MSEplot.fn | R Documentation |
function to plot the mean and standard error or standard deviation of multiscale entropy by group
MSEplot.fn(Scale, MSE, Name, responseName = NA, timeUnit = "", byGroup = TRUE,
MSEsd = NA, N = NA, stdError = TRUE, xRange = NA, yRange = NA, las = 2, col = NA,
pch = NA, Position = "topleft", cex.legend = 0.75, main = "")
Scale |
a vector for scale |
MSE |
matrix for entropy if byGroup=FALSE, and otherwise for average entropy value in a group at a scale. In the matrix, the row is for scale and column for individuals or groups. |
Name |
vector of names for groups |
responseName |
name to represent the response to be analyzed, such as 'glucose' |
timeUnit |
the time unit for scale |
byGroup |
If byGroup = TRUE, multiscale entropy is plotted by groups; otherwise, by individuals |
MSEsd |
matrix for standard deviation of entropy value in a group at a scale |
N |
matrix for number of subjects in a group at a scale |
stdError |
if it is true, the length of a vertical bar represent 2*standard error; otherwise, the length of a vertical bar represent 2*standard deviation |
xRange |
range for the x-axis |
yRange |
range for the y-axis |
las |
las for the y-axis |
col |
vector for the colors to indicate groups or individuals |
pch |
vector for the point types to indicate groups or individuals |
Position |
position for the legend |
cex.legend |
cex for the legend |
main |
main title for title() |
function to plot the mean and standard error or standard deviation of multiscale entropy by group
No value returned
Xiaohua Douglas Zhang
Zhang XD, Zhang Z, Wang D. 2018. CGManalyzer: an R package for analyzing continuous glucose monitoring studies. Bioinformatics 34(9): 1609-1611 (DOI: 10.1093/bioinformatics/btx826).
library(CGManalyzer)
package.name <- "CGManalyzer"
source( system.file("SPEC", "SPECexample.R", package = package.name) )
scalesInTime <- Scales*equal.interval
MSE.mat <- read.csv(file=system.file("SPEC", "MSE.csv", package = package.name), row.names=1)
Types <- unique( subjectTypes )
Types <- Types[order(Types)]
nType <-length(Types)
col.vec <- rep(NA, length(subjectTypes) )
for( i in 1:nType ) { col.vec[ subjectTypes == Types[i] ] <- i }
MSEplot.fn(scalesInTime, MSE=t(MSE.mat), Name=Types, responseName="glucose", timeUnit="minute",
byGroup=FALSE, MSEsd=NA, N=NA, stdError=TRUE, xRange=NA, yRange=NA,
pch=rep(1, dim(MSE.mat)[1]),las=2, col=col.vec, Position="topleft",
cex.legend=0.0005, main="A: MSE by individual")
legend("topleft", legend=paste0(Types, "(N=", table( subjectTypes ), ")"),
col=1:nType, cex=1, lty=1, pch=1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.