Dear R users,
I want to conduct a small simulation study and I have to use the lme
function in a loop to save the restricted log likelihood.
However, for one simulated data set the lme function gives this error
Error en lme.formula(yboot ~ X[, -1], data = data.fr, random = Z.block) :
nlminb problem, convergence error code = 1
message = singular convergence (7)
and then, the simulation stops. I would like to skip this
simulated data, and then continue with the loop, but I cannot find
the way of doing this. I am using R2.6.2.
Can anybody tell me if I can write a condition to avoid the "problematic" data?
I Have also obtained similar errors
Error en lme.formula(yboot ~ X[, -1], data = data.fr, random = Z.block) :
nlminb problem, convergence error code = 1
message = false convergence (8)
My data can be reproduced as follows
library(splines)
library(nlme)
x<-c(rep(1993:2007,9))
barrio<-factor(c(rep(1:9,rep(15,9))))
xl<-min(x)-0.00001
xr<-max(x)+0.00001
ndx<-4
bdeg<-3
pord<-2
dx <- (xr-xl)/ndx
knots <- seq(xl-bdeg*dx, xr+bdeg*dx, by=dx)
B<-spline.des(knots,x,bdeg+1,0*x)$design
m=ncol(B)
D=diff(diag(m),differences=pord)
P=t(D)%*%D
P.svd=svd(t(D)%*%D)
U=(P.svd$u)[,1:(m-pord)]
d=(P.svd$d)[1:(m-pord)]
Delta=diag(1/sqrt(d))
Z=B%*%U%*%Delta
X=NULL
for(i in 0:(pord-1)){X=cbind(X,x^i)}
### This X and Z matrix are the matrices in the mixed model
representation of a spline
n<-nrow(X)
Id<- factor(rep(1,n))
Z.block<-list(list(Id=pdIdent(~Z-1)),list(barrio=pdIdent(~1)))
Z.block<-unlist(Z.block,recursive=FALSE)
fijos2<-c(-326.9178203,0.1645375 )
ef.spline<-c(-0.2214524, -0.1649163, -0.1649163, 0.8878776, -0.2214524)
set.seed(48)
ef.error<-rnorm(135,0,sqrt(0.03073974))
where
ef.spline are standardized random effects, and errores are the residuals.
My loop looks like this
B<-1000
y.boot<-array(0,c(nrow(Z),B))
log.comp<-array(0,B)
while(i<B){
i<-i+1
spline<-sample(ef.spline,size=ncol(Z),replace=T)
errores<-sample(ef.error,size=nrow(Z),replace=T)
y.boot[,i]<-X%*%fijos2+Z%*%spline+errores
yboot<-y.boot[,i]
data.fr <- groupedData( yboot ~ X[,-1] |Id ,data =
data.frame(yboot,X,Z,barrio))
log.comp[i]<-lme(yboot~X[,-1],data=data.fr,random=Z.block)$logLik
}
The idea is to add a condition in the loop such that
if(condition......) {i<-i-1}
and then substitute the "problematic" data by a new one,
Many thanks,
Tomas Goicoa
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