adding the new ds.kurtosis function

NAMESPACE

@@ -35,6 +35,7 @@ export(ds.heatmapPlot)
 export(ds.histogram)
 export(ds.isNA)
 export(ds.isValid)
+export(ds.kurtosis)
 export(ds.length)
 export(ds.levels)
 export(ds.lexis)

R/ds.kurtosis.R

@@ -0,0 +1,133 @@
+#' 
+#' @title Calculates the kurtosis of a numeric variable
+#' @description This function calculates the kurtosis of a numeric variable.
+#' @details The function calculates the kurtosis of an input variable x with three different methods. 
+#' The method is specified by the argument \code{method}. If x contains any missings, the function removes those before
+#' the calculation of the kurtosis. If \code{method} is set to 1 the following formula is used
+#' \eqn{ kurtosis= \frac{\sum_{i=1}^{N} (x_i - \bar(x))^4 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^2) /N)^(2) } - 3},
+#' where \eqn{ \bar{x} } is the mean of x and \eqn{N} is the number of observations. If \code{method} is set to 2
+#' the following formula is used \eqn{ kurtosis= ((N+1)*(\frac{\sum_{i=1}^{N} (x_i - \bar(x))^4 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^2) /N)^(2) } - 3) + 6)*((N-1)/((N-2)*(N-3)))}.
+#' If \code{method} is set to 3 the following formula is used \eqn{ kurtosis= (\frac{\sum_{i=1}^{N} (x_i - \bar(x))^4 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^2) /N)^(2) })*(1-1/N)^2 - 3}.
+#' This function is similar to the function \code{kurtosis} in R package \code{e1071}.
+#' @param x a string character, the name of a numeric variable.
+#' @param method an integer between 1 and 3 selecting one of the algorithms for computing kurtosis
+#' detailed below. The default value is set to 1.
+#' @param type a character which represents the type of analysis to carry out. 
+#' If \code{type} is set to 'combine', 'combined', 'combines' or 'c', the global kurtosis is returned 
+#' if \code{type} is set to 'split', 'splits' or 's', the kurtosis is returned separately for each study.
+#' if \code{type} is set to 'both' or 'b', both sets of outputs are produced.
+#' The default value is set to 'both'.
+#' @param datasources a list of \code{\link{DSConnection-class}} objects obtained after login. 
+#' If the \code{datasources} argument is not specified 
+#' the default set of connections will be used: see \code{\link{datashield.connections_default}}.
+#' @return a matrix showing the kurtosis of the input numeric variable, the number of valid observations and
+#' the validity message.
+#' @author Demetris Avraam, for DataSHIELD Development Team
+#' @export
+#' 
+ds.kurtosis <- function(x=NULL, method=1, type='both', datasources=NULL){
+
+  # if no opal login details are provided look for 'opal' objects in the environment
+  if(is.null(datasources)){
+    datasources <- datashield.connections_find()
+  }
+
+  if(is.null(x)){
+    stop("Please provide the name of the input vector!", call.=FALSE)
+  }
+
+  if(!(method %in% c(1,2,3))){
+    stop("method must be an integer between 1 and 3", call.=FALSE)
+  }
+
+  # enable valid aliases for "type" argument                  
+  if(type == 'combine' | type == 'combined' | type == 'combines' | type == 'c') type <- 'combine'
+  if(type == 'split' | type == 'splits' | type == 's') type <- 'split'
+  if(type == 'both' | type == 'b' ) type <- 'both'
+
+  # the input variable might be given as column table (i.e. D$x)
+  # or just as a vector not attached to a table (i.e. x)
+  # we have to make sure the function deals with each case
+  xnames <- extract(x)
+  varname <- xnames$elements
+  obj2lookfor <- xnames$holders
+
+  # check if the input object(s) is(are) defined in all the studies
+  if(is.na(obj2lookfor)){
+    defined <- isDefined(datasources, varname)
+  }else{
+    defined <- isDefined(datasources, obj2lookfor)
+  }
+
+  # call the internal function that checks the input object is of the same class in all studies.
+  typ <- checkClass(datasources, x)
+
+  # the input object must be a numeric or an integer vector
+  if(typ != 'integer' & typ != 'numeric'){
+    message(paste0(x, " is of type ", typ, "!"))
+    stop("The input object must be an integer or numeric vector.", call.=FALSE)
+  }
+
+  if (type=='split' | type=='both'){
+    calltext.split <- call("kurtosisDS1", x, method)
+    output.split <- DSI::datashield.aggregate(datasources, calltext.split) 
+    mat.split <- matrix(as.numeric(matrix(unlist(output.split), nrow=length(datasources), byrow=TRUE)[,1:2]),nrow=length(datasources))
+    validity <- matrix(unlist(output.split), nrow=length(datasources), byrow=TRUE)[,3]
+    mat.split <- data.frame(cbind(mat.split, validity))
+    rownames(mat.split) <- names(output.split)
+    colnames(mat.split) <- c('Kurtosis', 'Nvalid', 'ValidityMessage')
+  }
+
+  if (type=='combine' | type=='both'){
+    global.mean <- ds.mean(x, type='combine')$Global.Mean[,'EstimatedMean']
+    #global.var <- ds.var(x, type='combine')$Global.Variance[,'EstimatedVar']
+
+    if (is.na(global.mean)){
+      stop("FAILED: The number of valid observations in one or more studies is less than nfilter.tab. \n Check that by using the argument type=='split'", call.=FALSE)
+    }else{
+      calltext.combined <- call("kurtosisDS2", x, global.mean)
+      output.combined <- DSI::datashield.aggregate(datasources, calltext.combined) 
+
+      Global.sum.quartics <- 0
+      Global.sum.squares <- 0
+      Global.Nvalid <- 0
+      for (s in 1:length(datasources)){
+        Global.sum.quartics <- Global.sum.quartics + output.combined[[s]]$Sum.quartics
+        Global.sum.squares <- Global.sum.squares + output.combined[[s]]$Sum.squares
+        Global.Nvalid <- Global.Nvalid + output.combined[[s]]$Nvalid
+      }
+
+      g2.global <- ((Global.sum.quartics/Global.Nvalid)/(Global.sum.squares/Global.Nvalid)^(2))-3
+
+      if(method==1){
+        Global.kurtosis <- g2.global
+        combinedMessage <- "VALID ANALYSIS"
+      }  
+      if(method==2){
+        Global.kurtosis <- ((Global.Nvalid + 1) * g2.global + 6) * (Global.Nvalid - 1)/((Global.Nvalid - 2) * (Global.Nvalid - 3))
+        combinedMessage <- "VALID ANALYSIS"
+      }  
+      if(method==3){
+        Global.kurtosis <- (g2.global + 3) * (1 - 1/Global.Nvalid)^2 - 3
+        combinedMessage <- "VALID ANALYSIS"
+      } 
+      mat.combined <- data.frame(cbind(Global.kurtosis, Global.Nvalid, combinedMessage))
+      rownames(mat.combined) <- 'studiesCombined'
+      colnames(mat.combined) <- c('Kurtosis', 'Nvalid', 'ValidityMessage')
+
+    }
+  }
+
+  if (type=='split'){
+    return(Kurtosis.by.Study=mat.split)
+  }
+
+  if (type=="combine") {
+    return(Global.Kurtosis=mat.combined)
+  }
+
+  if (type=="both") {
+    return(list(Kurtosis.by.Study=mat.split, Global.Kurtosis=mat.combined))
+  }
+
+}

R/ds.skewness.R

@@ -4,10 +4,10 @@
 #' @details The function calculates the skewness of an input variable x with three different methods. 
 #' The method is specified by the argument \code{method}. If x contains any missings, the function removes those before
 #' the calculation of the skewness. If \code{method} is set to 1 the following formula is used
-#' \eqn{ skewness= \frac{\sum_{i=1}^{N} (x_i - \bar(x))^3 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^3) /N)^(3/2) }},
+#' \eqn{ skewness= \frac{\sum_{i=1}^{N} (x_i - \bar(x))^3 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^2) /N)^(3/2) }},
 #' where \eqn{ \bar{x} } is the mean of x and \eqn{N} is the number of observations. If \code{method} is set to 2
-#' the following formula is used \eqn{ skewness= \frac{\sum_{i=1}^{N} (x_i - \bar(x))^3 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^3) /N)^(3/2) } * \frac{\sqrt(N(N-1)}{n-2}}.
-#' If \code{method} is set to 3 the following formula is used \eqn{ skewness= \frac{\sum_{i=1}^{N} (x_i - \bar(x))^3 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^3) /N)^(3/2) } * (\frac{N-1}{N})^(3/2)}.
+#' the following formula is used \eqn{ skewness= \frac{\sum_{i=1}^{N} (x_i - \bar(x))^3 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^2) /N)^(3/2) } * \frac{\sqrt(N(N-1)}{n-2}}.
+#' If \code{method} is set to 3 the following formula is used \eqn{ skewness= \frac{\sum_{i=1}^{N} (x_i - \bar(x))^3 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^2) /N)^(3/2) } * (\frac{N-1}{N})^(3/2)}.
 #' This function is similar to the function \code{skewness} in R package \code{e1071}.
 #' @param x a string character, the name of a numeric variable.
 #' @param method an integer between 1 and 3 selecting one of the algorithms for computing skewness