nonparametric extension

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: HRM
-Version: 0.8.0
-Date: 2018-02-01
+Version: 0.9.0
+Date: 2018-02-14
 Title: High-Dimensional Repeated Measures
 Authors@R: c(person("Martin Happ", role = c("aut", "cre"),
                       email = "[email protected]"),
@@ -10,7 +10,7 @@ Author: Martin Happ [aut, cre], Harrar W. Solomon [aut], Arne C. Bathke [aut]
 Maintainer: Martin Happ <[email protected]>
 LazyData: true
 Depends: R (>= 3.4.0), MASS, matrixcalc, plyr, ggplot2
-Imports: xtable, reshape2, tcltk
+Imports: xtable, reshape2, tcltk, data.table, pracma
 Suggests: RGtk2 (>= 2.8.0), cairoDevice, RGtk2Extras
 Description: Methods for testing main and interaction effects in possibly
     high-dimensional repeated measures in factorial designs. The observations

NAMESPACE

@@ -1,4 +1,4 @@
-export(hrm.test, hrm.GUI) 
+export(hrm_test, hrm.GUI, hrm_GUI, hrm.test.matrix, hrm.test.dataframe) 
 import(matrixcalc)
 import(MASS)
 import(plyr)
@@ -10,8 +10,10 @@ importFrom("utils", "read.table", "write.table")
 importFrom("xtable", "xtable")
 importFrom("grDevices", "dev.new")
 importFrom("tcltk", "tkchooseDirectory","tkgetOpenFile" , "tclvalue")
+importFrom("data.table", "setDT", "rbindlist", "setnames", "as.data.table")
+importFrom("pracma", "Lcm")
 S3method(print,HRM)
 S3method(summary,HRM)
 S3method(plot,HRM)
-S3method(hrm.test, list)
-S3method(hrm.test, data.frame)
+S3method(hrm_test, list)
+S3method(hrm_test, data.frame)

R/GUI.R

@@ -234,7 +234,7 @@ gui.results <- function(result, factors, dec, sep) {
 #' @description Graphical User Interface (R Package RGtk2 needed) for the Function 'hrm_test': Test for main effects and interaction effects of one or two between-subject factors and one, two or three within-subject factors (at most four factors can be used).
 #' @return The results can be saved as LaTeX Code or as plain text. Additionally a plot of the group profiles an be saved when using one whole- and one subplot factor. 
 #' @keywords export
-hrm.GUI <- function(){
+hrm_GUI <- function(){
 
   # variable to temporarily store the loaded data from the user
   tmp <- NULL
@@ -579,7 +579,8 @@ hrm.GUI <- function(){
           if(alpha > 0 & alpha < 1) { 
             # if the input by the user is fine, then do the caluclation
             tryCatch({
-                object.hrm <-  hrm.test(formula = formula, data = tmp, alpha = alpha, subject = subject )
+
+                object.hrm <-  hrm_test(formula = formula, data = tmp, alpha = alpha, subject = subject )
                 result <- object.hrm$result
 
                 # determin which columns are whole- and subplot factors
@@ -615,12 +616,9 @@ hrm.GUI <- function(){
               tryCatch(responseVariable <- as.character(terms.formula(formula)[[2]]), warning = function(w) "", error = function(e) "" )
               if(is.character(responseVariable)){
                 print("Profiles are being plotted ...")
-                #dev.new() # first graphics device is within RStudio
-                #dev.new() # show plot using new graphics device (i.e. separate window for plot); easer than drawing with cairoDevice
                 GUI_plot()
                 tryCatch({
                   print(plot.HRM(object.hrm))
-                  #print(hrm.plot(data = tmp, group = groupFactor, factor1 = timeFactor, subject = subject, response = responseVariable, xlab = "dimension", ylab = "means"))
                 }, error = function(e) "", warning = function(w)  "")
               }
             } else if(nfactors == 1 & !is.null(timeFactor) & errorOccured == 0) {

R/S3methods.R

@@ -10,13 +10,13 @@
 #' Test for Multi-Factor High-Dimensional Repeated Measures 
 #' 
 #' @description Performing main and interaction effects of up to three whole- or subplot-factors. In total, a maximum of four factors can be used. There are two different S3 methods available. The first method requires a list of matrices in the wide table format. The second methodl requres a data.frame in the long table format.
-#' @return Returns a data frame consisting of the degrees of freedom, the test value, the critical value and the p-value
-#' @rdname hrm.test
+#' @rdname hrm_test
 #' @param data Either a data.frame (one observation per row) or a list with matrices (one subject per row) for all groups containing the data
 #' @param formula A model formula object. The left hand side contains the response variable and the right hand side contains the whole- and subplot factors.
 #' @param subject column name within the data frame X identifying the subjects
-#' @param alpha alpha level used for the test
-#' @param ... Further arguments passed to 'hrm.test'
+#' @param alpha alpha level used for calculating the critical value for the test
+#' @param nonparametric Logical variable indicating wether the noparametric version of the test statistic should be used
+#' @param ... Further arguments passed to 'hrm_test' will be ignored
 #' @return Returns an object from class HRM containing
 #' @return \item{result}{A dataframe with the results from the hypotheses tests.}
 #' @return \item{formula}{The formula object which was used.}
@@ -26,34 +26,37 @@
 #' @return \item{data}{The data.frame or list containing the data.}
 #' @example R/example_1.txt
 #' @keywords export
-hrm.test <- function(data, ...) {
-  UseMethod("hrm.test")
+hrm_test <- function(data, ...) {
+  UseMethod("hrm_test")
 }
 
-#' @method hrm.test default
+#' @method hrm_test default
 #' @keywords export
-hrm.test.default <- function(data) {
-  stop("Your data needs either be a data.frame or a list containing matrices.")
+hrm_test.default <- function(data) {
+  stop("Your data needs either to be a data.frame or a list containing matrices (a matrix for each group).")
 }
 
-#' @rdname hrm.test
-#' @method hrm.test list
+#' @rdname hrm_test
+#' @method hrm_test list
 #' @keywords export
-hrm.test.list <- function(data, alpha = 0.05, ...) {
+hrm_test.list <- function(data, alpha = 0.05, ...) {
   return(hrm.test.matrices(data, alpha))
 }
 
-#' @rdname hrm.test
-#' @method hrm.test data.frame
+#' @rdname hrm_test
+#' @method hrm_test data.frame
 #' @keywords export
-hrm.test.data.frame <- function(data, formula, alpha = 0.05,  subject, ... ) {
-  return(hrm_test(formula=formula,alpha=alpha,subject=subject, data=data ))
+hrm_test.data.frame <- function(data, formula, alpha = 0.05,  subject, nonparametric = FALSE, ... ) { 
+  return(hrm_test_internal(formula=formula, data=data, alpha=alpha,subject=subject, nonparametric ))
 }
 
 
-
 #' @keywords export
 print.HRM <- function(x, ...) {
+  if(x$nonparametric){
+    cat("Nonparametric Repeated Measures Analysis\n")
+    cat("\n")
+  }
   if(!is.null(x$formula)) {
     cat("Call:", "\n")
     print(x$formula)
@@ -62,11 +65,15 @@ print.HRM <- function(x, ...) {
 
   print(x$result, row.names = FALSE)
   cat("\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1")
+  cat("\n")
 }
 
 
 #' @keywords export
 summary.HRM <- function(object, ...) {
+  if(object$nonparametric){
+    cat("Nonparametric Repeated Measures Analysis\n")
+  }
   cat("Summary:\n")
   cat("\n")
   if(!is.null(object$formula)) {
@@ -84,4 +91,5 @@ summary.HRM <- function(object, ...) {
 
   print(object$result, row.names = FALSE)
   cat("\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1")
+  cat("\n")
 }

R/example_1.txt

@@ -1,4 +1,4 @@
-## hrm.test with a list of matrices
+## hrm_test with a list of matrices
 
 # number patients per group
 n = c(10,10)
@@ -16,9 +16,12 @@ sigma_2 = 1.5*sigma_1
 X = list(mvrnorm(n[1],mu_1, sigma_1), mvrnorm(n[2],mu_2, sigma_2))
 X=lapply(X, as.matrix)
 
-hrm.test(data=X, alpha=0.05)
+hrm_test(data=X, alpha=0.05)
 
 
 ## hrm.test with a data.frame using a 'formula' object
 
-hrm.test(value ~ group*region*variable, subject = "subject", data = EEG)
+# using the EEG dataset
+?EEG
+
+hrm_test(value ~ group*region*variable, subject = "subject", data = EEG)

R/example_plot.txt

@@ -3,4 +3,14 @@ head(EEG)
 
 # plots profiles according to groups with
 # subplot-factor called dimension
-plot(hrm.test(value ~ group*dimension, subject = "subject", data = EEG))
+
+# first create an HRM object
+object_hrm <- hrm_test(value ~ group*dimension, subject = "subject", data = EEG)
+
+# plot the HRM object, here we use the additional argument 'theme_bw()' for ggplot2
+plot(object_hrm, legend = TRUE, legend.title = "Group", ... =  theme_bw() )
+
+# same plot without a legend
+# note that 'theme_bw' overwrites the standard legend properties of plot.HRM
+plot(object_hrm, ... =  theme_bw() +
+  theme(legend.title = element_blank(), legend.position="none") )