ClassErrorbar: ClassErrorbar

In DataVisualizations: Visualizations of High-Dimensional Data

ClassErrorbar

Description

Plots ClassErrorbars at Xvalue positions for one or more than one classes with user means and defined whiskers

Usage

ClassErrorbar(Xvalues, Ymatrix, Cls, ClassNames, ClassCols, ClassShape, 

MeanFun = median, SDfun, JitterPosition = 0.5,

main = "Error bar plot", xlab, ylab, WhiskerWidth = 7, Whisker_lwd = 1, BW = TRUE)

Arguments

Xvalues	[1:m] Numerical or character vector, positions of error bars (see details) in on x-axis for the m variables
Ymatrix	[1:n,1:d] of n cases and d=m*k variables with for which the error-bar statistics defined by MeanFun and SDfun should be computed
Cls	Optional, [1:d] numerical vector of k classes for the d variables. Each class is one method that will be shown as distinctive set of error bars in the plot
ClassNames	Optional, [1:k] character vector of k methods
ClassCols	Optional, [1:k] character vector of k colors
ClassShape	Optional, [1:k] numerical vector of k shapes, see pch in Classplot for details
MeanFun	Optional, error bar statstic of mean points, default=median
SDfun	Optional, error bar statstic for the length of whiskers, default is the robust estimation of standard deviation
JitterPosition	Optional, how much in values of Xvalues should the error bars jitter around Xvalues to not overlap
main	Optional, title of plot
xlab	Optional, x-axis label
ylab	Optional, y-axis label
WhiskerWidth	Optional, scalar above zero defining the width of the end of the whiskers
Whisker_lwd	Optional, scalar obove zero defining the thickness of the whisker lines
BW	Optional, FALSE: usual ggplot2 background and style which is good for screen visualizations. Default: TRUE: theme_bw() is used which is more appropriate for publications

Details

If k=1, e.g., one method is used, d=m and Cls=rep(1,m). All vector [1:k] assume the occurance of the classes in Cls as ordered with increasing value.

Statistics are provided in long table format with the column names Xvalues, Mean, SD and Method. The method column specifies the names of the k classes.

If Xvalues is a character vector (see example), ggplot2 automatically sets the position on the x-axis. Otherwise specific numeric positions can be set. This allowes also for plotting a smooth line over the average (see example).

Value

List with

ggobj	The ggplot object of the ClassErrorbar
Statistics	[1:(d*k)1:4] data frame of statstics per class used for plotting

Author(s)

Michael Thrun

Examples

data('FundamentalData_Q1_2018')
Data=as.matrix(FundamentalData_Q1_2018$Data)
Cls = FundamentalData_Q1_2018$Cls
Class1Data = matrix(NA, nrow = nrow(Data), ncol = 2)
Class2Data = matrix(NA, nrow = nrow(Data), ncol = 2)
Class1Data[which(Cls==1), ] = Data[which(Cls==1), c("TotalAssets", "TotalLiabilities")]
Class2Data[which(Cls==2), ] = Data[which(Cls==2), c("TotalAssets", "TotalLiabilities")]
YMatrix = cbind(Class1Data, 
                Class2Data)

#Option 1: character vector
ClassErrorbar(c("TotalRevenue","GrossProfit"), 
         YMatrix, c
		 (1,1,2,2), 
         ClassNames=c("Class 1", "Class 2"), 
         main="ClassErrorbar of Q1 2018 for total revenue and gross profit",
         xlab="GrossProfit/TotalRevenue", 
         ylab="Median +- std", 
         WhiskerWidth = 1)
		 
#Option 2: numerical vector
ClassErrorbar(c(1,2), 
		 YMatrix,
		 c(1,1,2,2), 
         ClassNames=c("Class 1", "Class 2"), 
         main="ClassErrorbar of Q1 2018 for total revenue and gross profit",
         xlab="GrossProfit/TotalRevenue", 
         ylab="Median +- std", 
         WhiskerWidth = 1)

#Option 3: numerical vector + line
## Not run: 
#arbitrary data
Y_someOtherData=cbind(YMatrix,YMatrix,
YMatrix,YMatrix)
some_values=c(2,3,4,5,6,8,9,10)
ClassErrorbar(some_values, 
		 Y_someOtherData,
		 c(1,1,2,2), 
         ClassNames=c("Class 1", "Class 2"), 
         main="ClassErrorbar of Q1 2018 for total revenue and gross profit",
         xlab="GrossProfit/TotalRevenue", 
         ylab="Median +- std", 
         WhiskerWidth = 1)$ggobj+
geom_smooth(method="auto", se=F, fullrange=F, level=0.95)

## End(Not run)

DataVisualizations documentation built on Oct. 10, 2023, 9:06 a.m.