Description Usage Arguments Value References See Also Examples
View source: R/graphResCov.fun.r
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.
1 2  graphResCov.fun(Xvar, nint, mlePP, h = NULL, typeRes = "Pearson", namX = NULL,
histWgraph=TRUE, plotDisp=c(2,2), tit = "")

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 
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/√{\hat λ(t)} are calculated. \hat λ(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 
plotDisp 
A vector of the form 
tit 
Character string. A title for the plot. 
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. 
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 67,617666.
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), 124.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  #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.

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