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Updated code to include 'group_modify' with the broom::tidy call #19

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Updated code to include 'group_modify' with the broom::tidy call #19

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69 changes: 48 additions & 21 deletions 09-Two-Level-Longitudinal-Data.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,7 @@ chart.long <- chart.wide %>%
gather(key = "key", value = "MathAvgScore",
MathAvgScore.0:MathAvgScore.2) %>%
separate(key, into = c("name", "year08"), sep = "\\.") %>%
select(-c("X1", "name")) %>%
select(-c("...1", "name")) %>%
arrange(schoolid, year08) %>%
mutate(year08 = as.numeric(year08))
head(chart.long)
Expand Down Expand Up @@ -184,7 +184,7 @@ wide.public <- chart.wide %>%
filter(charter == 0) %>%
sample_n( dim(wide.charter)[1] )
sampdata <- bind_rows(wide.charter, wide.public) %>%
select(-X1) %>%
select(-`...1`) %>%
mutate(vars = row_number()) # Just use numbers 1-146 as school ids
head(sampdata)

Expand Down Expand Up @@ -384,9 +384,14 @@ Another advantage of assuming a linear trend at Level One (within schools) is th

```{r, include=FALSE, warning=FALSE}
#95% CI's for slope and intercept of 24 schools (2 are filtered out since 1 obs)
regressions <- smallchart.long %>%
group_by(schoolid) %>%
do(fit = lm(MathAvgScore ~ year08, data=.))
# regressions <- smallchart.long %>%
# group_by(schoolid) %>%
# do(fit = lm(MathAvgScore ~ year08, data=.))

regressions <- smallchart.long %>%
group_by(schoolid) %>%
# do(fit = lm(MathAvgScore ~ year08, data=.))
group_modify(~ broom::tidy(lm(MathAvgScore ~ year08, data = .x)))

sd_filter <- smallchart.long %>%
group_by(schoolid) %>%
Expand All @@ -397,22 +402,31 @@ regressions <- regressions %>%
filter(!is.na(sds))

lm_info1 <- regressions %>%
tidy(fit) %>%
ungroup() %>%
# tidy(fit) %>%
# ungroup() %>%
select(schoolid, term, estimate) %>%
spread(key = term, value = estimate) %>%
rename(rate = year08, int = `(Intercept)`)

lm_info2 <- regressions %>%
tidy(fit) %>%
ungroup() %>%
# tidy(fit) %>%
# ungroup() %>%
select(schoolid, term, std.error) %>%
spread(key = term, value = std.error) %>%
rename(se_rate = year08, se_int = `(Intercept)`)

lm_info <- regressions %>%
glance(fit) %>%
ungroup() %>%
regressions_glance <- smallchart.long %>%
group_by(schoolid) %>%
# do(fit = lm(MathAvgScore ~ year08, data=.))
group_modify(~ broom::glance(lm(MathAvgScore ~ year08, data = .x)))

regressions_glance <- regressions_glance %>%
right_join(sd_filter, by="schoolid") %>%
filter(!is.na(sds))

lm_info <- regressions_glance %>%
# glance(fit) %>%
# ungroup() %>%
select(schoolid, r.squared, df.residual) %>%
inner_join(lm_info1, by = "schoolid") %>%
inner_join(lm_info2, by = "schoolid") %>%
Expand Down Expand Up @@ -447,7 +461,8 @@ grid.arrange(slope.ci,int.ci,ncol=2)
# Find slope and intercept of all 618 schools (540 after filter those with 1 obs)
regressions <- chart.long %>%
group_by(schoolid) %>%
do(fit = lm(MathAvgScore ~ year08, data=.))
# do(fit = lm(MathAvgScore ~ year08, data=.))
group_modify(~ broom::tidy(lm(MathAvgScore ~ year08, data = .x)))

sd_filter <- chart.long %>%
group_by(schoolid) %>%
Expand All @@ -458,22 +473,31 @@ regressions <- regressions %>%
filter(!is.na(sds))

lm_info1 <- regressions %>%
tidy(fit) %>%
ungroup() %>%
# tidy(fit) %>%
# ungroup() %>%
select(schoolid, term, estimate) %>%
spread(key = term, value = estimate) %>%
rename(rate = year08, int = `(Intercept)`)

lm_info2 <- regressions %>%
tidy(fit) %>%
ungroup() %>%
# tidy(fit) %>%
# ungroup() %>%
select(schoolid, term, std.error) %>%
spread(key = term, value = std.error) %>%
rename(se_rate = year08, se_int = `(Intercept)`)

lm_info <- regressions %>%
glance(fit) %>%
ungroup() %>%
regressions_glance <- chart.long %>%
group_by(schoolid) %>%
# do(fit = lm(MathAvgScore ~ year08, data=.))
group_modify(~ broom::glance(lm(MathAvgScore ~ year08, data = .x)))

regressions_glance <- regressions_glance %>%
right_join(sd_filter, by="schoolid") %>%
filter(!is.na(sds))

lm_info <- regressions_glance %>%
# glance(fit) %>%
# ungroup() %>%
select(schoolid, r.squared, df.residual) %>%
inner_join(lm_info1, by = "schoolid") %>%
inner_join(lm_info2, by = "schoolid") %>%
Expand All @@ -482,7 +506,6 @@ lm_info <- regressions %>%
intub = int + tstar * se_int,
ratelb = rate - tstar * se_rate,
rateub = rate + tstar * se_rate)
head(data.frame(lm_info))

# summary stats for intercepts
summary(lm_info$int)
Expand Down Expand Up @@ -1262,9 +1285,13 @@ anova(cs.lme,std.lme)

# Heterogeneous compound symmetry error structure
# lmeControl(msMaxIter=200) # didn't help
# https://stats.stackexchange.com/questions/40647/lme-error-iteration-limit-reached
ctrl <- lmeControl(opt='optim');

hcs.lme=lme(MathAvgScore ~ year08 * charter, chart.long,
random = ~ 1 | schoolid, na.action=na.exclude,
correlation=corCompSymm(form = ~ 1 |schoolid),
control=ctrl,
weights=varIdent(form = ~1|year08))
summary(hcs.lme)
hcs.lme$modelStruct
Expand Down