version-2.0.3

.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$

.gitignore

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+.Rproj.user
+.Rhistory
+.RData

DESCRIPTION

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+Package: basicTrendline
+Version: 2.0.3
+Date: 2018-07-26
+Title: Add Trendline and Confidence Interval of Basic Regression Models to Plot
+Authors@R: c(
+		person("Weiping", "Mei",     email = "[email protected]", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0001-6400-9862")),
+		person("Guangchuang", "Yu",     email = "[email protected]", role = c("aut"), comment = c(ORCID = "0000-0002-6485-8781")),
+		person("Jiangshan", "Lai", role = c("ctb")),
+		person("Qiang", "Rao",   role = "ctb"),
+		person("Yu", "Umezawa",  role = "ctb")
+	   )
+Maintainer: Weiping Mei <[email protected]>
+Description:  Plot, draw regression line and confidence interval, and show regression equation, R-square and P-value,  as simple as possible, by using different models ("line2P", "line3P", "log2P", "exp2P", "exp3P", "power2P", "power3P") built in the 'trendline()' function.
+Depends: R (>= 2.1.0)
+Imports:
+    graphics,
+    stats,
+    scales,
+    investr
+BugReports: https://github.com/PhDMeiwp/basicTrendline/issues
+License: GPL-3
+URL: https://github.com/PhDMeiwp/basicTrendline
+LazyData: true
+RoxygenNote: 6.0.1
+
+

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(SSexp2P)
+export(SSexp3P)
+export(SSpower2P)
+export(SSpower3P)
+export(trendline)
+export(trendline_summary)
+import(graphics)
+import(investr)
+import(scales)
+import(stats)

NEWS

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+Changes in version 2.0.3
+------------------------------------------------------------------------------
+Date: 2018-07-26
+* add several arguments to `trendline()` function, including show.equation, show.Rpvalue, Rname, Pname, xname, yname, yhat, CI.fill, CI.level, CI.alpha, CI.color, CI.lty, CI.lwd, ePos.x, ePos.y, las.
+* enable to draw confidence interval for regression models (arguments CI.fill, CI.level, etc.)
+* add 'show.equation' and show.Rpvale' arguments to enable to choose which parameter to show
+* add 'Rname' and 'Pname' arguments to specify the character of R-square and P-vlaue (i.e. R^2 or r^2; P or p)
+* add 'xname' and 'ynameto' arguments to specify the character of 'x' and 'y' in the equation
+* add 'yhat' argument to enable to add a hat symbol on the top of 'y' in the equation
+* add 'ePos.x' and 'ePos.y' arguments to specify the x and y co-ordinates of equation's position
+* deleted the 'ePos' argument 
+* add "Residual Sum of Squares" to the output of 'trendline_summary()' function
+
+
+Changes in version 1.2.0
+------------------------------------------------------------------------------
+Date: 2018-07-13
+* change the expression for `model` of `exp3P` using a supscript
+* add `trendline_summary()` function
+* add `SSexp2P()` function
+* add `SSpower2P` function
+* add `Pvalue.corrected` argument in `trendline()` and `trendline_summary()` , for P-vlaue calculation for non-linear regression.
+* add `Details` in `trendline()` and `trendline_summary()` 
+* add `...` argument in `trendline()` as same as those in `plot()`
+

R/SSexp2P.R

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+#' Self-Starting Nls 'exp2P' Regression Model
+#'
+#' This selfStart model evaluates the power regression function (formula as: y=a*exp(b*x)). It has an initial attribute that will evaluate initial estimates of the parameters 'a' and 'b' for a given set of data.
+#'
+#' @usage SSexp2P(predictor, a, b)
+#' @param predictor  a numeric vector of values at which to evaluate the model.
+#' @param a,b The numeric parameters responsing to the exp2P model.
+#' @export
+#' @examples
+#' library(basicTrendline)
+#' x<-1:5
+#' y<-c(2,4,8,20,25)
+#' xy<-data.frame(x,y)
+#' getInitial(y ~ SSexp2P(x,a,b), data = xy)
+#' ## Initial values are in fact the converged values
+#'
+#' fitexp2P <- nls(y~SSexp2P(x,a,b), data=xy)
+#' summary(fitexp2P)
+#'
+#' @author Weiping Mei \email{[email protected]}
+#' @seealso  \code{\link{trendline}}, \code{\link{SSexp3P}}, \code{\link{SSpower3P}}, \code{\link[stats]{nls}}, \code{\link[stats]{selfStart}}
+
+
+# selfStart method for exp2P model (formula as y = a *exp(b*x))
+SSexp2P<-selfStart(
+  function(predictor,a,b){a*exp(b*predictor)},
+  function(mCall,LHS, data)
+  {
+    xy <- sortedXyData(mCall[["predictor"]],LHS, data)
+
+    if (min(y)>0){
+    lmFit <- lm(log(xy[,"y"]) ~ xy[,"x"])
+    coefs <- coef(lmFit)
+    a <- exp(coefs[1])  #intercept
+    b <- coefs[2]   #slope
+    value <- c(a, b)
+    names(value) <- mCall[c("a","b")]
+    value
+  }else{stop("
+>>Try to use other selfStart functions.
+Because the 'SSexp2P' function need ALL x values greater than 0.")
+  }
+  },c("a","b"))
+
+# getInitial(y~SSexp2P(x,a,b),data = xy)

R/SSexp3P.R

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+#' Self-Starting Nls 'exp3P' Regression Model
+#'
+#' This selfStart model evaluates the exponential regression function (formula as: y=a*exp(b*x)+c). It has an initial attribute that will evaluate initial estimates of the parameters a, b, and c for a given set of data.
+#'
+#' @usage SSexp3P(predictor, a, b, c)
+#' @param predictor  a numeric vector of values at which to evaluate the model.
+#' @param a,b,c Three numeric parameters responsing to the exp3P model.
+#' @export
+#' @examples
+#' library(basicTrendline)
+#' x<-1:5
+#' y<-c(2,4,8,16,28)
+#' xy<-data.frame(x,y)
+#' getInitial(y ~ SSexp3P(x,a,b,c), data = xy)
+#' ## Initial values are in fact the converged values
+#'
+#' fitexp3P <- nls(y~SSexp3P(x,a,b,c), data=xy)
+#' summary(fitexp3P)
+#'
+#' @author Weiping Mei \email{[email protected]}
+#' @seealso  \code{\link{trendline}}, \code{\link{SSexp3P}}, \code{\link{SSpower3P}}, \code{\link[stats]{nls}}, \code{\link[stats]{selfStart}}
+
+# selfStart method for exp3P model (formula as y = a *exp(b*x)+ c)
+SSexp3P<-selfStart(
+  function(predictor,a,b,c){a*exp(b*predictor)+c},
+  function(mCall,LHS, data)
+  {
+    xy <- sortedXyData(mCall[["predictor"]],LHS, data)
+    y=xy[,"y"]
+    x=xy[,"x"]
+    adjy=y-min(y)+1
+    xadjy=data.frame(x,adjy)
+
+    lmFit <- lm(log(adjy) ~ x)
+    coefs <- coef(lmFit)
+    get.b <- coefs[2]   #slope
+
+    nlsFit<-nls(adjy~cbind(1+exp(b*x),exp(b*x)),
+                start = list(b=get.b),data = xadjy,algorithm = "plinear",
+                nls.control(maxiter = 5000000,minFactor = 10^(-10)))
+
+    coef<-coef(nlsFit)
+    b<-coef[1]
+    c<-coef[2]+min(y)-1
+    a<-coef[3]+coef[2]
+
+    value <- c(a,b,c)
+    names(value) <- mCall[c("a","b","c")]
+    value
+  },c("a","b","c"))
+
+  # getInitial(y~SSexp3P(x,a,b,c),data = z)

R/SSpower2P.R

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+#' Self-Starting Nls 'power2P' Regression Model
+#'
+#' This selfStart model evaluates the power regression function (formula as: y=a*x^b). It has an initial attribute that will evaluate initial estimates of the parameters 'a' and 'b' for a given set of data.
+#'
+#' @usage SSpower2P(predictor, a, b)
+#' @param predictor  a numeric vector of values at which to evaluate the model.
+#' @param a,b The numeric parameters responsing to the exp2P model.
+#' @export
+#' @examples
+#' library(basicTrendline)
+#' x<-1:5
+#' y<-c(2,4,8,20,25)
+#' xy<-data.frame(x,y)
+#' getInitial(y ~ SSpower2P(x,a,b), data = xy)
+#' ## Initial values are in fact the converged values
+#'
+#' fitpower2P <- nls(y~SSpower2P(x,a,b), data=xy)
+#' summary(fitpower2P)
+#'
+#' @author Weiping Mei \email{[email protected]}
+#' @seealso  \code{\link{trendline}}, \code{\link{SSexp3P}}, \code{\link{SSpower3P}}, \code{\link[stats]{nls}}, \code{\link[stats]{selfStart}}
+
+
+# selfStart method for power2P model (formula as y = a *x^b)
+SSpower2P<-selfStart(
+  function(predictor,a,b){a*predictor^b},
+  function(mCall,LHS, data)
+{
+  xy <- sortedXyData(mCall[["predictor"]],LHS, data)
+
+  if (min(x)>0){
+  lmFit <- lm(log(xy[,"y"]) ~ log(xy[,"x"])) # both x and adjy values should be greater than 0.
+  coefs <- coef(lmFit)
+  a <- exp(coefs[1])  #intercept
+  b <- coefs[2]   #slope
+
+  value <- c(a,b)
+  names(value) <- mCall[c("a","b")]
+  value
+
+  }else{stop("
+>>Try to use other selfStart functions.
+Because the 'SSpower2P' function need ALL x values greater than 0.")
+  }
+  },c("a","b"))
+
+# getInitial(y~SSpower2P(x,a,b),data = xy)

R/SSpower3P.R

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+#' Self-Starting Nls 'power3P' Regression Model
+#'
+#' This selfStart model evaluates the power regression function (formula as: y=a*x^b+c). It has an initial attribute that will evaluate initial estimates of the parameters a, b, and c for a given set of data.
+#'
+#' @usage SSpower3P(predictor, a, b, c)
+#' @param predictor  a numeric vector of values at which to evaluate the model.
+#' @param a,b,c Three numeric parameters responsing to the exp3P model.
+#' @export
+#' @examples
+#' library(basicTrendline)
+#' x<-1:5
+#' y<-c(2,4,8,20,25)
+#' xy<-data.frame(x,y)
+#' getInitial(y ~ SSpower3P(x,a,b,c), data = xy)
+#' ## Initial values are in fact the converged values
+#'
+#' fitpower3P <- nls(y~SSpower3P(x,a,b,c), data=xy)
+#' summary(fitpower3P)
+#'
+#' @author Weiping Mei \email{[email protected]}
+#' @seealso  \code{\link{trendline}}, \code{\link{SSexp3P}}, \code{\link{SSpower3P}}, \code{\link[stats]{nls}}, \code{\link[stats]{selfStart}}
+
+# selfStart method for power3P model (formula as y = a *x^b+ c)
+SSpower3P<-selfStart(
+  function(predictor,a,b,c){a*predictor^b+c},
+  function(mCall,LHS, data)
+    {
+      xy <- sortedXyData(mCall[["predictor"]],LHS, data)
+      y=xy[,"y"]
+      x=xy[,"x"]
+
+    if (min(x)>0){
+
+      adjy=y-min(y)+1
+      xadjy=data.frame(x,adjy)
+
+      lmFit <- lm(log(adjy) ~ log(x)) # both x and adjy values should be greater than 0.
+      coefs <- coef(lmFit)
+      get.b <- coefs[2]   #slope
+
+      nlsFit<-nls(adjy~cbind(1+x^b,x^b),
+                  start = list(b=get.b),data = xadjy,algorithm = "plinear",
+                  nls.control(maxiter = 5000000,minFactor = 10^(-10)))
+
+      coef<-coef(nlsFit)
+      b<-coef[1]
+      c<-coef[2]+min(y)-1
+      a<-coef[3]+coef[2]
+
+      value <- c(a,b,c)
+      names(value) <- mCall[c("a","b","c")]
+      value
+
+      }else{stop("
+>>Try to use other selfStart functions.
+Because the 'SSpower3P' function need ALL x values greater than 0.")
+        }
+    },c("a","b","c"))
+
+    # getInitial(y~SSpower3P(x,a,b,c),data = xy)