BORPD: Male - Female]: +#> +#> Estimate Lower CI Upper CI +#> Δ Elevation 0.069 0.000 0.136 +#> Δ X-Value 0.080 0.017 0.146 +#> Δ Y-Value -0.072 -0.152 0.010 +#> Δ Amplitude -0.088 -0.160 -0.003 +#> Δ Displacement -40.880 -105.533 19.259 +#> Δ Model Fit -0.447

 ssm_table(results6)
@@ -858,25 +1050,72 @@

Contrasts between groups’ corre Contrast

- + + + + + + + + + + + + + + + + + + + + @@ -898,7 +1137,8 @@

Contrasts between groups’ corre

- + +
-&Delta; Elevation +Elevation -&Delta; X-Value +X.Value -&Delta; Y-Value +Y.Value -&Delta; Amplitude +Amplitude -&Delta; Displacement +Displacement -&Delta; Fit +Fit
+BORPD: Female + +0.25 (0.21, 0.29) + +-0.07 (-0.11, -0.02) + +0.13 (0.06, 0.18) + +0.14 (0.08, 0.20) + +118.2 (100.0, 138.9) + +0.947 +
+BORPD: Male + +0.32 (0.27, 0.37) + +0.01 (-0.03, 0.06) + +0.05 (0.00, 0.11) + +0.06 (0.02, 0.11) + +77.4 (13.0, 134.0) + +0.500 +
BORPD: Male - Female -0.447
 ssm_plot(results6)
@@ -924,8 +1164,6 @@

3. Taxonomy of analysis types - -
Arguments Needed @@ -933,9 +1171,6 @@

3. Taxonomy of analysis types -# - Usage @@ -952,122 +1187,101 @@

3. Taxonomy of analysis types -1 - Examine overall mean profile -<span style=” color: black !important;” >FALSE</span> +FALSE -<span style=” color: black !important;” >FALSE</span> +FALSE -<span style=” color: black !important;” >FALSE</span> +FALSE - -2 - Examine groups’ mean profiles -<span style=” color: black !important;” >FALSE</span> +FALSE -<span style=” color: blue !important;” >TRUE</span> +TRUE -<span style=” color: black !important;” >FALSE</span> +FALSE - -3 - Compare groups’ mean profiles -<span style=” color: black !important;” >FALSE</span> +FALSE -<span style=” color: blue !important;” >TRUE</span> +TRUE -<span style=” color: blue !important;” >TRUE</span> +TRUE - -4 - Examine variables’ correlation profiles -<span style=” color: blue !important;” >TRUE</span> +TRUE -<span style=” color: black !important;” >FALSE</span> +FALSE -<span style=” color: black !important;” >FALSE</span> +FALSE - -5 - Compare variables’ correlation profiles -<span style=” color: blue !important;” >TRUE</span> +TRUE -<span style=” color: black !important;” >FALSE</span> +FALSE -<span style=” color: blue !important;” >TRUE</span> +TRUE - -6 - Examine groups’ correlation profiles -<span style=” color: blue !important;” >TRUE</span> +TRUE -<span style=” color: blue !important;” >TRUE</span> +TRUE -<span style=” color: black !important;” >FALSE</span> +FALSE - -7 - Compare groups’ correlation profiles -<span style=” color: blue !important;” >TRUE</span> +TRUE -<span style=” color: blue !important;” >TRUE</span> +TRUE -<span style=” color: blue !important;” >TRUE</span> +TRUE @@ -1089,11 +1303,11 @@

3. Taxonomy of analysis types @@ -1104,135 +1318,19 @@

Basic customizations of tablesssm_table() function can be explored using the ?ssm_table command. Two useful options -are the xy argument, which shows or hides the x-value and -y-value columns, and the caption argument which allows a -custom string to be printed above the table. Note as well that the +are the drop_xy argument, which shows or hides the x-value +and y-value columns, and the caption argument which allows +a custom string to be printed above the table. Note as well that the return object of this function is just a tibble or data frame, which can be easily edited to add, change, or remove text. To change the formatting of the table, see the htmlTable or kableExtra packages.

-
-

Combining and exporting tables -

-

In some cases, such as multiple contrasts or groupings, we may want -to combine several tables into one by joining them together as rows. -This can be done using the ssm_append() function.

-
-res1 <- ssm_analyze(jz2017s, PA:NO, octants())
-res2 <- ssm_analyze(jz2017s, PA:NO, octants(), grouping = Gender)
-tab1 <- ssm_table(res1, xy = FALSE, render = FALSE)
-tab2 <- ssm_table(res2, xy = FALSE, render = FALSE)
-ssm_append(tab1, tab2)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-Profile - -Elevation - -Amplitude - -Displacement - -Fit -
-All - -0.92 (0.89, 0.94) - -0.43 (0.40, 0.46) - -324.3 (320.7, 327.8) - -0.878 -
-Female - -0.95 (0.91, 0.98) - -0.55 (0.51, 0.60) - -326.0 (322.0, 330.0) - -0.889 -
-Male - -0.88 (0.84, 0.93) - -0.29 (0.26, 0.33) - -320.7 (313.5, 328.2) - -0.824 -
-

5. Working with SSM figures

-

Basic customizations of figures -

-

Additional arguments to the ssm_plot() function can be -explored using the ?ssm_plot, -?ssm_plot_circle, and ?ssm_plot_contrast -commands. For both types of figures, the fontsize argument -will control the baseline text size in pts (default = 12). Some text -elements will be rescaled from the specified fontsize -(e.g., multiplying it by 150%). For circle plots, we have already seen -the lowfit argument for including points with low model -fit. Another useful option is the amax argument for the -maximum value of the amplitude scale. By default, the package picks a -number that will fit all the points and generate “round” numbers at each -1/5 tick; however, this default can be overwritten using the -amax argument.

-
-ssm_plot(results4, amax = 0.6)
-

-

For contrast plots, several arguments exist for customizing the -output. We have already seen the xy argument for including -or excluding the x-value and y-value contrasts. We can also change the -color and linesize of the point range -(linesize in mm). Finally, we can change the axislabel -displayed to the left of the plots. More complex customizations will be -the subject of an advanced vignette.

-
-ssm_plot(results6, xy = FALSE, color = "blue", linesize = 1,
-  axislabel = "BORPD: Male - Female")
-

-
-

Exporting figures as files

All SSM plots are created using the ggplot2 package, @@ -1248,11 +1346,9 @@

Exporting figures as files -
-p <- ssm_plot(results6, xy = FALSE, color = "blue", linesize = 1,
-  axislabel = "BORPD: Male - Female")
-ggsave(filename = "bordpd_gender.png", plot = p, width = 7.5, height = 4,
-  units = "in")
+
+ssm_plot(results6)
+ggsave("bordpd_gender.png", width = 7.5, height = 4, units = "in")

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    2. Example data: jz2017?jz2017 command in R.

     data("jz2017")
    -print(jz2017)
    -#> # A tibble: 1,166 × 19
    -#>    Gender    PA    BC    DE    FG    HI    JK    LM    NO PARPD SCZPD SZTPD
    -#>    <fct>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <int>
    -#>  1 Female  1.5   1.5   1.25  1     2     2.5   2.25  2.5      4     3     7
    -#>  2 Female  0     0.25  0     0.25  1.25  1.75  2.25  2.25     1     0     2
    -#>  3 Female  0     0     0     0     0     0     0     0        0     1     0
    -#>  4 Male    2     1.75  1.75  2.5   2     1.75  2     2.5      1     0     0
    -#>  5 Female  0.25  0.5   0.25  0     0     0     0     0        0     0     0
    -#>  6 Male    1.5   1.75  2.25  1.75  2     1.25  2.25  2.5      5     5     7
    -#>  7 Male    2     1.75  1.75  2     1.5   1.25  1.25  1.75     3     6     7
    -#>  8 Female  0     0.25  0.25  0.25  1     1.25  1.25  1        0     0     1
    -#>  9 Female  0     0     0     0     0.25  0.25  1     0        0     1     0
    -#> 10 Male    0.75  2     1.75  2.5   2     1.5   2     2.25     4     5     5
    -#> # ℹ 1,156 more rows
    -#> # ℹ 7 more variables: ASPD <int>, BORPD <int>, HISPD <int>, NARPD <int>,
    -#> #   AVPD <int>, DPNPD <int>, OCPD <int>
    +head(jz2017) +#> Gender PA BC DE FG HI JK LM NO PARPD SCZPD SZTPD ASPD BORPD +#> 1 Female 1.50 1.50 1.25 1.00 2.00 2.50 2.25 2.50 4 3 7 7 8 +#> 2 Female 0.00 0.25 0.00 0.25 1.25 1.75 2.25 2.25 1 0 2 0 1 +#> 3 Female 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 1 0 4 1 +#> 4 Male 2.00 1.75 1.75 2.50 2.00 1.75 2.00 2.50 1 0 0 0 1 +#> 5 Female 0.25 0.50 0.25 0.00 0.00 0.00 0.00 0.00 0 0 0 0 1 +#> 6 Male 1.50 1.75 2.25 1.75 2.00 1.25 2.25 2.50 5 5 7 5 4 +#> HISPD NARPD AVPD DPNPD OCPD +#> 1 4 6 3 4 6 +#> 2 2 3 0 1 0 +#> 3 5 4 0 0 1 +#> 4 0 0 0 0 0 +#> 5 0 0 1 0 0 +#> 6 4 7 2 0 3

  • The circumplex scales in jz2017 come from the Inventory of Interpersonal Problems - Short Circumplex (IIP-SC). These scales can be arranged into the following circular model, which is organized around @@ -290,59 +288,23 @@

    Conducting SSM for a group’s

    To begin, let’s say that we want to use the SSM to describe the interpersonal problems of the average individual in the entire dataset. -Although it is possible to analyze the raw scores contained in -jz2017, our results will be more interpretable if we -standardize the scores first. We can do this using the -standardize() function. The first argument to this function -is .data, a data frame frame containing the circumplex -scales to be standardized. The second argument is scales -and specifies where in .data the circumplex scales are -(either in terms of their variable names or their column numbers). The -third argument is angles and specifies the angle of each of -the circumplex scales included in scales. Note that the -scales and angles arguments need to be vectors -(hence the c() function) that have the same ordering and -length. Finally, the fourth argument is norms, a data frame -containing the normative data we will use to standardize the circumplex -scales. Here, we will use normative data for the IIP-SC by loading the -iipsc data frame.

    +We will use the raw scores contained in jz2017, although +note that the results may be more interpretable if we standardized the +scores first (e.g., using the norm_standardize() function +as described in the “Using Circumplex Instruments” vignette).

    +

    We can use the ssm_analyze() function to perform the SSM +analysis. The first three arguments to this function are +data (the data frame containing our scores), +scales (a vector of the names or numbers of columns +containing the circumplex scales), and angles (a vector of +the circular angle of each scale, in degrees). Below, I spell out the +names of all the scales and the numbers of all the +angles for teaching purposes. Later on, we will learn some +shortcuts.

    -data("iipsc")
    -jz2017s <- standardize(
    -  .data = jz2017,
    -  scales = c(PA, BC, DE, FG, HI, JK, LM, NO),
    -  angles = c(90, 135, 180, 225, 270, 315, 360, 45),
    -  instrument = iipsc,
    -  sample = 1
    -)
    -print(jz2017s)
    -#> # A tibble: 1,166 × 27
    -#>    Gender    PA    BC    DE    FG    HI    JK    LM    NO PARPD SCZPD SZTPD
    -#>    <fct>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <int>
    -#>  1 Female  1.5   1.5   1.25  1     2     2.5   2.25  2.5      4     3     7
    -#>  2 Female  0     0.25  0     0.25  1.25  1.75  2.25  2.25     1     0     2
    -#>  3 Female  0     0     0     0     0     0     0     0        0     1     0
    -#>  4 Male    2     1.75  1.75  2.5   2     1.75  2     2.5      1     0     0
    -#>  5 Female  0.25  0.5   0.25  0     0     0     0     0        0     0     0
    -#>  6 Male    1.5   1.75  2.25  1.75  2     1.25  2.25  2.5      5     5     7
    -#>  7 Male    2     1.75  1.75  2     1.5   1.25  1.25  1.75     3     6     7
    -#>  8 Female  0     0.25  0.25  0.25  1     1.25  1.25  1        0     0     1
    -#>  9 Female  0     0     0     0     0.25  0.25  1     0        0     1     0
    -#> 10 Male    0.75  2     1.75  2.5   2     1.5   2     2.25     4     5     5
    -#> # ℹ 1,156 more rows
    -#> # ℹ 15 more variables: ASPD <int>, BORPD <int>, HISPD <int>, NARPD <int>,
    -#> #   AVPD <int>, DPNPD <int>, OCPD <int>, PA_z <dbl>, BC_z <dbl>, DE_z <dbl>,
    -#> #   FG_z <dbl>, HI_z <dbl>, JK_z <dbl>, LM_z <dbl>, NO_z <dbl>
    -

    Now we can use the ssm_analyze() function to perform the -SSM analysis. The first three arguments are the same as the first three -arguments to standardize(). We can pass the new -jz2017s data frame that contains standardized scores as -.data and the same vectors to scales and -angles since these haven’t changed.

    -
     results <- ssm_analyze(
    -  .data = jz2017s,
    -  scales = c(PA_z, BC_z, DE_z, FG_z, HI_z, JK_z, LM_z, NO_z),
    +  data = jz2017,
    +  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
       angles = c(90, 135, 180, 225, 270, 315, 360, 45)
     )

    The output of the function has been saved in the results @@ -352,12 +314,8 @@

    Conducting SSM for a group’s ?ssm_analyze to learn how to change them) and, most importantly, the estimated SSM parameter values with bootstrapped confidence intervals.

    -
    +
     summary(results)
    -#> Call:
    -#> ssm_analyze(.data = jz2017s, scales = c(PA_z, BC_z, DE_z, FG_z, 
    -#>     HI_z, JK_z, LM_z, NO_z), angles = c(90, 135, 180, 225, 270, 
    -#>     315, 360, 45))
     #> 
     #> Statistical Basis:    Mean Scores 
     #> Bootstrap Resamples:  2000 
    @@ -365,34 +323,29 @@ 

    Conducting SSM for a group’s #> Listwise Deletion: TRUE #> Scale Displacements: 90 135 180 225 270 315 360 45 #> -#> Profile [All]: +#> +#> # Profile [All]: +#> #> Estimate Lower CI Upper CI -#> Elevation -0.225 -0.258 -0.191 -#> X-Value 0.131 0.097 0.161 -#> Y-Value -0.015 -0.048 0.020 -#> Amplitude 0.131 0.098 0.165 -#> Displacement 353.525 339.484 9.564 -#> Model Fit 0.710

    +#> Elevation 0.917 0.890 0.945 +#> X-Value 0.351 0.324 0.377 +#> Y-Value -0.252 -0.280 -0.223 +#> Amplitude 0.432 0.402 0.461 +#> Displacement 324.292 320.827 327.766 +#> Model Fit 0.878

    That was pretty easy! We can now write up these results. However, the circumplex package has some features that can make what we -just did even easier. First, because the first three arguments of the -ssm_analyze() function are always the same, we can omit -their names. Second, because we organized the jz2017s data -frame to have the circumplex scale variables adjacent and in order from -PA to NO, we can simplify their specification by using the -PA:NO shortcut. Finally, because the use of octant scales -is so common, the circumplex package comes with a -convenience function for outputting their angular displacements: -octants(). Note how, even when using these shortcuts, the -results are the same except for minor stochastic differences in the -confidence intervals due to the randomness inherent to bootstrapping. -(To get the exact same results, we could use the set.seed() -function to control the random number generator in R.)

    -
    -results2 <- ssm_analyze(jz2017s, PA_z:NO_z, octants())
    +just did even easier. First, because we organized the
    +jz2017 data frame to have the circumplex scale variables
    +adjacent and in order from PA to NO, we can simplify their specification
    +by using the PANO() shortcut. Second, because the use of
    +octant scales is so common, we can use the octants()
    +shortcut. Note that, even when using these shortcuts, the results are
    +the same except for minor stochastic differences in the confidence
    +intervals due to the randomness inherent to bootstrapping.

    +
    +results2 <- ssm_analyze(data = jz2017, scales = PANO(), angles = octants())
     summary(results2)
    -#> Call:
    -#> ssm_analyze(.data = jz2017s, scales = PA_z:NO_z, angles = octants())
     #> 
     #> Statistical Basis:    Mean Scores 
     #> Bootstrap Resamples:  2000 
    @@ -400,14 +353,16 @@ 

    Conducting SSM for a group’s #> Listwise Deletion: TRUE #> Scale Displacements: 90 135 180 225 270 315 360 45 #> -#> Profile [All]: +#> +#> # Profile [All]: +#> #> Estimate Lower CI Upper CI -#> Elevation -0.225 -0.259 -0.192 -#> X-Value 0.131 0.100 0.165 -#> Y-Value -0.015 -0.050 0.019 -#> Amplitude 0.131 0.101 0.167 -#> Displacement 353.525 339.560 8.994 -#> Model Fit 0.710

    +#> Elevation 0.917 0.889 0.945 +#> X-Value 0.351 0.326 0.380 +#> Y-Value -0.252 -0.281 -0.225 +#> Amplitude 0.432 0.404 0.464 +#> Displacement 324.292 320.913 327.623 +#> Model Fit 0.878

    Visualizing the results with a table and figure @@ -418,7 +373,7 @@

    Visualizing the results To create the table, simply pass the results (or results2) object to the ssm_table() function.

    -
    +
     ssm_table(results2)
    @@ -432,10 +387,10 @@

    Visualizing the results Elevation

    -X-Value +X.Value -Y-Value +Y.Value Amplitude @@ -452,22 +407,22 @@

    Visualizing the results All

    --0.23 (-0.26, -0.19) +0.92 (0.89, 0.94) -0.13 (0.10, 0.17) +0.35 (0.33, 0.38) --0.01 (-0.05, 0.02) +-0.25 (-0.28, -0.23) -0.13 (0.10, 0.17) +0.43 (0.40, 0.46) -353.5 (339.6, 9.0) +324.3 (320.9, 327.6) -0.710 +0.878
    @@ -477,7 +432,7 @@

    Visualizing the results displacement parameter to the points’ rotation from due-east (as is conventional). This, again, is as simple as passing the results object to the ssm_plot() function.

    -
    +
     ssm_plot(results2)

    @@ -503,15 +458,15 @@

    Condu function and the data, scales, and angles are the same as before. However, we also need to let the function know that we want to analyze correlations with NARPD as opposed to scale means. To do this, we add an -additional argument measures. Note that since correlations -are already standardized, we don’t need to worry about standardizing the -circumplex scales when measures is used.

    -
    -results3 <- ssm_analyze(jz2017, PA:NO, octants(), measures = NARPD)
    +additional argument measures.

    +
    +results3 <- ssm_analyze(
    +  data = jz2017, 
    +  scales = PANO(), 
    +  angles = octants(),
    +  measures = "NARPD"
    +)
     summary(results3)
    -#> Call:
    -#> ssm_analyze(.data = jz2017, scales = PA:NO, angles = octants(), 
    -#>     measures = NARPD)
     #> 
     #> Statistical Basis:    Correlation Scores 
     #> Bootstrap Resamples:  2000 
    @@ -519,7 +474,9 @@ 

    Condu #> Listwise Deletion: TRUE #> Scale Displacements: 90 135 180 225 270 315 360 45 #> -#> Profile [NARPD]: +#> +#> # Profile [NARPD]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.202 0.170 0.237 #> X-Value -0.062 -0.096 -0.028 @@ -540,7 +497,7 @@

    Visualizing the resul ssm_plot() functions are smart enough to know whether the results are mean-based or correlation-based and will work in both cases.

    -
    +
     ssm_table(results3)
    @@ -554,10 +511,10 @@

    Visualizing the resul Elevation

    -X-Value +X.Value -Y-Value +Y.Value Amplitude @@ -604,7 +561,7 @@

    Visualizing the resul somewhere between 100 and 120 degrees; to interpret this we would need to either consult the mapping between scales and angles or plot the results.

    -
    +
     ssm_plot(results3)

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b/docs/dev/articles/using-instruments.html index 18ac49c1..9f598d96 100644 --- a/docs/dev/articles/using-instruments.html +++ b/docs/dev/articles/using-instruments.html @@ -91,8 +91,7 @@ +library(circumplex)
    @@ -124,7 +123,7 @@

    2. Loading and Examining Instrument Objects @@ -175,7 +174,7 @@

    Loading a specific instrumentinstrument() function. We can then examine that instrument data using the print() function.

    -instrument(csip)
    +instrument("csip")
     print(csip)
     #> CSIP: Circumplex Scales of Interpersonal Problems
     #> 64 items, 8 scales, 1 normative data sets
    @@ -192,7 +191,7 @@ 

    Loading a specific instrument#> Hopwood et al. (2011) #> <https://doi.org/10.1111/j.1467-6494.2011.00696.x>

    -instrument(isc)
    +instrument("isc")
     print(isc)
     #> ISC: Interpersonal Sensitivities Circumplex
     #> 64 items, 8 scales, 1 normative data sets
    @@ -383,23 +382,39 @@ 

    Ipsatizing item-level data

    @@ -414,33 +429,72 @@

    Ipsatizing item-level dataipsatize() function.

    -ips_iipsc <- ipsatize(.data = raw_iipsc, items = IIP01:IIP32, na.rm = TRUE, overwrite = TRUE)
    +ips_iipsc <- ipsatize(data = raw_iipsc, items = 1:32, append = FALSE)
     print(ips_iipsc)
    -#> # A tibble: 10 × 32
    -#>      IIP01   IIP02  IIP03  IIP04  IIP05   IIP06  IIP07   IIP08   IIP09   IIP10
    -#>      <dbl>   <dbl>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>
    -#>  1 -1      -1      -1     -1      0     -1       0     -1       1       0     
    -#>  2  0.0625  0.0625 -0.938 -0.938  2.06   1.06    1.06   0.0625 -0.938   0.0625
    -#>  3 -0.406  -1.41   -0.406 -1.41  -0.406 -0.406  -0.406  1.59   -1.41   -0.406 
    -#>  4  0.710  -0.290   0.710 NA     -0.290  0.710  -0.290  0.710  -0.290   0.710 
    -#>  5 -0.625  -0.625  -0.625  0.375 -0.625 -0.625   0.375  0.375  -0.625   0.375 
    -#>  6 -0.281  -0.281  -0.281 -0.281 -0.281 -0.281   0.719  0.719  -0.281  -0.281 
    -#>  7  0.5    -0.5    -0.5   -0.5    1.5    0.5     0.5   -0.5     0.5    -0.5   
    -#>  8  0.258  -0.742   0.258 -0.742  0.258  0.258   1.26   0.258   0.258  -0.742 
    -#>  9 -0.969  -0.969   1.03   1.03  -0.969  0.0312  2.03  -0.969   0.0312 -0.969 
    -#> 10 -0.219  -0.219  -0.219 -0.219 -0.219 -0.219   1.78  -0.219  -0.219  -0.219 
    -#> # ℹ 22 more variables: IIP11 <dbl>, IIP12 <dbl>, IIP13 <dbl>, IIP14 <dbl>,
    -#> #   IIP15 <dbl>, IIP16 <dbl>, IIP17 <dbl>, IIP18 <dbl>, IIP19 <dbl>,
    -#> #   IIP20 <dbl>, IIP21 <dbl>, IIP22 <dbl>, IIP23 <dbl>, IIP24 <dbl>,
    -#> #   IIP25 <dbl>, IIP26 <dbl>, IIP27 <dbl>, IIP28 <dbl>, IIP29 <dbl>,
    -#> #   IIP30 <dbl>, IIP31 <dbl>, IIP32 <dbl>
    -

    Above, we told the function to overwrite the original item variables -with their ipsatized versions; new ipsatized variables (ending in -_i) could have been appended to the data frame instead by -changing the overwrite argument to FALSE. We -also told it to calculate the mean for each observation after ignoring -any missing values. We could have changed this by setting the -na.rm argument to FALSE.

    +#> IIP01_i IIP02_i IIP03_i IIP04_i IIP05_i IIP06_i IIP07_i +#> 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 0.0000000 -1.0000000 0.0000000 +#> 2 0.0625000 0.0625000 -0.9375000 -0.9375000 2.0625000 1.0625000 1.0625000 +#> 3 -0.4062500 -1.4062500 -0.4062500 -1.4062500 -0.4062500 -0.4062500 -0.4062500 +#> 4 0.7096774 -0.2903226 0.7096774 NA -0.2903226 0.7096774 -0.2903226 +#> 5 -0.6250000 -0.6250000 -0.6250000 0.3750000 -0.6250000 -0.6250000 0.3750000 +#> 6 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 0.7187500 +#> 7 0.5000000 -0.5000000 -0.5000000 -0.5000000 1.5000000 0.5000000 0.5000000 +#> 8 0.2580645 -0.7419355 0.2580645 -0.7419355 0.2580645 0.2580645 1.2580645 +#> 9 -0.9687500 -0.9687500 1.0312500 1.0312500 -0.9687500 0.0312500 2.0312500 +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 1.7812500 +#> IIP08_i IIP09_i IIP10_i IIP11_i IIP12_i IIP13_i IIP14_i +#> 1 -1.0000000 1.0000000 0.0000000 -1.0000000 -1.0000000 -1.0000000 0.000000 +#> 2 0.0625000 -0.9375000 0.0625000 -0.9375000 0.0625000 3.0625000 2.062500 +#> 3 1.5937500 -1.4062500 -0.4062500 -1.4062500 -1.4062500 0.5937500 1.593750 +#> 4 0.7096774 -0.2903226 0.7096774 -0.2903226 1.7096774 -0.2903226 -1.290323 +#> 5 0.3750000 -0.6250000 0.3750000 -0.6250000 1.3750000 0.3750000 0.375000 +#> 6 0.7187500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.281250 +#> 7 -0.5000000 0.5000000 -0.5000000 -0.5000000 -0.5000000 0.5000000 0.500000 +#> 8 0.2580645 0.2580645 -0.7419355 -0.7419355 -0.7419355 0.2580645 NA +#> 9 -0.9687500 0.0312500 -0.9687500 0.0312500 0.0312500 -0.9687500 1.031250 +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 1.781250 +#> IIP15_i IIP16_i IIP17_i IIP18_i IIP19_i IIP20_i IIP21_i +#> 1 3.0000000 2.0000000 1.0000000 3.0000000 1.0000000 -1.0000000 0.0000000 +#> 2 2.0625000 0.0625000 -0.9375000 -0.9375000 0.0625000 -0.9375000 0.0625000 +#> 3 1.5937500 0.5937500 0.5937500 -0.4062500 -0.4062500 -1.4062500 1.5937500 +#> 4 -0.2903226 0.7096774 -1.2903226 -0.2903226 -0.2903226 -1.2903226 0.7096774 +#> 5 2.3750000 0.3750000 -0.6250000 0.3750000 -0.6250000 0.3750000 0.3750000 +#> 6 1.7187500 0.7187500 0.7187500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 +#> 7 0.5000000 -0.5000000 0.5000000 -0.5000000 -0.5000000 -0.5000000 -0.5000000 +#> 8 1.2580645 0.2580645 0.2580645 -0.7419355 0.2580645 -0.7419355 -0.7419355 +#> 9 1.0312500 1.0312500 0.0312500 1.0312500 1.0312500 -0.9687500 -0.9687500 +#> 10 1.7812500 0.7812500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 +#> IIP22_i IIP23_i IIP24_i IIP25_i IIP26_i IIP27_i IIP28_i +#> 1 -1.0000000 -1.0000000 -1.0000000 2.0000000 2.0000000 2.0000000 -1.0000000 +#> 2 1.0625000 -0.9375000 -0.9375000 -0.9375000 -0.9375000 -0.9375000 0.0625000 +#> 3 0.5937500 1.5937500 -0.4062500 -0.4062500 -0.4062500 -0.4062500 -1.4062500 +#> 4 -0.2903226 0.7096774 -0.2903226 -1.2903226 -0.2903226 0.7096774 -0.2903226 +#> 5 -0.6250000 0.3750000 0.3750000 1.3750000 0.3750000 -0.6250000 -0.6250000 +#> 6 -0.2812500 0.7187500 0.7187500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 +#> 7 0.5000000 0.5000000 0.5000000 0.5000000 -0.5000000 -0.5000000 -0.5000000 +#> 8 -0.7419355 0.2580645 0.2580645 0.2580645 0.2580645 0.2580645 -0.7419355 +#> 9 -0.9687500 2.0312500 -0.9687500 0.0312500 -0.9687500 0.0312500 -0.9687500 +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 +#> IIP29_i IIP30_i IIP31_i IIP32_i +#> 1 -1.0000000 -1.0000000 0.0000000 -1.0000000 +#> 2 -0.9375000 -0.9375000 -0.9375000 1.0625000 +#> 3 1.5937500 0.5937500 1.5937500 0.5937500 +#> 4 0.7096774 -0.2903226 0.7096774 -0.2903226 +#> 5 -0.6250000 -0.6250000 -0.6250000 -0.6250000 +#> 6 -0.2812500 -0.2812500 -0.2812500 0.7187500 +#> 7 0.5000000 0.5000000 -0.5000000 -0.5000000 +#> 8 -0.7419355 0.2580645 1.2580645 0.2580645 +#> 9 -0.9687500 0.0312500 2.0312500 -0.9687500 +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500

    +

    Above, we told the function to take all the variables from column 1 +to column 32 and calculate new ipsatized variables ending in +_i (although different prefixes and +suffixes can be used). By default, the mean for each +observation/row is calculated after ignoring any missing values, but we +could have changed this by adding na.rm = FALSE. By setting +append to FALSE, only the ipsatized versions are returned, +but if we changed this to TRUE then the original variables would have +been included as well.

    We can check that the ipsatization was successful by calculating the mean of each row (i.e., each participant’s mean response) in the original and ipsatized data frames. We do this below using the @@ -498,50 +552,31 @@

    Scoring item-level dataitems() function, especially if you shuffle your items.

    -scale_scores <- score(.data = raw_iipsc, items = IIP01:IIP32, instrument = iipsc)
    +scale_scores <- score(
    +  data = raw_iipsc, 
    +  items = 1:32, 
    +  instrument = iipsc, 
    +  append = FALSE
    +)
     print(scale_scores)
    -#> # A tibble: 10 × 40
    -#>    IIP01 IIP02 IIP03 IIP04 IIP05 IIP06 IIP07 IIP08 IIP09 IIP10 IIP11 IIP12 IIP13
    -#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    -#>  1     0     0     0     0     1     0     1     0     2     1     0     0     0
    -#>  2     1     1     0     0     3     2     2     1     0     1     0     1     4
    -#>  3     1     0     1     0     1     1     1     3     0     1     0     0     2
    -#>  4     3     2     3    NA     2     3     2     3     2     3     2     4     2
    -#>  5     0     0     0     1     0     0     1     1     0     1     0     2     1
    -#>  6     0     0     0     0     0     0     1     1     0     0     0     0     0
    -#>  7     1     0     0     0     2     1     1     0     1     0     0     0     1
    -#>  8     1     0     1     0     1     1     2     1     1     0     0     0     1
    -#>  9     0     0     2     2     0     1     3     0     1     0     1     1     0
    -#> 10     0     0     0     0     0     0     2     0     0     0     0     0     0
    -#> # ℹ 27 more variables: IIP14 <dbl>, IIP15 <dbl>, IIP16 <dbl>, IIP17 <dbl>,
    -#> #   IIP18 <dbl>, IIP19 <dbl>, IIP20 <dbl>, IIP21 <dbl>, IIP22 <dbl>,
    -#> #   IIP23 <dbl>, IIP24 <dbl>, IIP25 <dbl>, IIP26 <dbl>, IIP27 <dbl>,
    -#> #   IIP28 <dbl>, IIP29 <dbl>, IIP30 <dbl>, IIP31 <dbl>, IIP32 <dbl>, PA <dbl>,
    -#> #   BC <dbl>, DE <dbl>, FG <dbl>, HI <dbl>, JK <dbl>, LM <dbl>, NO <dbl>
    -

    The scale_scores data frame contains all the item-level -data from item_level_csie, as well as the newly calculated -scale scores. Note that the scale score variables were named using the -scale abbreviations shown by the scales() function (i.e., -two-letter abbreviations from PA to NO). You -can customize the naming of these variables by using the -prefix and suffix arguments from the -score() function. We can select and view just the scale -score variables using the select() function.

    -
    -select(scale_scores, PA:NO)
    -#> # A tibble: 10 × 8
    -#>       PA    BC    DE    FG    HI    JK    LM    NO
    -#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    -#>  1  1.75  2     1.25  0     0.5  0.25   1.5   0.75
    -#>  2  0.25  0.5   0.25  0.5   2    1.75   1.25  1   
    -#>  3  1     0.75  0.75  0     2.25 2      2.5   2   
    -#>  4  1.75  2.25  2.5   2.33  2.5  2      2.5   2.5 
    -#>  5  0.5   0.75  0     1     0.5  0.25   1.25  0.75
    -#>  6  0.25  0     0     0     0    0      1     1   
    -#>  7  1     0     0     0     1    1      0.75  0.25
    -#>  8  1     0.25  0.75  0     0.5  0.667  1.75  1   
    -#>  9  0.75  0.5   1.5   0.75  0    1      2.75  0.5 
    -#> 10  0     0     0     0     0    0.5    1     0.25
    +#> PA BC DE FG HI JK LM NO +#> 1 1.75 2.00 1.25 0.000000 0.50 0.2500000 1.50 0.75 +#> 2 0.25 0.50 0.25 0.500000 2.00 1.7500000 1.25 1.00 +#> 3 1.00 0.75 0.75 0.000000 2.25 2.0000000 2.50 2.00 +#> 4 1.75 2.25 2.50 2.333333 2.50 2.0000000 2.50 2.50 +#> 5 0.50 0.75 0.00 1.000000 0.50 0.2500000 1.25 0.75 +#> 6 0.25 0.00 0.00 0.000000 0.00 0.0000000 1.00 1.00 +#> 7 1.00 0.00 0.00 0.000000 1.00 1.0000000 0.75 0.25 +#> 8 1.00 0.25 0.75 0.000000 0.50 0.6666667 1.75 1.00 +#> 9 0.75 0.50 1.50 0.750000 0.00 1.0000000 2.75 0.50 +#> 10 0.00 0.00 0.00 0.000000 0.00 0.5000000 1.00 0.25

    +

    Because we set append to FALSE, the +scale_scores data frame contains only the scale score +variables. These were named using the scale abbreviations shown by the +scales() function (i.e., two-letter abbreviations from PA +to NO). You can customize the naming of these variables by using the +prefix and suffix arguments (e.g., to make +them IIP_PA to IIP_NO).

    Note that the na.rm argument for the score() function defaulted to TRUE, which means that missing values were ignored in calculating the scale scores. @@ -555,12 +590,12 @@

    Standardizing scale-level dataStandardizing scale-level data
    +
     norms(iipsc)
     #> The IIP-SC currently has 2 normative data set(s):
     #> 1. 872 American college students
    @@ -588,63 +623,49 @@ 

    Standardizing scale-level datastandardize() function and give it the -scale_scores object we saved the item-level and scale-level -data within. We will save the output of this function to a data frame -named z_scales to reflect the idea that standardized scores -are often called “z-scores.”

    -
    -z_scales <- standardize(
    -  .data = scale_scores, 
    -  scales = PA:NO, 
    -  angles = octants(),
    +we can call the norm_standardize() function and give it the
    +scale_scores object we created above. We will save the
    +output of this function to a data frame named z_scales to
    +reflect the idea that standardized scores are often called
    +“z-scores.”

    +
    +z_scales <- norm_standardize(
    +  data = scale_scores, 
    +  scales = 1:8, 
       instrument = iipsc,
    -  sample = 1
    +  sample = 1,
    +  append = FALSE
     )
     print(z_scales)
    -#> # A tibble: 10 × 48
    -#>    IIP01 IIP02 IIP03 IIP04 IIP05 IIP06 IIP07 IIP08 IIP09 IIP10 IIP11 IIP12 IIP13
    -#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    -#>  1     0     0     0     0     1     0     1     0     2     1     0     0     0
    -#>  2     1     1     0     0     3     2     2     1     0     1     0     1     4
    -#>  3     1     0     1     0     1     1     1     3     0     1     0     0     2
    -#>  4     3     2     3    NA     2     3     2     3     2     3     2     4     2
    -#>  5     0     0     0     1     0     0     1     1     0     1     0     2     1
    -#>  6     0     0     0     0     0     0     1     1     0     0     0     0     0
    -#>  7     1     0     0     0     2     1     1     0     1     0     0     0     1
    -#>  8     1     0     1     0     1     1     2     1     1     0     0     0     1
    -#>  9     0     0     2     2     0     1     3     0     1     0     1     1     0
    -#> 10     0     0     0     0     0     0     2     0     0     0     0     0     0
    -#> # ℹ 35 more variables: IIP14 <dbl>, IIP15 <dbl>, IIP16 <dbl>, IIP17 <dbl>,
    -#> #   IIP18 <dbl>, IIP19 <dbl>, IIP20 <dbl>, IIP21 <dbl>, IIP22 <dbl>,
    -#> #   IIP23 <dbl>, IIP24 <dbl>, IIP25 <dbl>, IIP26 <dbl>, IIP27 <dbl>,
    -#> #   IIP28 <dbl>, IIP29 <dbl>, IIP30 <dbl>, IIP31 <dbl>, IIP32 <dbl>, PA <dbl>,
    -#> #   BC <dbl>, DE <dbl>, FG <dbl>, HI <dbl>, JK <dbl>, LM <dbl>, NO <dbl>,
    -#> #   PA_z <dbl>, BC_z <dbl>, DE_z <dbl>, FG_z <dbl>, HI_z <dbl>, JK_z <dbl>,
    -#> #   LM_z <dbl>, NO_z <dbl>
    -

    Again, the output contains all the item-level, scale-level, and now -the standardized scale-level variables. The new variables are named the -same as the scale score variables except with a configurable -prefix and suffix (by default, they are given -only a suffix of _z). We can again use the -select() function to view just the newly created -standardized variables. These variables are the ones we are most likely -to use in subsequent analyses (e.g., the structural summary method).

    -
    -select(z_scales, PA_z:NO_z)
    -#> # A tibble: 10 × 8
    -#>       PA_z    BC_z   DE_z    FG_z   HI_z   JK_z    LM_z    NO_z
    -#>      <dbl>   <dbl>  <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>
    -#>  1  1.5     1.75    0.409 -1.11   -1.01  -1.33   0.0424 -0.344 
    -#>  2 -0.773  -0.424  -0.760 -0.578   0.634  0.428 -0.261  -0.0312
    -#>  3  0.364  -0.0616 -0.175 -1.11    0.907  0.721  1.25    1.22  
    -#>  4  1.5     2.11    1.87   1.36    1.18   0.721  1.25    1.84  
    -#>  5 -0.394  -0.0616 -1.05  -0.0501 -1.01  -1.33  -0.261  -0.344 
    -#>  6 -0.773  -1.15   -1.05  -1.11   -1.55  -1.62  -0.564  -0.0312
    -#>  7  0.364  -1.15   -1.05  -1.11   -0.459 -0.452 -0.867  -0.969 
    -#>  8  0.364  -0.786  -0.175 -1.11   -1.01  -0.843  0.345  -0.0312
    -#>  9 -0.0152 -0.424   0.702 -0.314  -1.55  -0.452  1.56   -0.656 
    -#> 10 -1.15   -1.15   -1.05  -1.11   -1.55  -1.04  -0.564  -0.969
    +#> PA_z BC_z DE_z FG_z HI_z JK_z +#> 1 1.50000000 1.7500000 0.4093567 -1.10554090 -1.0054645 -1.3313783 +#> 2 -0.77272727 -0.4239130 -0.7602339 -0.57783641 0.6338798 0.4281525 +#> 3 0.36363636 -0.0615942 -0.1754386 -1.10554090 0.9071038 0.7214076 +#> 4 1.50000000 2.1123188 1.8713450 1.35708004 1.1803279 0.7214076 +#> 5 -0.39393939 -0.0615942 -1.0526316 -0.05013193 -1.0054645 -1.3313783 +#> 6 -0.77272727 -1.1485507 -1.0526316 -1.10554090 -1.5519126 -1.6246334 +#> 7 0.36363636 -1.1485507 -1.0526316 -1.10554090 -0.4590164 -0.4516129 +#> 8 0.36363636 -0.7862319 -0.1754386 -1.10554090 -1.0054645 -0.8426197 +#> 9 -0.01515152 -0.4239130 0.7017544 -0.31398417 -1.5519126 -0.4516129 +#> 10 -1.15151515 -1.1485507 -1.0526316 -1.10554090 -1.5519126 -1.0381232 +#> LM_z NO_z +#> 1 0.04242424 -0.34375 +#> 2 -0.26060606 -0.03125 +#> 3 1.25454545 1.21875 +#> 4 1.25454545 1.84375 +#> 5 -0.26060606 -0.34375 +#> 6 -0.56363636 -0.03125 +#> 7 -0.86666667 -0.96875 +#> 8 0.34545455 -0.03125 +#> 9 1.55757576 -0.65625 +#> 10 -0.56363636 -0.96875
    +

    Again, because we set append to FALSE, the output +contains only the norm standardized scale-level variables. The new +variables are named the same as the scale score variables except with a +configurable prefix and suffix (by default, +they are given only a suffix of _z). These variables are +the ones we are most likely to use in subsequent analyses (e.g., the +structural summary method).

    diff --git a/docs/dev/index.html b/docs/dev/index.html index 7a960f8c..14d9fb42 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -100,21 +100,19 @@

    Installation

    Usage

    +
    +

    Example 1 +

     data("jz2017")
     results <- ssm_analyze(
    -  .data = jz2017, 
    -  scales = c(PA, BC, DE, FG, HI, JK, LM, NO), 
    +  data = jz2017, 
    +  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), 
       angles = c(90, 135, 180, 225, 270, 315, 360, 45), 
    -  measures = c(NARPD, ASPD),
    +  measures = c("NARPD", "ASPD"),
       measures_labels = c("Narcissistic PD", "Antisocial PD")
     )
     summary(results)
    -#> Call:
    -#> ssm_analyze(.data = jz2017, scales = c(PA, BC, DE, FG, HI, JK, 
    -#>     LM, NO), angles = c(90, 135, 180, 225, 270, 315, 360, 45), 
    -#>     measures = c(NARPD, ASPD), measures_labels = c("Narcissistic PD", 
    -#>         "Antisocial PD"))
     #> 
     #> Statistical Basis:    Correlation Scores 
     #> Bootstrap Resamples:  2000 
    @@ -122,7 +120,9 @@ 

    Usage #> Listwise Deletion: TRUE #> Scale Displacements: 90 135 180 225 270 315 360 45 #> -#> Profile [Narcissistic PD]: +#> +#> # Profile [Narcissistic PD]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.202 0.169 0.238 #> X-Value -0.062 -0.094 -0.029 @@ -131,7 +131,9 @@

    Usage #> Displacement 108.967 98.633 118.537 #> Model Fit 0.957 #> -#> Profile [Antisocial PD]: +#> +#> # Profile [Antisocial PD]: +#> #> Estimate Lower CI Upper CI #> Elevation 0.124 0.087 0.158 #> X-Value -0.099 -0.133 -0.064 @@ -140,8 +142,8 @@

    Usage #> Displacement 115.927 107.327 124.188 #> Model Fit 0.964

    -ssm_table(results, xy = FALSE)
    - +ssm_table(results, drop_xy = TRUE) +
    @@ -203,6 +205,139 @@

    Usage ssm_plot(results)

    +
    +

    Example 2 +

    +
    +results2 <- ssm_analyze(
    +  data = jz2017, 
    +  scales = PANO(), 
    +  angles = octants(), 
    +  grouping = "Gender",
    +  contrast = TRUE
    +)
    +summary(results2)
    +#> 
    +#> Statistical Basis:    Mean Scores 
    +#> Bootstrap Resamples:  2000 
    +#> Confidence Level:     0.95 
    +#> Listwise Deletion:    TRUE 
    +#> Scale Displacements:  90 135 180 225 270 315 360 45 
    +#> 
    +#> 
    +#> # Profile [Female]:
    +#> 
    +#>                Estimate   Lower CI   Upper CI
    +#> Elevation         0.946      0.909      0.984
    +#> X-Value           0.459      0.421      0.498
    +#> Y-Value          -0.310     -0.355     -0.267
    +#> Amplitude         0.554      0.511      0.598
    +#> Displacement    325.963    322.182    330.063
    +#> Model Fit         0.889                      
    +#> 
    +#> 
    +#> # Profile [Male]:
    +#> 
    +#>                Estimate   Lower CI   Upper CI
    +#> Elevation         0.884      0.843      0.923
    +#> X-Value           0.227      0.192      0.262
    +#> Y-Value          -0.186     -0.225     -0.148
    +#> Amplitude         0.294      0.257      0.329
    +#> Displacement    320.685    313.462    327.843
    +#> Model Fit         0.824                      
    +#> 
    +#> 
    +#> # Contrast [Male - Female]:
    +#> 
    +#>                  Estimate   Lower CI   Upper CI
    +#> Δ Elevation        -0.062     -0.118     -0.005
    +#> Δ X-Value          -0.232     -0.283     -0.181
    +#> Δ Y-Value           0.124      0.067      0.183
    +#> Δ Amplitude        -0.261     -0.318     -0.202
    +#> Δ Displacement     -5.278    -13.353      2.755
    +#> Δ Model Fit        -0.066
    +
    +ssm_table(results2, drop_xy = TRUE)
    +

    Correlation-based Structural Summary Statistics with 95% CIs
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +Correlation-based Structural Summary Statistics with 95% CIs +
    +Contrast + +Elevation + +Amplitude + +Displacement + +Fit +
    +Female + +0.95 (0.91, 0.98) + +0.55 (0.51, 0.60) + +326.0 (322.2, 330.1) + +0.889 +
    +Male + +0.88 (0.84, 0.92) + +0.29 (0.26, 0.33) + +320.7 (313.5, 327.8) + +0.824 +
    +Male - Female + +-0.06 (-0.12, -0.01) + +-0.26 (-0.32, -0.20) + +-5.3 (-13.4, 2.8) + +-0.066 +
    +
    +ssm_plot(results2)
    +

    +
    +

    Code of Conduct

    diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index 420872c2..6e3a74c8 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -55,7 +55,10 @@

    circumplex (development version)

    -
    +
    • Re-factor package to remove most external dependencies

    • +
    • Breaking change: removed support for non-standard evaluation

    • +
    • Breaking change: replaced model-based contrasts with test-based contrasts

    • +

    circumplex 0.3.10

    CRAN release: 2023-08-22

    diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 90a210c8..dc6128eb 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -5,7 +5,7 @@ articles: intermediate-ssm-analysis: intermediate-ssm-analysis.html introduction-to-ssm-analysis: introduction-to-ssm-analysis.html using-instruments: using-instruments.html -last_built: 2024-10-21T18:25Z +last_built: 2024-10-25T04:02Z urls: reference: http://circumplex.jmgirard.com/reference article: http://circumplex.jmgirard.com/articles diff --git a/docs/dev/reference/anchors.html b/docs/dev/reference/anchors.html index 0682bd3b..f804627f 100644 --- a/docs/dev/reference/anchors.html +++ b/docs/dev/reference/anchors.html @@ -94,6 +94,7 @@

    See also

    Examples

    instrument(csip)
    +#> Error in instrument(csip): is.character(code) is not TRUE
     anchors(csip)
     #> The CSIP is rated using the following 4-point scale.
     #> 0. Not a problem
    diff --git a/docs/dev/reference/html_render.html b/docs/dev/reference/html_render.html
    index 5f04c348..25d4c1ff 100644
    --- a/docs/dev/reference/html_render.html
    +++ b/docs/dev/reference/html_render.html
    @@ -90,7 +90,6 @@ 

    Value

    See also

    Other table functions: -ssm_append(), ssm_table()

    diff --git a/docs/dev/reference/iis64.html b/docs/dev/reference/iis64.html index dd84e011..362cc8f5 100644 --- a/docs/dev/reference/iis64.html +++ b/docs/dev/reference/iis64.html @@ -144,7 +144,7 @@

    Examples#> 39. I can take care of myself, even when others' needs feel pressing #> 40. I can take charge in a group #> 41. I work really well as an assistant -#> 42. When friends as for favors, I'm delighted to help them out +#> 42. When friends ask for favors, I'm delighted to help them out #> 43. I feel enriched by helping others #> 44. I'm excited about meeting new people #> 45. I recognize when others need privacy @@ -225,7 +225,7 @@

    Examples#> 27. I stick by my friends when they're in trouble #> 33. It makes me happy when others are happy #> 35. My feelings of gratitude warm my relationships with others -#> 42. When friends as for favors, I'm delighted to help them out +#> 42. When friends ask for favors, I'm delighted to help them out #> 43. I feel enriched by helping others #> 52. I enjoy complimenting others #> LM: Connect (360 degrees) diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index 1568d16a..cfdb7a63 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -124,7 +124,7 @@

    Tidying Functionsstandardize() + norm_standardize()
    Standardize circumplex scales using normative data
    @@ -182,12 +182,6 @@

    Secondary SSM Functions
    - ssm_append() - -
    -
    Combine SSM tables
    -
    - ssm_plot_circle()
    @@ -239,6 +233,12 @@

    Convenience Functions
    Angular displacements for quadrant circumplex scales
    +

    + + PANO() + +
    +
    Two-letter abbreviations for octant circumplex scales

    Example Data

    diff --git a/docs/dev/reference/instrument.html b/docs/dev/reference/instrument.html index 23966073..90d55f92 100644 --- a/docs/dev/reference/instrument.html +++ b/docs/dev/reference/instrument.html @@ -104,9 +104,8 @@

    See also

    Examples

    -
    instrument(iip32)
    -instrument("iip32")
    -x <- instrument(iip32)
    +    
    instrument("iip32")
    +x <- instrument("iip32")
     

    Examples

    data("raw_iipsc")
    -ipsatize(raw_iipsc, IIP01:IIP32)
    -#> # A tibble: 10 × 64
    -#>    IIP01 IIP02 IIP03 IIP04 IIP05 IIP06 IIP07 IIP08 IIP09 IIP10 IIP11 IIP12 IIP13
    -#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    -#>  1     0     0     0     0     1     0     1     0     2     1     0     0     0
    -#>  2     1     1     0     0     3     2     2     1     0     1     0     1     4
    -#>  3     1     0     1     0     1     1     1     3     0     1     0     0     2
    -#>  4     3     2     3    NA     2     3     2     3     2     3     2     4     2
    -#>  5     0     0     0     1     0     0     1     1     0     1     0     2     1
    -#>  6     0     0     0     0     0     0     1     1     0     0     0     0     0
    -#>  7     1     0     0     0     2     1     1     0     1     0     0     0     1
    -#>  8     1     0     1     0     1     1     2     1     1     0     0     0     1
    -#>  9     0     0     2     2     0     1     3     0     1     0     1     1     0
    -#> 10     0     0     0     0     0     0     2     0     0     0     0     0     0
    -#> # ℹ 51 more variables: IIP14 <dbl>, IIP15 <dbl>, IIP16 <dbl>, IIP17 <dbl>,
    -#> #   IIP18 <dbl>, IIP19 <dbl>, IIP20 <dbl>, IIP21 <dbl>, IIP22 <dbl>,
    -#> #   IIP23 <dbl>, IIP24 <dbl>, IIP25 <dbl>, IIP26 <dbl>, IIP27 <dbl>,
    -#> #   IIP28 <dbl>, IIP29 <dbl>, IIP30 <dbl>, IIP31 <dbl>, IIP32 <dbl>,
    -#> #   IIP01_i <dbl>, IIP02_i <dbl>, IIP03_i <dbl>, IIP04_i <dbl>, IIP05_i <dbl>,
    -#> #   IIP06_i <dbl>, IIP07_i <dbl>, IIP08_i <dbl>, IIP09_i <dbl>, IIP10_i <dbl>,
    -#> #   IIP11_i <dbl>, IIP12_i <dbl>, IIP13_i <dbl>, IIP14_i <dbl>, …
    +ipsatize(raw_iipsc, items = 1:32)
    +#>    IIP01 IIP02 IIP03 IIP04 IIP05 IIP06 IIP07 IIP08 IIP09 IIP10 IIP11 IIP12
    +#> 1      0     0     0     0     1     0     1     0     2     1     0     0
    +#> 2      1     1     0     0     3     2     2     1     0     1     0     1
    +#> 3      1     0     1     0     1     1     1     3     0     1     0     0
    +#> 4      3     2     3    NA     2     3     2     3     2     3     2     4
    +#> 5      0     0     0     1     0     0     1     1     0     1     0     2
    +#> 6      0     0     0     0     0     0     1     1     0     0     0     0
    +#> 7      1     0     0     0     2     1     1     0     1     0     0     0
    +#> 8      1     0     1     0     1     1     2     1     1     0     0     0
    +#> 9      0     0     2     2     0     1     3     0     1     0     1     1
    +#> 10     0     0     0     0     0     0     2     0     0     0     0     0
    +#>    IIP13 IIP14 IIP15 IIP16 IIP17 IIP18 IIP19 IIP20 IIP21 IIP22 IIP23 IIP24
    +#> 1      0     1     4     3     2     4     2     0     1     0     0     0
    +#> 2      4     3     3     1     0     0     1     0     1     2     0     0
    +#> 3      2     3     3     2     2     1     1     0     3     2     3     1
    +#> 4      2     1     2     3     1     2     2     1     3     2     3     2
    +#> 5      1     1     3     1     0     1     0     1     1     0     1     1
    +#> 6      0     0     2     1     1     0     0     0     0     0     1     1
    +#> 7      1     1     1     0     1     0     0     0     0     1     1     1
    +#> 8      1    NA     2     1     1     0     1     0     0     0     1     1
    +#> 9      0     2     2     2     1     2     2     0     0     0     3     0
    +#> 10     0     2     2     1     0     0     0     0     0     0     0     0
    +#>    IIP25 IIP26 IIP27 IIP28 IIP29 IIP30 IIP31 IIP32    IIP01_i    IIP02_i
    +#> 1      3     3     3     0     0     0     1     0 -1.0000000 -1.0000000
    +#> 2      0     0     0     1     0     0     0     2  0.0625000  0.0625000
    +#> 3      1     1     1     0     3     2     3     2 -0.4062500 -1.4062500
    +#> 4      1     2     3     2     3     2     3     2  0.7096774 -0.2903226
    +#> 5      2     1     0     0     0     0     0     0 -0.6250000 -0.6250000
    +#> 6      0     0     0     0     0     0     0     1 -0.2812500 -0.2812500
    +#> 7      1     0     0     0     1     1     0     0  0.5000000 -0.5000000
    +#> 8      1     1     1     0     0     1     2     1  0.2580645 -0.7419355
    +#> 9      1     0     1     0     0     1     3     0 -0.9687500 -0.9687500
    +#> 10     0     0     0     0     0     0     0     0 -0.2187500 -0.2187500
    +#>       IIP03_i    IIP04_i    IIP05_i    IIP06_i    IIP07_i    IIP08_i    IIP09_i
    +#> 1  -1.0000000 -1.0000000  0.0000000 -1.0000000  0.0000000 -1.0000000  1.0000000
    +#> 2  -0.9375000 -0.9375000  2.0625000  1.0625000  1.0625000  0.0625000 -0.9375000
    +#> 3  -0.4062500 -1.4062500 -0.4062500 -0.4062500 -0.4062500  1.5937500 -1.4062500
    +#> 4   0.7096774         NA -0.2903226  0.7096774 -0.2903226  0.7096774 -0.2903226
    +#> 5  -0.6250000  0.3750000 -0.6250000 -0.6250000  0.3750000  0.3750000 -0.6250000
    +#> 6  -0.2812500 -0.2812500 -0.2812500 -0.2812500  0.7187500  0.7187500 -0.2812500
    +#> 7  -0.5000000 -0.5000000  1.5000000  0.5000000  0.5000000 -0.5000000  0.5000000
    +#> 8   0.2580645 -0.7419355  0.2580645  0.2580645  1.2580645  0.2580645  0.2580645
    +#> 9   1.0312500  1.0312500 -0.9687500  0.0312500  2.0312500 -0.9687500  0.0312500
    +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500  1.7812500 -0.2187500 -0.2187500
    +#>       IIP10_i    IIP11_i    IIP12_i    IIP13_i   IIP14_i    IIP15_i    IIP16_i
    +#> 1   0.0000000 -1.0000000 -1.0000000 -1.0000000  0.000000  3.0000000  2.0000000
    +#> 2   0.0625000 -0.9375000  0.0625000  3.0625000  2.062500  2.0625000  0.0625000
    +#> 3  -0.4062500 -1.4062500 -1.4062500  0.5937500  1.593750  1.5937500  0.5937500
    +#> 4   0.7096774 -0.2903226  1.7096774 -0.2903226 -1.290323 -0.2903226  0.7096774
    +#> 5   0.3750000 -0.6250000  1.3750000  0.3750000  0.375000  2.3750000  0.3750000
    +#> 6  -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.281250  1.7187500  0.7187500
    +#> 7  -0.5000000 -0.5000000 -0.5000000  0.5000000  0.500000  0.5000000 -0.5000000
    +#> 8  -0.7419355 -0.7419355 -0.7419355  0.2580645        NA  1.2580645  0.2580645
    +#> 9  -0.9687500  0.0312500  0.0312500 -0.9687500  1.031250  1.0312500  1.0312500
    +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500  1.781250  1.7812500  0.7812500
    +#>       IIP17_i    IIP18_i    IIP19_i    IIP20_i    IIP21_i    IIP22_i    IIP23_i
    +#> 1   1.0000000  3.0000000  1.0000000 -1.0000000  0.0000000 -1.0000000 -1.0000000
    +#> 2  -0.9375000 -0.9375000  0.0625000 -0.9375000  0.0625000  1.0625000 -0.9375000
    +#> 3   0.5937500 -0.4062500 -0.4062500 -1.4062500  1.5937500  0.5937500  1.5937500
    +#> 4  -1.2903226 -0.2903226 -0.2903226 -1.2903226  0.7096774 -0.2903226  0.7096774
    +#> 5  -0.6250000  0.3750000 -0.6250000  0.3750000  0.3750000 -0.6250000  0.3750000
    +#> 6   0.7187500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.2812500  0.7187500
    +#> 7   0.5000000 -0.5000000 -0.5000000 -0.5000000 -0.5000000  0.5000000  0.5000000
    +#> 8   0.2580645 -0.7419355  0.2580645 -0.7419355 -0.7419355 -0.7419355  0.2580645
    +#> 9   0.0312500  1.0312500  1.0312500 -0.9687500 -0.9687500 -0.9687500  2.0312500
    +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500
    +#>       IIP24_i    IIP25_i    IIP26_i    IIP27_i    IIP28_i    IIP29_i    IIP30_i
    +#> 1  -1.0000000  2.0000000  2.0000000  2.0000000 -1.0000000 -1.0000000 -1.0000000
    +#> 2  -0.9375000 -0.9375000 -0.9375000 -0.9375000  0.0625000 -0.9375000 -0.9375000
    +#> 3  -0.4062500 -0.4062500 -0.4062500 -0.4062500 -1.4062500  1.5937500  0.5937500
    +#> 4  -0.2903226 -1.2903226 -0.2903226  0.7096774 -0.2903226  0.7096774 -0.2903226
    +#> 5   0.3750000  1.3750000  0.3750000 -0.6250000 -0.6250000 -0.6250000 -0.6250000
    +#> 6   0.7187500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.2812500
    +#> 7   0.5000000  0.5000000 -0.5000000 -0.5000000 -0.5000000  0.5000000  0.5000000
    +#> 8   0.2580645  0.2580645  0.2580645  0.2580645 -0.7419355 -0.7419355  0.2580645
    +#> 9  -0.9687500  0.0312500 -0.9687500  0.0312500 -0.9687500 -0.9687500  0.0312500
    +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500
    +#>       IIP31_i    IIP32_i
    +#> 1   0.0000000 -1.0000000
    +#> 2  -0.9375000  1.0625000
    +#> 3   1.5937500  0.5937500
    +#> 4   0.7096774 -0.2903226
    +#> 5  -0.6250000 -0.6250000
    +#> 6  -0.2812500  0.7187500
    +#> 7  -0.5000000 -0.5000000
    +#> 8   1.2580645  0.2580645
    +#> 9   2.0312500 -0.9687500
    +#> 10 -0.2187500 -0.2187500
    +ipsatize(raw_iipsc, items = sprintf("IIP%02d", 1:32))
    +#>    IIP01 IIP02 IIP03 IIP04 IIP05 IIP06 IIP07 IIP08 IIP09 IIP10 IIP11 IIP12
    +#> 1      0     0     0     0     1     0     1     0     2     1     0     0
    +#> 2      1     1     0     0     3     2     2     1     0     1     0     1
    +#> 3      1     0     1     0     1     1     1     3     0     1     0     0
    +#> 4      3     2     3    NA     2     3     2     3     2     3     2     4
    +#> 5      0     0     0     1     0     0     1     1     0     1     0     2
    +#> 6      0     0     0     0     0     0     1     1     0     0     0     0
    +#> 7      1     0     0     0     2     1     1     0     1     0     0     0
    +#> 8      1     0     1     0     1     1     2     1     1     0     0     0
    +#> 9      0     0     2     2     0     1     3     0     1     0     1     1
    +#> 10     0     0     0     0     0     0     2     0     0     0     0     0
    +#>    IIP13 IIP14 IIP15 IIP16 IIP17 IIP18 IIP19 IIP20 IIP21 IIP22 IIP23 IIP24
    +#> 1      0     1     4     3     2     4     2     0     1     0     0     0
    +#> 2      4     3     3     1     0     0     1     0     1     2     0     0
    +#> 3      2     3     3     2     2     1     1     0     3     2     3     1
    +#> 4      2     1     2     3     1     2     2     1     3     2     3     2
    +#> 5      1     1     3     1     0     1     0     1     1     0     1     1
    +#> 6      0     0     2     1     1     0     0     0     0     0     1     1
    +#> 7      1     1     1     0     1     0     0     0     0     1     1     1
    +#> 8      1    NA     2     1     1     0     1     0     0     0     1     1
    +#> 9      0     2     2     2     1     2     2     0     0     0     3     0
    +#> 10     0     2     2     1     0     0     0     0     0     0     0     0
    +#>    IIP25 IIP26 IIP27 IIP28 IIP29 IIP30 IIP31 IIP32    IIP01_i    IIP02_i
    +#> 1      3     3     3     0     0     0     1     0 -1.0000000 -1.0000000
    +#> 2      0     0     0     1     0     0     0     2  0.0625000  0.0625000
    +#> 3      1     1     1     0     3     2     3     2 -0.4062500 -1.4062500
    +#> 4      1     2     3     2     3     2     3     2  0.7096774 -0.2903226
    +#> 5      2     1     0     0     0     0     0     0 -0.6250000 -0.6250000
    +#> 6      0     0     0     0     0     0     0     1 -0.2812500 -0.2812500
    +#> 7      1     0     0     0     1     1     0     0  0.5000000 -0.5000000
    +#> 8      1     1     1     0     0     1     2     1  0.2580645 -0.7419355
    +#> 9      1     0     1     0     0     1     3     0 -0.9687500 -0.9687500
    +#> 10     0     0     0     0     0     0     0     0 -0.2187500 -0.2187500
    +#>       IIP03_i    IIP04_i    IIP05_i    IIP06_i    IIP07_i    IIP08_i    IIP09_i
    +#> 1  -1.0000000 -1.0000000  0.0000000 -1.0000000  0.0000000 -1.0000000  1.0000000
    +#> 2  -0.9375000 -0.9375000  2.0625000  1.0625000  1.0625000  0.0625000 -0.9375000
    +#> 3  -0.4062500 -1.4062500 -0.4062500 -0.4062500 -0.4062500  1.5937500 -1.4062500
    +#> 4   0.7096774         NA -0.2903226  0.7096774 -0.2903226  0.7096774 -0.2903226
    +#> 5  -0.6250000  0.3750000 -0.6250000 -0.6250000  0.3750000  0.3750000 -0.6250000
    +#> 6  -0.2812500 -0.2812500 -0.2812500 -0.2812500  0.7187500  0.7187500 -0.2812500
    +#> 7  -0.5000000 -0.5000000  1.5000000  0.5000000  0.5000000 -0.5000000  0.5000000
    +#> 8   0.2580645 -0.7419355  0.2580645  0.2580645  1.2580645  0.2580645  0.2580645
    +#> 9   1.0312500  1.0312500 -0.9687500  0.0312500  2.0312500 -0.9687500  0.0312500
    +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500  1.7812500 -0.2187500 -0.2187500
    +#>       IIP10_i    IIP11_i    IIP12_i    IIP13_i   IIP14_i    IIP15_i    IIP16_i
    +#> 1   0.0000000 -1.0000000 -1.0000000 -1.0000000  0.000000  3.0000000  2.0000000
    +#> 2   0.0625000 -0.9375000  0.0625000  3.0625000  2.062500  2.0625000  0.0625000
    +#> 3  -0.4062500 -1.4062500 -1.4062500  0.5937500  1.593750  1.5937500  0.5937500
    +#> 4   0.7096774 -0.2903226  1.7096774 -0.2903226 -1.290323 -0.2903226  0.7096774
    +#> 5   0.3750000 -0.6250000  1.3750000  0.3750000  0.375000  2.3750000  0.3750000
    +#> 6  -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.281250  1.7187500  0.7187500
    +#> 7  -0.5000000 -0.5000000 -0.5000000  0.5000000  0.500000  0.5000000 -0.5000000
    +#> 8  -0.7419355 -0.7419355 -0.7419355  0.2580645        NA  1.2580645  0.2580645
    +#> 9  -0.9687500  0.0312500  0.0312500 -0.9687500  1.031250  1.0312500  1.0312500
    +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500  1.781250  1.7812500  0.7812500
    +#>       IIP17_i    IIP18_i    IIP19_i    IIP20_i    IIP21_i    IIP22_i    IIP23_i
    +#> 1   1.0000000  3.0000000  1.0000000 -1.0000000  0.0000000 -1.0000000 -1.0000000
    +#> 2  -0.9375000 -0.9375000  0.0625000 -0.9375000  0.0625000  1.0625000 -0.9375000
    +#> 3   0.5937500 -0.4062500 -0.4062500 -1.4062500  1.5937500  0.5937500  1.5937500
    +#> 4  -1.2903226 -0.2903226 -0.2903226 -1.2903226  0.7096774 -0.2903226  0.7096774
    +#> 5  -0.6250000  0.3750000 -0.6250000  0.3750000  0.3750000 -0.6250000  0.3750000
    +#> 6   0.7187500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.2812500  0.7187500
    +#> 7   0.5000000 -0.5000000 -0.5000000 -0.5000000 -0.5000000  0.5000000  0.5000000
    +#> 8   0.2580645 -0.7419355  0.2580645 -0.7419355 -0.7419355 -0.7419355  0.2580645
    +#> 9   0.0312500  1.0312500  1.0312500 -0.9687500 -0.9687500 -0.9687500  2.0312500
    +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500
    +#>       IIP24_i    IIP25_i    IIP26_i    IIP27_i    IIP28_i    IIP29_i    IIP30_i
    +#> 1  -1.0000000  2.0000000  2.0000000  2.0000000 -1.0000000 -1.0000000 -1.0000000
    +#> 2  -0.9375000 -0.9375000 -0.9375000 -0.9375000  0.0625000 -0.9375000 -0.9375000
    +#> 3  -0.4062500 -0.4062500 -0.4062500 -0.4062500 -1.4062500  1.5937500  0.5937500
    +#> 4  -0.2903226 -1.2903226 -0.2903226  0.7096774 -0.2903226  0.7096774 -0.2903226
    +#> 5   0.3750000  1.3750000  0.3750000 -0.6250000 -0.6250000 -0.6250000 -0.6250000
    +#> 6   0.7187500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.2812500 -0.2812500
    +#> 7   0.5000000  0.5000000 -0.5000000 -0.5000000 -0.5000000  0.5000000  0.5000000
    +#> 8   0.2580645  0.2580645  0.2580645  0.2580645 -0.7419355 -0.7419355  0.2580645
    +#> 9  -0.9687500  0.0312500 -0.9687500  0.0312500 -0.9687500 -0.9687500  0.0312500
    +#> 10 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500 -0.2187500
    +#>       IIP31_i    IIP32_i
    +#> 1   0.0000000 -1.0000000
    +#> 2  -0.9375000  1.0625000
    +#> 3   1.5937500  0.5937500
    +#> 4   0.7096774 -0.2903226
    +#> 5  -0.6250000 -0.6250000
    +#> 6  -0.2812500  0.7187500
    +#> 7  -0.5000000 -0.5000000
    +#> 8   1.2580645  0.2580645
    +#> 9   2.0312500 -0.9687500
    +#> 10 -0.2187500 -0.2187500
     
    diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml index b943b400..1bac698f 100644 --- a/docs/dev/sitemap.xml +++ b/docs/dev/sitemap.xml @@ -11,12 +11,16 @@ http://circumplex.jmgirard.com/dev/LICENSE.html http://circumplex.jmgirard.com/dev/news/index.html http://circumplex.jmgirard.com/dev/reference/anchors.html +http://circumplex.jmgirard.com/dev/reference/as_label.html +http://circumplex.jmgirard.com/dev/reference/as_name.html http://circumplex.jmgirard.com/dev/reference/aw2009.html http://circumplex.jmgirard.com/dev/reference/circumplex-package.html http://circumplex.jmgirard.com/dev/reference/csie.html http://circumplex.jmgirard.com/dev/reference/csig.html http://circumplex.jmgirard.com/dev/reference/csip.html http://circumplex.jmgirard.com/dev/reference/csiv.html +http://circumplex.jmgirard.com/dev/reference/enquo.html +http://circumplex.jmgirard.com/dev/reference/enquos.html http://circumplex.jmgirard.com/dev/reference/fit_fisher.html http://circumplex.jmgirard.com/dev/reference/fit_gap.html http://circumplex.jmgirard.com/dev/reference/fit_randall.html @@ -42,7 +46,9 @@ http://circumplex.jmgirard.com/dev/reference/jz2017.html http://circumplex.jmgirard.com/dev/reference/mutate.html http://circumplex.jmgirard.com/dev/reference/norms.html +http://circumplex.jmgirard.com/dev/reference/norm_standardize.html http://circumplex.jmgirard.com/dev/reference/octants.html +http://circumplex.jmgirard.com/dev/reference/PANO.html http://circumplex.jmgirard.com/dev/reference/pipe.html http://circumplex.jmgirard.com/dev/reference/poles.html http://circumplex.jmgirard.com/dev/reference/quadrants.html diff --git a/man/figures/README-plot2-1.png b/man/figures/README-plot2-1.png new file mode 100644 index 0000000000000000000000000000000000000000..3da187de018e78e84ca5d91d9a57de7e092b3ce0 GIT binary patch literal 8076 zcmd6Mc|4R~`~R6?khLUhXtQsH79_W#5E)UnP_jkH&I}o8*I2TJ%#4IVC|N3IP}z$q zBD)ZS$ewKTy+_aI^ZZ`F*YkZ|&-eBH=X;y+n){sVT<5yab-mx$IoEv;edZJorzj@? zfXC24?<@dF5&(!n4i>n@n5uFGTM@JDy zqNu2-!X6PdWf?U!HCRX!Xleo%&DdDY*dAC;M?^jtQ7Bzq4bdhI`CbhSMiIk|>+gw-(`q8UB0a0i0{>w||fr5Fr` zw7VS*UfM0!46OYC;C;vZBI>=#P5_8^8S3d;1V5fiUnRDm46GFs9^Su2Q3Ts5*3H&Y z@!&9`%JEPUs^{vi#yUPt-hCd(=Kb#;r4WSZ490Hx+GbGG1)KS4peKOsr6ZV0qN36R}q0|fY$0OvhHa7&Ntf6z?> zsU!S5uN@Knen;8AGNo*#>y-O9mmcr7!$H#VRbH#kxgqn(@%LOwmFzoX>y}fvKFV+w zcBQN~TWa+89??F@)r~TUg2e5cc9~DMsg5eh%+@0zpy|RU>uDDvOcjKJcoH zZ@Y46+BjSlp;Y(8+B>I1-7>GyiR}5|RT6tj&bn7koaT931ky74 zsQQaUpX70SC{A)(|Mt;|kfGU`?zp0}o%Xo@f**yUVwvX=z?V1p;l_D!yC(YQ1N0^d z^>;p*pAzdbUa+J(RRrE@Zpp=M7)ws%ocQ!o=YlU%UCWKAh)mHu@?+>F4+pL;>xpH% z@k0!4=S_2jU5G%DhJ3^ayu(1nxUWEwbm3fPL5R;0$vY>x-F&X8wqj|IYtjRur~__n zZENjzYjYv!%jXR#R}i;y|#4x#a{&PNyuTWcR>z=|->+?5D(6 z5~s+g4&g;;YZf`0x#lgAg!Q6IEl*(cA#$3m^XQyFPI$?FzSO}Bm8ST)6T~MW0%PoZ z`GHabUe}UO#`I324?nN9~ATk6Qe2x zNZTqEA&1_L#akWEHTH9T{fe{^b(t+Ibg&++k6nqJ!HwFsul!CbNKAUIMT!~N@WHM| zxMvBK&zE7)>LM9#5+KaS)|r%sCr0X{ieoO^WW)rr6ycI?hg7*Ej6XS65!M?evbFvI9zL4WHWa4J{E45e4DFTVk=*I5vP3B>~zoWU8Hj=b3djeB}a~oSXuN0SwVPii>?5% zV!uJl!PcOr4eNk=<72Iu*@8)L#v_;6p{3v6pG~ACBL%NzGb|iq2Hs?rxn{CUBWy%Q ziPHEC}bvJbFq`Q=G)w&c8-fWtNqO zw)uDxY6U*`jeGaEp;{-%n1oQjzhhI;xO6Io$tra!* zz1wMP;_0RkBxc(lsF5F+O^CiP;YvIi{$S!Trp?ELaFTO;)Hbj=j~~O*v9!o7>hl9p z;C%7S(lx(}mV*P~ig$!o4CsAYl@bYK;V&*NKRYUWCYigcBy3WftbFHF_Ab^8szxJ=p#7HJR8?EO~3^T*mUXT1fkaQzOH|%NShX~t< zv5Bw7k%g->?3V7hJlz2v9(qNH<;HTQ23x_ww?>RARqp01(&AbyB{Sw?KUlQc!pisC zeD#0e`!rmFgH|V4YL_OoQ63)h^AzQP(gJA%o)EphIP88_CM_WM2cFx;nTMRBD~@*I z?d3db)mgFV-a+sB%}_tETq-&~c6(fSSFLZ@da|U8)}X`asoBy3r!0oU`P&$Spvx6U z+$>McW)VfNt>JGxKYG9Qx*a}tKB#G1>k589?wCXGlZE3iJdfG7-bo#=dm1!RE8H$d zijqsfwg{Dn`&`0Slyue?j#XV*5L09%34Gu#o5ZR5&4lIc>G`fYg2{`Y>>QmEqOs$Z zq89hQteD{fI#+xqGqt7_)F4QmwNF1>Z) zS78VD&C?4vBJ%)Pln3%u~wwC=K%cKsmf8>J5sz%wYQ@mO19;jRKL;xGu{)gJn5{Nh#-CXMZ4@DCiV zG2iNd0Nt6|07a76!-57zTq6!cX)?i_Lx0g=`w1r|zeTK_4xMK;)eS`fx0od$fWzG7 zhU$6gX+N_ij}|D2{@j;-&{PdiTlx$Vtq@yq$6jY|s0rbN9=4?Bq?~laUcnhX!vV;Y z8v1Bw*j&4ndVeD_Ow35g)MW3g^>P9tUAXwJVllFve^y6R1bvRJ5v*(sF;IZk}!22KO8FyeJcZfzvW*R0BFq`_Rt_b>Y+ zqOe%(L_o|;-Z4#Q?RD5a{l|k?$$Z#swoj2~S$-!kkG?QK;Cx{h__+ZielS%_OhJP0 z7#8#x4J7OX-BCJfDA)itQcDIx))15kg}ZQqgvq`gr0CAMSKU1vunT@Q@B^MeO?`Mt z$tqZ}40iGDq}SFLK3nZ6%w@PYn#JC^yU;BoM;8_K50m;-*c-$9zc|C zH57y(WB&x5?EPmUif8XW`)>wp-Er}8(`71iDM86FXXS6Nl^Sy~7%sM;L}?-3V?u}P z&yCbVV=`ZSrYS+$;ZiOjdn0H`G|n@qT>EdfZ$8JgSgqOuBR)H^KtPX*%(6ADoR zY&H=_J4ix_fNYA*ZlaAopByQ4TpYCN!Lag_AQNeks(XX>H#+iG1m+O)vt%5u&9~p@ zL7&|JKsPL(!?Uc>w5Z+Go2p`Ka3!X&1o@ye%c6QK@54uNT5KiD<89-sd_tX>ZjW~C zzja00e#9PVuPk5kojbSerDudPPVcSN)0Xj7!N~_4*cyKpq0$f9%e#3W0&?kLuvpM> zC7YWT=`Rzs&?P&jI|WCfcVfoka|NPp6Nc7zMTX_TfgLm7c%U$@9L@i9DgVc7|8EA^ zv1k%U-dw44?Jb2=B#iOi5Ad)5xG^1xsnoD;PPQLNdmLs_>3QJ{K_$=of%u@WU4_7& zd?*y=F;Gbs__#T_!ISr%jjVReBF@O^~+B_^_5j?Fws?pSfC5mk&<9o zM6@7G>NCddTMj)4>c*lU=qj1RQIjrNOMG4BH~oEf^yJ3yv_Gam{I4!?mX($MlMj)p zKP>8Jju_0P!I+~9(}q8$iIDtQXrt=p>xHPxUJ_AEvrM(=itCn5uRJ2`pY@eq{O|{H zbXV>-=34Q3Jbz=5J}i4(Rl{#UdaHii!5DqcxN}hTQCW?@?*I-{>dLtZec-Q%b9rZ~ zlT>0{dwp=dYjWrM`rk?Cp0onU5IFDt@YmL7x3b4 zNM8J=f;;Iqj6*d%SfK62Ob2lS9z1SS~{rb&WNV$w`A! 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Generalizing the basic analyses ### Generalizing to multiple means -We've already seen how adding the `measures` argument can change `ssm_analyze()` from analyzing means to analyzing correlations. Similarly, we can change it from analyzing all observations as a single group to analyzing subgroups separately. This is done using the `grouping` argument. This argument needs to contain a single variable (name or column number) that specifies each observation's group. For instance, the `Gender` variable in the `jz2017` dataset is a factor with two levels: Female and Male. To analyze each gender separately, we need to add the `grouping = Gender` argument to the function call. +We've already seen how adding the `measures` argument can change `ssm_analyze()` from analyzing means to analyzing correlations. Similarly, we can change it from analyzing all observations as a single group to analyzing subgroups separately. This is done using the `grouping` argument. This argument needs to contain a single variable (name or column number) that specifies each observation's group. For instance, the `Gender` variable in the `jz2017` dataset is a factor with two levels: Female and Male. To analyze each gender separately, we need to add the `grouping = "Gender"` argument to the function call. ```{r group} data("jz2017") -data("iipsc") -jz2017s <- standardize(jz2017, PA:NO, octants(), instrument = iipsc, sample = 1) -results <- ssm_analyze(jz2017s, PA:NO, octants(), grouping = Gender) +results <- ssm_analyze( + data = jz2017, + scales = PANO(), + angles = octants(), + grouping = "Gender" +) summary(results) ``` @@ -41,25 +44,26 @@ ssm_table(results) ```{r group_table, echo = FALSE} ssm_table(results, render = FALSE) %>% - kable(caption = circumplex:::dcaption(results)) %>% - kable_styling(full_width = TRUE, font_size = 12) + knitr::kable(caption = circumplex:::dcaption(results)) %>% + kableExtra::kable_styling(full_width = TRUE, font_size = 12) ``` ```{r group_plot, fig.width = 7.5, fig.height = 4, out.width = "100%"} ssm_plot(results) ``` -Any number of groups can be analyzed in this way, and the output will contain additional profile blocks, the table will contain additional rows, and the figure will contain additional points. The grouping variable just needs to contain more than one level (i.e., unique value). Also notice how, in the plot above, the Male profile has a dashed border instead of the typical solid border. This is because the Male profile has low prototypicality (i.e., $R^2<.70$) and therefore its plot is untrustworthy. It is also possible to prevent profiles from low prototypicality from plotting using the `lowfit` argument. - -```{r group_plot_lowfit, fig.width = 7.5, fig.height = 4, out.width = "100%"} -ssm_plot(results, lowfit = FALSE) -``` +Any number of groups can be analyzed in this way, and the output will contain additional profile blocks, the table will contain additional rows, and the figure will contain additional points. The grouping variable just needs to contain more than one level (i.e., unique value). ### Generalizing to multiple measures -Similarly, we can analyze multiple external measures in a single function call by providing a vector of variables to the `measures` argument instead of a single variable. This can be done by wrapping the variable names or column numbers with the `c()` or, if they are adjacent columns, with the `:` shortcut. The package functions were written to analyze all measures and groups within a single bootstrap, so adding additional measures and groups should still be fast. +Similarly, we can analyze multiple external measures in a single function call by providing a vector of variables to the `measures` argument instead of a single variable. This can be done by wrapping the variable names or column numbers with the `c()` or, if they are adjacent columns, with the column numbers and the `:` shortcut. The package functions were written to analyze all measures and groups within a single bootstrapping, so adding additional measures and groups should still be fast. ```{r measures} -results2 <- ssm_analyze(jz2017, PA:NO, octants(), measures = c(NARPD, ASPD)) +results2 <- ssm_analyze( + data = jz2017, + scales = PANO(), + angles = octants(), + measures = c("NARPD", "ASPD") +) summary(results2) ``` @@ -69,8 +73,8 @@ ssm_table(results2) ```{r measures_table, echo = FALSE} ssm_table(results2, render = FALSE) %>% - kable(caption = circumplex:::dcaption(results2)) %>% - kable_styling(full_width = TRUE, font_size = 12) + knitr::kable(caption = circumplex:::dcaption(results2)) %>% + kableExtra::kable_styling(full_width = TRUE, font_size = 12) ``` ```{r measures_plot, fig.width = 7.5, fig.height = 4, out.width = "100%"} @@ -78,10 +82,15 @@ ssm_plot(results2) ``` ### Generalizing to multiple groups and multiple measures -Finally, it is possible to analyze multiple measures within multiple groups. As you might expect, this requires providing both the `measures` and `grouping` arguments to the same function call. Again, any number of measures and groups is possible. The profiles in such an analysis will be named GROUP_MEASURE as below. +Finally, it is possible to analyze multiple measures within multiple groups. As you might expect, this requires providing both the `measures` and `grouping` arguments to the same function call. Again, any number of measures and groups is possible. The profiles in such an analysis will be named "Measure: Group" as below. ```{r general} -results3 <- ssm_analyze(jz2017, PA:NO, octants(), grouping = Gender, measures = PARPD:SZTPD) +results3 <- ssm_analyze( + data = jz2017, + scales = PANO(), + angles = octants(), + grouping = "Gender", + measures = 10:12) summary(results3) ``` @@ -91,26 +100,38 @@ ssm_table(results3) ```{r general_table, echo = FALSE} ssm_table(results3, render = FALSE) %>% - kable(caption = circumplex:::dcaption(results3)) %>% - kable_styling(full_width = TRUE, font_size = 12) + knitr::kable(caption = circumplex:::dcaption(results3)) %>% + kableExtra::kable_styling(full_width = TRUE, font_size = 12) ``` ```{r general_plot, fig.width = 7.5, fig.height = 4, out.width = "100%"} ssm_plot(results3) ``` +Note that the borders for the "PARDPD: Male" and "SZTPD: Male" blocks are dashed instead of solid. This indicates that these profiles have low fit (i.e., $R^2<.7$). We could alternatively hide these such profiles by adding `lowfit = FALSE` as below (noting that the color guide may change as profiles are dropped). + +```{r general_plot2, fig.width = 7.5, fig.height = 4, out.width = "100%"} +ssm_plot(results3, lowfit = FALSE) +``` + ## 2. Contrast analyses -The final argument to master is `contrast`, which allows us to compare two groups or measures. The value of this argument must be one of the following strings: "none" for no contrast (default), "model" for a model contrast, or "test" for a parameter contrast. A model contrast can be useful for mean-based analyses and first generates scores for each group (or measure), then calculates the difference between these scores, and finally estimates SSM parameters for these difference scores. A parameter contrast is typically what is desired for inferential comparison and first generates scores for each group (or measure), then estimates SSM parameters for these scores, and finally calculates the difference between the parameters. To keep the code simpler and to discourage "fishing expeditions," only two groups or measures can be compared at a time. +The final argument to master in this vignette is `contrast`, which allows us to compare two groups or measures. Setting this argument to TRUE will first generate scores for each group (or measure), then estimate SSM parameters for these scores, and finally calculate the difference between the parameters. To keep the code simpler and to discourage "fishing expeditions," only two groups or measures can be compared at a time. ### Contrasts between groups' means -To compare the mean profiles for females and males, we can start with the same syntax we had before and then add a `contrast` argument. For the sake of illustration, we will use a model contrast here, but for some purposes, a parameter contrast might be more appropriate. +To compare the mean profiles for females and males, we can start with the same syntax we had before and then add a `contrast` argument. ```{r model_contrast} -results4 <- ssm_analyze(jz2017s, PA:NO, octants(), grouping = Gender, contrast = "model") +results4 <- ssm_analyze( + data = jz2017, + scales = PANO(), + angles = octants(), + grouping = "Gender", + contrast = TRUE +) summary(results4) ``` -Note that the profile blocks we are used to have been replaced by a contrast block. By default, the contrast is made by subtracting the first level of the grouping variable from the second level (e.g., Male - Female). This provides an indication of the direction of the contrast. We can again generate a table and figure to display the results. Note that, because we used a model contrast, the figure will be a circular figure and not a contrast figure which we will see below. However, we would get a contrast figure if we used a parameter contrast. +Note that we have the profile blocks for each group as well as a contrast block. The contrast is made by subtracting the first level of the grouping variable from the second level (e.g., Male - Female). This provides an indication of the direction of the contrast. We can again generate a table and figure to display the results. ```r ssm_table(results4) @@ -118,8 +139,8 @@ ssm_table(results4) ```{r model_table, echo = FALSE} ssm_table(results4, render = FALSE) %>% - kable(caption = circumplex:::dcaption(results4)) %>% - kable_styling(full_width = TRUE, font_size = 12) + knitr::kable(caption = circumplex:::dcaption(results4)) %>% + kableExtra::kable_styling(full_width = TRUE, font_size = 12) ``` ```{r model_plot, fig.width = 7.5, fig.height = 4, out.width = "100%"} @@ -130,8 +151,13 @@ ssm_plot(results4) Comparing measures in a group is very similar. Again, all we need to do is add the `contrast` argument to the function call containing `measures`. Here we will use a parameter contrast to see what they look like. ```{r measure_contrast} -results5 <- ssm_analyze(jz2017, PA:NO, octants(), measures = c(NARPD, ASPD), - contrast = "test") +results5 <- ssm_analyze( + data = jz2017, + scales = PANO(), + angles = octants(), + measures = c("NARPD", "ASPD"), + contrast = TRUE +) summary(results5) ``` @@ -141,8 +167,8 @@ ssm_table(results5) ```{r measure_contrast_table, echo = FALSE, results = "asis"} ssm_table(results5, render = FALSE) %>% - kable(caption = circumplex:::dcaption(results5)) %>% - kable_styling(full_width = TRUE, font_size = 12) + knitr::kable(caption = circumplex:::dcaption(results5)) %>% + kableExtra::kable_styling(full_width = TRUE, font_size = 12) ``` ```{r measure_contrast_plot, fig.width = 7.5, fig.height = 4, out.width = "100%"} @@ -152,11 +178,17 @@ ssm_plot(results5) Here, instead of a circle plot, we see a contrast plot showing the difference between the two measures' SSM parameters and their 95% confidence intervals. Because the confidence interval for the elevation parameter does not include zero, this parameter is significantly different between the measures. ### Contrasts between groups' correlations -Finally, we might want to compare a single measure's profiles in two different groups. To do so, we need to specify the `measures`, the `grouping` variable, and the type of `contrast`. In this case, we need to ensure that we are providing only a single measure and a grouping variable with just two levels (as again only two things can be contrasted at a time). Note that the contrast name in this case will take the form of MEASURE: GROUP2 - GROUP1. +Finally, we might want to compare a single measure's profiles in two different groups. To do so, we need to specify the `measures`, the `grouping` variable, and the type of `contrast`. In this case, we need to ensure that we are providing only a single measure and a grouping variable with just two levels (as again only two things can be contrasted at a time). Note that the contrast name in this case will take the form of "Measure: Group2 - Group1". ```{r group_contrast} -results6 <- ssm_analyze(jz2017, PA:NO, octants(), measures = BORPD, - grouping = Gender, contrast = "test") +results6 <- ssm_analyze( + data = jz2017, + scales = PANO(), + angles = octants(), + measures = "BORPD", + grouping = "Gender", + contrast = TRUE +) summary(results6) ``` @@ -166,8 +198,8 @@ ssm_table(results6) ```{r group_contrast_table, echo = FALSE, results = "asis"} ssm_table(results6, render = FALSE) %>% - kable(caption = circumplex:::dcaption(results6)) %>% - kable_styling(full_width = TRUE, font_size = 12) + knitr::kable(caption = circumplex:::dcaption(results6)) %>% + kableExtra::kable_styling(full_width = TRUE, font_size = 12) ``` ```{r group_contrast_plot, fig.width = 7.5, fig.height = 4, out.width = "100%"} @@ -181,8 +213,7 @@ Although all SSM analyses are based on the idea of decomposing circumplex scores msr <- c("FALSE", "FALSE", "FALSE", "TRUE", "TRUE", "TRUE", "TRUE") grp <- c("FALSE", "TRUE", "TRUE", "FALSE", "FALSE", "TRUE", "TRUE") ctr <- c("FALSE", "FALSE", "TRUE", "FALSE", "TRUE", "FALSE", "TRUE") -tab <- tibble( - `#` = c(1, 2, 3, 4, 5, 6, 7), +tab <- data.frame( Usage = c( "Examine overall mean profile", "Examine groups' mean profiles", @@ -192,13 +223,13 @@ tab <- tibble( "Examine groups' correlation profiles", "Compare groups' correlation profiles" ), - measures = cell_spec(msr, color = ifelse(msr == "TRUE", "blue", "black")), - grouping = cell_spec(grp, color = ifelse(grp == "TRUE", "blue", "black")), - contrast = cell_spec(ctr, color = ifelse(ctr == "TRUE", "blue", "black")) + measures = msr, + grouping = grp, + contrast = ctr ) knitr::kable(tab, escape = FALSE) %>% - column_spec(2, width = "3in") %>% - add_header_above(c("", "", "Arguments Needed" = 3)) + column_spec(1, width = "3in") %>% + add_header_above(c("", "Arguments Needed" = 3)) ``` The three main questions to ask when conducting a new SSM analysis are: @@ -209,58 +240,21 @@ If yes, we must specify one or more `measures`, and the scores that get modeled 1. **Do we want to perform analyses separately for groups within the dataset?** If yes, we must specify a `grouping` variable, and the output will contain results for each group (i.e., unique value of this variable). If no, we must omit the `grouping` argument, and a single set of results for all data will be output. -1. **Do we want to contrast/compare two sets of results?** If yes, we must specify a `contrast` type (i.e., "model" or "test") and ensure that we are only requesting two sets of results (i.e., two groups or two measures). If no, we must omit the `contrast` argument, and the results themselves will be output, rather than their contrast. +1. **Do we want to contrast/compare two sets of results?** If yes, we must specify `contrast` as TRUE and ensure that we are only requesting two sets of results (i.e., two groups or two measures). If no, we must omit the `contrast` argument or set it to FALSE, and the results themselves will be output, rather than their contrast. ## 4. Working with SSM tables ### Basic customizations of tables -Additional arguments to the `ssm_table()` function can be explored using the `?ssm_table` command. Two useful options are the `xy` argument, which shows or hides the x-value and y-value columns, and the `caption` argument which allows a custom string to be printed above the table. Note as well that the return object of this function is just a tibble or data frame, which can be easily edited to add, change, or remove text. To change the formatting of the table, see the `htmlTable` or `kableExtra` packages. - -### Combining and exporting tables -In some cases, such as multiple contrasts or groupings, we may want to combine several tables into one by joining them together as rows. This can be done using the `ssm_append()` function. - -```r -res1 <- ssm_analyze(jz2017s, PA:NO, octants()) -res2 <- ssm_analyze(jz2017s, PA:NO, octants(), grouping = Gender) -tab1 <- ssm_table(res1, xy = FALSE, render = FALSE) -tab2 <- ssm_table(res2, xy = FALSE, render = FALSE) -ssm_append(tab1, tab2) -``` - -```{r append, echo = FALSE} -res1 <- ssm_analyze(jz2017s, PA:NO, octants()) -res2 <- ssm_analyze(jz2017s, PA:NO, octants(), grouping = Gender) -tab1 <- ssm_table(res1, xy = FALSE, render = FALSE) -tab2 <- ssm_table(res2, xy = FALSE, render = FALSE) -ssm_append(tab1, tab2, render = FALSE) %>% - kable() %>% - kable_styling(full_width = TRUE, font_size = 12) -``` +Additional arguments to the `ssm_table()` function can be explored using the `?ssm_table` command. Two useful options are the `drop_xy` argument, which shows or hides the x-value and y-value columns, and the `caption` argument which allows a custom string to be printed above the table. Note as well that the return object of this function is just a tibble or data frame, which can be easily edited to add, change, or remove text. To change the formatting of the table, see the `htmlTable` or `kableExtra` packages. ## 5. Working with SSM figures -### Basic customizations of figures -Additional arguments to the `ssm_plot()` function can be explored using the `?ssm_plot`, `?ssm_plot_circle`, and `?ssm_plot_contrast` commands. For both types of figures, the `fontsize` argument will control the baseline text size in pts (default = 12). Some text elements will be rescaled from the specified `fontsize` (e.g., multiplying it by 150%). For circle plots, we have already seen the `lowfit` argument for including points with low model fit. Another useful option is the `amax` argument for the maximum value of the amplitude scale. By default, the package picks a number that will fit all the points and generate "round" numbers at each 1/5 tick; however, this default can be overwritten using the `amax` argument. - -```{r amax, fig.width = 7.5, fig.height = 4, out.width = "100%"} -ssm_plot(results4, amax = 0.6) -``` - -For contrast plots, several arguments exist for customizing the output. We have already seen the `xy` argument for including or excluding the x-value and y-value contrasts. We can also change the `color` and `linesize` of the point range (linesize in mm). Finally, we can change the `axislabel` displayed to the left of the plots. More complex customizations will be the subject of an advanced vignette. - -```{r custom, fig.width = 7.5, fig.height = 4, out.width = "100%"} -ssm_plot(results6, xy = FALSE, color = "blue", linesize = 1, - axislabel = "BORPD: Male - Female") -``` - ### Exporting figures as files All SSM plots are created using the `ggplot2` package, which is incredibly flexible and powerful. It also offers the `ggsave()` function to export figures to external files of various types. See the documentation for this function (`?ggsave`) to learn more, but some useful arguments are `filename`, `plot`, `width`, `height`, and `units`. We can save the figure as a raster image file (e.g., "png", "jpeg", "tiff"), a vector image file (e.g., "svg"), or a portable document (e.g., "pdf" or "tex"). We can also control the exact width and height of the image in different units (i.e., "in", "cm", or "mm"). Because the underlying graphics are vectorized in R, they can be easily scaled to any size without loss of quality and used in manuscripts, presentations, or posters. ```r -p <- ssm_plot(results6, xy = FALSE, color = "blue", linesize = 1, - axislabel = "BORPD: Male - Female") -ggsave(filename = "bordpd_gender.png", plot = p, width = 7.5, height = 4, - units = "in") +ssm_plot(results6) +ggsave("bordpd_gender.png", width = 7.5, height = 4, units = "in") ``` ## Wrap-up @@ -273,4 +267,4 @@ In this vignette, we learned how to generalize the SSM analyses to multiple grou * Wright, A. G. C., Pincus, A. L., Conroy, D. E., & Hilsenroth, M. J. (2009). Integrating methods to optimize circumplex description and comparison of groups. _Journal of Personality Assessment, 91_(4), 311–322. -* Zimmermann, J., & Wright, A. G. C. (2017). Beyond description in interpersonal construct validation: Methodological advances in the circumplex Structural Summary Approach. _Assessment, 24_(1), 3–23. \ No newline at end of file +* Zimmermann, J., & Wright, A. G. C. (2017). Beyond description in interpersonal construct validation: Methodological advances in the circumplex Structural Summary Approach. _Assessment, 24_(1), 3–23. diff --git a/vignettes/introduction-to-ssm-analysis.Rmd b/vignettes/introduction-to-ssm-analysis.Rmd index e597f77d..3431aa00 100644 --- a/vignettes/introduction-to-ssm-analysis.Rmd +++ b/vignettes/introduction-to-ssm-analysis.Rmd @@ -53,11 +53,11 @@ ggplot2::ggplot() + yend = 5 * sin(angles[c(1, 3, 5, 7)] * pi / 180) ), color = "gray60", - size = 1 + linewidth = 1 ) + # Draw inner labels for the octant angles ggplot2::geom_label( - aes( + ggplot2::aes( x = 3 * cos(angles * pi / 180), y = 3 * sin(angles * pi / 180), label = sprintf("%d\u00B0", angles) @@ -106,7 +106,7 @@ dat_r <- tibble::tibble( # Create column plot ggplot2::ggplot(dat_r, ggplot2::aes(x = Scale, y = r)) + - ggplot2::geom_hline(yintercept = 0, size = 1.25, color = "darkgray") + + ggplot2::geom_hline(yintercept = 0, linewidth = 1.25, color = "darkgray") + ggplot2::geom_col(position = ggplot2::position_dodge(.9), fill = "red") + ggplot2::scale_y_continuous( limits = c(-0.02, 0.5) @@ -135,10 +135,10 @@ dat_r <- tibble::tibble( # Plot correlations as connected point ranges with 95% CI ranges ggplot2::ggplot(dat_r, ggplot2::aes(x = Angle, y = est)) + - ggplot2::geom_hline(yintercept = 0, size = 1.25, color = "darkgray") + + ggplot2::geom_hline(yintercept = 0, linewidth = 1.25, color = "darkgray") + ggplot2::geom_point(size = 3, color = "red") + - ggplot2::geom_path(size = 1.25, color = "red") + - ggplot2::geom_label(aes(label = Scale), nudge_y = 0.075) + + ggplot2::geom_path(linewidth = 1.25, color = "red") + + ggplot2::geom_label(ggplot2::aes(label = Scale), nudge_y = 0.075) + ggplot2::scale_x_continuous( limits = c(0, 360), breaks = c(0, octants()), @@ -171,9 +171,9 @@ f <- function(x) { # Plot correlations along with SSM cosine model ggplot2::ggplot(dat_r, ggplot2::aes(x = Angle, y = est)) + - ggplot2::geom_hline(yintercept = 0, size = 1.25, color = "darkgray") + + ggplot2::geom_hline(yintercept = 0, linewidth = 1.25, color = "darkgray") + ggplot2::geom_point(size = 3) + - ggplot2::geom_path(size = 1.25) + + ggplot2::geom_path(linewidth = 1.25) + ggplot2::stat_function(fun = f, size = 2, color = "red") + ggplot2::scale_x_continuous( limits = c(0, 360), @@ -202,13 +202,13 @@ where $S_i$ and $\theta_i$ are the score and angle on scale $i$, respectively, a ```{r residuals, echo = FALSE, fig.width = 7.5, fig.height = 4, out.width = "100%"} # Plot correlations as path, SSM cosine model, and differences ggplot2::ggplot(dat_r, ggplot2::aes(x = Angle, y = est)) + - ggplot2::geom_hline(yintercept = 0, size = 1.25, color = "darkgray") + + ggplot2::geom_hline(yintercept = 0, linewidth = 1.25, color = "darkgray") + ggplot2::stat_function(fun = f, size = 2, color = "gray20") + ggplot2::geom_point(size = 5.5, color = "black") + - ggplot2::geom_path(size = 1.25, color = "black") + + ggplot2::geom_path(linewidth = 1.25, color = "black") + ggplot2::geom_segment( ggplot2::aes(x = Angle, xend = Angle, y = est, yend = f(Angle)), - size = 4, linetype = "solid", color = "red" + linewidth = 4, linetype = "solid", color = "red" ) + ggplot2::scale_x_continuous( limits = c(0, 360), @@ -256,7 +256,7 @@ To illustrate the SSM functions, we will use the example dataset `jz2017`, which ```{r jz2017} data("jz2017") -print(jz2017) +head(jz2017) ``` The circumplex scales in `jz2017` come from the Inventory of Interpersonal Problems - Short Circumplex (IIP-SC). These scales can be arranged into the following circular model, which is organized around the two primary dimensions of agency (y-axis) and communion (x-axis). Note that the two-letter scale abbreviations and angular values are based in convention. A high score on PA indicates that one has interpersonal problems related to being "domineering" or too high on agency, whereas a high score on DE indicates problems related to being "cold" or too low on communion. Scales that are not directly on the y-axis or x-axis (i.e., BC, FG, JK, and NO) represent blends of agency and communion. @@ -293,7 +293,7 @@ ggplot2::ggplot() + yend = 5 * sin(angles * pi / 180) ), color = "gray60", - size = 0.5 + linewidth = 0.5 ) + # Draw inner labels for the octant abbreviations ggplot2::geom_label( @@ -345,26 +345,14 @@ ggplot2::ggplot() + ## 3. Mean-based SSM Analysis ### Conducting SSM for a group's mean scores -To begin, let's say that we want to use the SSM to describe the interpersonal problems of the average individual in the entire dataset. Although it is possible to analyze the raw scores contained in `jz2017`, our results will be more interpretable if we standardize the scores first. We can do this using the `standardize()` function. The first argument to this function is `.data`, a data frame frame containing the circumplex scales to be standardized. The second argument is `scales` and specifies where in `.data` the circumplex scales are (either in terms of their variable names or their column numbers). The third argument is `angles` and specifies the angle of each of the circumplex scales included in `scales`. Note that the `scales` and `angles` arguments need to be vectors (hence the `c()` function) that have the same ordering and length. Finally, the fourth argument is `norms`, a data frame containing the normative data we will use to standardize the circumplex scales. Here, we will use normative data for the IIP-SC by loading the `iipsc` data frame. - -```{r} -data("iipsc") -jz2017s <- standardize( - .data = jz2017, - scales = c(PA, BC, DE, FG, HI, JK, LM, NO), - angles = c(90, 135, 180, 225, 270, 315, 360, 45), - instrument = iipsc, - sample = 1 -) -print(jz2017s) -``` +To begin, let's say that we want to use the SSM to describe the interpersonal problems of the average individual in the entire dataset. We will use the raw scores contained in `jz2017`, although note that the results may be more interpretable if we standardized the scores first (e.g., using the `norm_standardize()` function as described in the "Using Circumplex Instruments" vignette). -Now we can use the `ssm_analyze()` function to perform the SSM analysis. The first three arguments are the same as the first three arguments to `standardize()`. We can pass the new `jz2017s` data frame that contains standardized scores as `.data` and the same vectors to `scales` and `angles` since these haven't changed. +We can use the `ssm_analyze()` function to perform the SSM analysis. The first three arguments to this function are `data` (the data frame containing our scores), `scales` (a vector of the names or numbers of columns containing the circumplex scales), and `angles` (a vector of the circular angle of each scale, in degrees). Below, I spell out the names of all the `scales` and the numbers of all the `angles` for teaching purposes. Later on, we will learn some shortcuts. ```{r analyze} results <- ssm_analyze( - .data = jz2017s, - scales = c(PA_z, BC_z, DE_z, FG_z, HI_z, JK_z, LM_z, NO_z), + data = jz2017, + scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), angles = c(90, 135, 180, 225, 270, 315, 360, 45) ) ``` @@ -375,10 +363,10 @@ The output of the function has been saved in the `results` object, which we can summary(results) ``` -That was pretty easy! We can now write up these results. However, the `circumplex` package has some features that can make what we just did even easier. First, because the first three arguments of the `ssm_analyze()` function are always the same, we can omit their names. Second, because we organized the `jz2017s` data frame to have the circumplex scale variables adjacent and in order from PA to NO, we can simplify their specification by using the `PA:NO` shortcut. Finally, because the use of octant scales is so common, the `circumplex` package comes with a convenience function for outputting their angular displacements: `octants()`. Note how, even when using these shortcuts, the results are the same except for minor stochastic differences in the confidence intervals due to the randomness inherent to bootstrapping. (To get the exact same results, we could use the `set.seed()` function to control the random number generator in R.) +That was pretty easy! We can now write up these results. However, the `circumplex` package has some features that can make what we just did even easier. First, because we organized the `jz2017` data frame to have the circumplex scale variables adjacent and in order from PA to NO, we can simplify their specification by using the `PANO()` shortcut. Second, because the use of octant scales is so common, we can use the `octants()` shortcut. Note that, even when using these shortcuts, the results are the same except for minor stochastic differences in the confidence intervals due to the randomness inherent to bootstrapping. ```{r summary1b} -results2 <- ssm_analyze(jz2017s, PA_z:NO_z, octants()) +results2 <- ssm_analyze(data = jz2017, scales = PANO(), angles = octants()) summary(results2) ``` @@ -391,8 +379,8 @@ ssm_table(results2) ```{r table1, echo = FALSE} ssm_table(results2, render = FALSE) %>% - kable(caption = circumplex:::dcaption(results2)) %>% - kable_styling(full_width = TRUE, font_size = 14) + knitr::kable(caption = circumplex:::dcaption(results2)) %>% + kableExtra::kable_styling(full_width = TRUE, font_size = 14) ``` Next, let's leverage the fact that we are working within a circumplex space by creating a nice-looking circular plot by mapping the amplitude parameter to the points' distance from the center of the circle and the displacement parameter to the points' rotation from due-east (as is conventional). This, again, is as simple as passing the `results` object to the `ssm_plot()` function. @@ -406,10 +394,15 @@ ssm_plot(results2) ### Conducting SSM for a group's correlations with an external measure Next, let's say that we are interested in analyzing not the mean scores on the circumplex scales but rather their correlations with an external measure. This is sometimes referred to as "projecting" that external measure into the circumplex space. As an example, let's project the NARPD variable, which captures symptoms of narcissistic personality disorder, into the circumplex space defined by the IIP-SC. Based on theory and previous findings, we can expect this measure to be associated with some general interpersonal distress and a style that is generally high in agency. -To conduct this analysis, we can start with the syntax from the mean-based analysis. All SSM analyses use the `ssm_analyze()` function and the data, scales, and angles are the same as before. However, we also need to let the function know that we want to analyze correlations with NARPD as opposed to scale means. To do this, we add an additional argument `measures`. Note that since correlations are already standardized, we don't need to worry about standardizing the circumplex scales when `measures` is used. +To conduct this analysis, we can start with the syntax from the mean-based analysis. All SSM analyses use the `ssm_analyze()` function and the data, scales, and angles are the same as before. However, we also need to let the function know that we want to analyze correlations with NARPD as opposed to scale means. To do this, we add an additional argument `measures`. ```{r summary2} -results3 <- ssm_analyze(jz2017, PA:NO, octants(), measures = NARPD) +results3 <- ssm_analyze( + data = jz2017, + scales = PANO(), + angles = octants(), + measures = "NARPD" +) summary(results3) ``` @@ -424,8 +417,8 @@ ssm_table(results3) ```{r table2, echo = FALSE} ssm_table(results3, render = FALSE) %>% - kable(caption = circumplex:::dcaption(results3)) %>% - kable_styling(full_width = TRUE, font_size = 14) + knitr::kable(caption = circumplex:::dcaption(results3)) %>% + kableExtra::kable_styling(full_width = TRUE, font_size = 14) ``` From the table, we can see that the model fit is good (>.80) and that all three SSM parameters are significantly different from zero, i.e., their confidence intervals do not include zero. Furthermore, the confidence intervals for the elevation and amplitude parameters are greater than or equal to 0.15, which can be interpreted as being "marked." So, consistent with our hypotheses, NARPD was associated with marked general interpersonal distress (elevation) and was markedly distinctive in its profile (amplitude). The displacement parameter was somewhere between 100 and 120 degrees; to interpret this we would need to either consult the mapping between scales and angles or plot the results. diff --git a/vignettes/using-instruments.Rmd b/vignettes/using-instruments.Rmd index 7d45c22d..6f2fb562 100644 --- a/vignettes/using-instruments.Rmd +++ b/vignettes/using-instruments.Rmd @@ -13,7 +13,6 @@ knitr::opts_chunk$set(collapse = TRUE, comment = "#>") ```{r setup, message=FALSE} library(circumplex) -library(dplyr) ``` @@ -22,7 +21,7 @@ Although the circumplex package is capable of analyzing and visualizing data in The first part of this vignette will discuss how to preview the instruments currently available in the circumplex package, how to load information about a specific instrument for use in analysis, and how to extract general and specific information about that instrument. The following functions will be discussed: `instruments()`, `instrument()`, `print()`, `summary()`, `scales()`, `items()`, `anchors()`, `norms()`, and `View()`. -The second part of this vignette will discuss how to use the information about an instrument to transform and summarize circumplex data. It will demonstrate how to ipsatize item-level responses (i.e., apply deviation scoring across variables), how to calculate scale scores from item-level responses (with or without imputing/prorating missing values), and how to standardize scale scores using normative/comparison data. The following functions will be discussed: `ipsatize()`, `score()`, and `standardize()`. +The second part of this vignette will discuss how to use the information about an instrument to transform and summarize circumplex data. It will demonstrate how to ipsatize item-level responses (i.e., apply deviation scoring across variables), how to calculate scale scores from item-level responses (with or without imputing/prorating missing values), and how to standardize scale scores using normative/comparison data. The following functions will be discussed: `ipsatize()`, `score()`, and `norm_standardize()`. ## 2. Loading and Examining Instrument Objects @@ -39,7 +38,7 @@ If there are additional circumplex instruments you would like to have added to t To reduce loading time and memory usage, instrument information is not loaded into R's memory when the circumplex package is loaded. Instead, instruments should be loaded into memory on an as-needed basis. As demonstrated below, this can be done by passing an instrument's code (which we saw how to find in the last section) to the `instrument()` function. We can then examine that instrument data using the `print()` function. ```{r} -instrument(csip) +instrument("csip") print(csip) ``` @@ -50,7 +49,7 @@ print(isc) ``` ```{r} -instrument(isc) +instrument("isc") print(isc) ``` @@ -107,11 +106,11 @@ print(raw_iipsc) For some forms of circumplex data analysis (e.g., analysis of circumplex fit) but not others (e.g., structural summary method), it can be helpful to transform item-level responses by subtracting each participant's mean across all items from his or her response on each item. This practice is called "ipsatizing" or, more precisely, deviation scoring across variables. This practice will attenuate the general factor across all items and recasts the item-level responses as deviations from one's own mean rather than absolute responses. To perform ipsatizing and create a new set of ipsatized responses to each item, use the `ipsatize()` function. ```{r} -ips_iipsc <- ipsatize(.data = raw_iipsc, items = IIP01:IIP32, na.rm = TRUE, overwrite = TRUE) +ips_iipsc <- ipsatize(data = raw_iipsc, items = 1:32, append = FALSE) print(ips_iipsc) ``` -Above, we told the function to overwrite the original item variables with their ipsatized versions; new ipsatized variables (ending in `_i`) could have been appended to the data frame instead by changing the `overwrite` argument to `FALSE`. We also told it to calculate the mean for each observation after ignoring any missing values. We could have changed this by setting the `na.rm` argument to `FALSE`. +Above, we told the function to take all the variables from column 1 to column 32 and calculate new ipsatized variables ending in `_i` (although different `prefix`es and `suffix`es can be used). By default, the mean for each observation/row is calculated after ignoring any missing values, but we could have changed this by adding `na.rm = FALSE`. By setting `append` to FALSE, only the ipsatized versions are returned, but if we changed this to TRUE then the original variables would have been included as well. We can check that the ipsatization was successful by calculating the mean of each row (i.e., each participant's mean response) in the original and ipsatized data frames. We do this below using the `rowMeans()` function; we also apply the `round()` function to make the results fit on one row. As expected, we find below that the mean of each participant is zero in the ipsatized data frame but not in the original. @@ -133,20 +132,21 @@ Although calculating the arithmetic mean of a handful of items is not terribly d To demonstrate, let's return to the `raw_iipsc` data set. We need to give the `score()` function a data frame containing the item-level data (i.e., the data set), a list of variables from that data frame that contain the item-level responses to be scored, and an instrument object containing instructions on how to score the data. In order for scoring to work properly, **the list of items must be in ascending order from the first to the last item and the ordering of the items must be the same as that assumed by the package**. Be sure to check your item numbers against those displayed by the `items()` function, especially if you shuffle your items. ```{r} -scale_scores <- score(.data = raw_iipsc, items = IIP01:IIP32, instrument = iipsc) +scale_scores <- score( + data = raw_iipsc, + items = 1:32, + instrument = iipsc, + append = FALSE +) print(scale_scores) ``` -The `scale_scores` data frame contains all the item-level data from `item_level_csie`, as well as the newly calculated scale scores. Note that the scale score variables were named using the scale abbreviations shown by the `scales()` function (i.e., two-letter abbreviations from `PA` to `NO`). You can customize the naming of these variables by using the `prefix` and `suffix` arguments from the `score()` function. We can select and view just the scale score variables using the `select()` function. - -```{r} -select(scale_scores, PA:NO) -``` +Because we set `append` to FALSE, the `scale_scores` data frame contains only the scale score variables. These were named using the scale abbreviations shown by the `scales()` function (i.e., two-letter abbreviations from PA to NO). You can customize the naming of these variables by using the `prefix` and `suffix` arguments (e.g., to make them IIP_PA to IIP_NO). Note that the `na.rm` argument for the `score()` function defaulted to `TRUE`, which means that missing values were ignored in calculating the scale scores. This practice is common in the literature, but is technically a form of single imputation and thus can produce biased results when data are not missing completely at random (MCAR). Please examine and report the amount and patterns of missingness in your data. ### Standardizing scale-level data -Finally, it can often be helpful to transform scale-level data through reference to a normative or comparison sample. This is often called "standardizing" and involves subtracting the normative sample's mean score on a scale from each participant's score on that scale and then dividing this difference by the normative sample's standard deviation. This rescales the scale scores to be in standard deviation units and to describe the magnitude of each participant's difference from the normative average. +Finally, it can often be helpful to transform scale-level data through reference to a normative or comparison sample. This is often called "norm standardizing" and involves subtracting the normative sample's mean score on a scale from each participant's score on that scale and then dividing this difference by the normative sample's standard deviation. This rescales the scale scores to be in standard deviation units and to describe the magnitude of each participant's difference from the normative average. For many circumplex instruments, the data needed to perform standardization is included in its instrument object. Some instruments even have multiple (e.g., different or overlapping) normative samples for comparisons that are matched in terms of gender, age, or nationality. In selecting a normative sample to compare to, it is important to consider both the size and the appropriateness of the sample. @@ -158,28 +158,22 @@ norms(iipsc) Assuming our example data also come from a non-psychiatric community sample of mostly college students, the first normative sample seems like a better choice, especially since it is so much larger and therefore subject to less sampling error. However, there may be times when the second normative sample would be the more appropriate comparison, even despite its smaller sample. -To transform the scale scores we calculated during the last section, we can call the `standardize()` function and give it the `scale_scores` object we saved the item-level and scale-level data within. We will save the output of this function to a data frame named `z_scales` to reflect the idea that standardized scores are often called "z-scores." +To transform the scale scores we calculated during the last section, we can call the `norm_standardize()` function and give it the `scale_scores` object we created above. We will save the output of this function to a data frame named `z_scales` to reflect the idea that standardized scores are often called "z-scores." ```{r} -z_scales <- standardize( - .data = scale_scores, - scales = PA:NO, - angles = octants(), +z_scales <- norm_standardize( + data = scale_scores, + scales = 1:8, instrument = iipsc, - sample = 1 + sample = 1, + append = FALSE ) print(z_scales) ``` -Again, the output contains all the item-level, scale-level, and now the standardized scale-level variables. The new variables are named the same as the scale score variables except with a configurable `prefix` and `suffix` (by default, they are given only a suffix of `_z`). We can again use the `select()` function to view just the newly created standardized variables. These variables are the ones we are most likely to use in subsequent analyses (e.g., the structural summary method). - -```{r} -select(z_scales, PA_z:NO_z) -``` +Again, because we set `append` to FALSE, the output contains only the norm standardized scale-level variables. The new variables are named the same as the scale score variables except with a configurable `prefix` and `suffix` (by default, they are given only a suffix of `_z`). These variables are the ones we are most likely to use in subsequent analyses (e.g., the structural summary method). ## 4. Wrap-up In this vignette, we learned how to preview the instruments available in the circumplex package, load and examine the information contained in one of these instrument objects, ipsatize item-level data, calculate scale scores from item-level data, and standardize those scale scores using normative data included in the package. We are now in an excellent position to discover and implement new circumplex instruments. Later vignettes describe analyses and visualizations that make use of the data collected using these tools. Special thanks to the authors and publishers who granted permission to include information about their instruments in this package: Chloe Bliton, Michael Boudreaux, Robert Hatcher, Christopher Hopwood, Leonard Horowitz, Kenneth Locke, Patrick Markey, Aaron Pincus, Elisa Trucco, and MindGarden Inc. - -