graphResCov.fun: Perform lurking variable plots for a set of variables

Description

This function performs lurking variable plots for a set of variables. The function graphResX.fun performs the lurking variable plot for one variable and graphResCov.fun calls this function for a set of variables; see graphResX.fun for details.

Usage

graphResCov.fun(Xvar, nint, mlePP, h = NULL, typeRes = "Pearson", namX = NULL, 
 histWgraph=TRUE, plotDisp=c(2,2), tit = "")

Value

A list with elements

mXres: Matrix of residuals (each column contains the residuals of a variable).
mXm: Matrix of mean values (each column contains the mean values of a variable in each interval).
mXpc: Matrix of the quantiles that define the intervals of each variable (each column contains the quantiles of one variable).
nint: Input argument.
mlePP: Input argument.

Arguments

Xvar: Matrix of variables (each column is a variable).
nint: Number of intervals each covariate is divided into to perform the lurking variable plot.
mlePP: An object of class mlePP-class; usually, the output from fitPP.fun.
typeRes: Label indicating the type of residuals ("Raw" or any type of scaled residuals such as "Pearson") used in the plots.
h: Optional. Weight function used to calculate the scaled residuals (if typeRes is not equal to "Raw"). By default, Pearson residuals with \(h(t)=1/\sqrt{\hat \lambda(t)}\) are calculated. \(\hat \lambda(t)\) is provided by element lambdafit in mlePP.
namX: Optional. Vector of the names of the variables in Xvar.
histWgraph: Logical flag. If it is TRUE, a new graphical device is opened with the option record=TRUE, so that the history of all plots is recorded in the new device. This option may not work on some platforms; for example, RStudio does not allow the user to open new graphical devices.
plotDisp: A vector of the form c(nr, nc). The lurking variable plots will be drawn in a nr\(\times\)nc array. It is used as argument mfrow in par. By default, a 2 \(\times\) 2 window is used.
tit: Character string. A title for the plot.

References

Atkinson, A. (1985). Plots, transformations and regression. Oxford University Press.

Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B 67,617-666.

Cebrian, A.C., Abaurrea, J. and Asin, J. (2015). NHPoisson: An R Package for Fitting and Validating Nonhomogeneous Poisson Processes. Journal of Statistical Software, 64(6), 1-24.

Examples

Run this code




#Simulated process without any relationship with variables Y1 and Y2
#The plots are performed dividing the  variables into  50 intervals
#Raw residuals. 

X1<-rnorm(500)
X2<-rnorm(500)
auxmlePP<-fitPP.fun(posE=round(runif(50,1,500)), inddat=rep(1,500),
	covariates=cbind(X1,X2),start=list(b0=1,b1=0,b2=0))

Y1<-rnorm(500)
Y2<-rnorm(500)
res<-graphResCov.fun(mlePP=auxmlePP, Xvar=cbind(Y1,Y2), nint=50,  
	typeRes="Raw",namX=c("Y1","Y2"),plotDisp=c(2,1))

#If more variables were specified in the argument Xvar, with
#the same 2X1 layout specified in plotDisp, the resulting plots could be 
#scrolled up and down with the "Page Up" and "Page Down" keys.