MCMCglmm Poisson with an offset term and splines
Hi, Yes - the intercept is a parameter so should be included. If you get 13 effects then the data/code you sent us are different from the data/code you are using. The issue is not really anything to do with your (mis)understanding about priors it is simply that you have set priors for k parameters whereas there are in fact not k parameters. Cheers, Jarrod
On 27/09/2017 15:38, dani wrote:
Hello again, Thanks! I was indeed referring to the k in the prior formula. I guess I am still confused whether I should add the intercept to the number of fixed effects, as I have 12 variables as I count as fixed effects without the intercept. Would it be possible to confirm that for me, please? Also, for k=12 in the prior formula, I get this error: Error in MCMCglmm(y ~ age + x2 + x8 + x9 + x3 + l_lfvcspo + x4 + x5 + ?: ? fixed effect mu prior is the wrong dimension I am puzzled about this error. I do not seem to understand how to select the fixed effects. Could someone point me to a source of information where I can learn more about this, please? ?Also, is there a book where I can learn more about prior specification? I think I need to see more examples. Thank you so much. I apologize for this, but I am very confused and I would like to learn more about this! Best, DNM Sent from Outlook <http://aka.ms/weboutlook> ------------------------------------------------------------------------ *From:* Jarrod Hadfield <j.hadfield at ed.ac.uk> *Sent:* Wednesday, September 27, 2017 7:28:17 AM *To:* dani; Ben Bolker *Cc:* Matthew; r-sig-mixed-models at r-project.org *Subject:* Re: [R-sig-ME] MCMCglmm Poisson with an offset term and splines Hi, There are 12 terms in the model not 13 so k=12. The k in the prior specification is completely unrelated to the number of knot points in the spline. Cheers, Jarrod On 27/09/2017 15:16, dani wrote:
Hello Jarrod,
I have attached my code and my file.
Variable age has 3 levels, variables x2, x8, and x9 have two levels
each, and the rest of the variables are continuous.
I re-ran the spl2 function and this time around I obtained one single
fixed smoother (l_lfvcspo)?and one single random smoother
(l_lfvcspn). Last time around I got 8 variables with suffixes from
1-8 for l_lfvcspn and I did not know what to do with those.
Also, as I had 11 fixed effects (corresponding to 11 variables), I
thought it was appropriate to choose k=11. I was not sure whether the
intercept needed to be counted as well as the levels of the
categorical fixed predictors (except for their reference categories).
Because I kept on getting the "mu" error for k=11, I tried k=13
(which includes the intercept and the 2 levels for the 3-level age
variable, which I did not consider before). I am not sure this is the
way to go, I guess I need to read more to be able to model properly
fixed effects in R, but for now I was wondering whether this
consideration of fixed effects?sounds ok in this particular example.
The MCMC model worked properly based on the prior using k=13.
Please see the code below:
*#spl2 function*
library(mgcv)
spl2<-function(formula, data, p=TRUE, dataX=data){
aug<-nrow(data)-nrow(dataX)
? if(aug!=0){
? ? if(aug<0){
? ? ? stop("sorry nrow(dataX) must be less than or equal to nrow(data)")
? ? }else{
augX<-matrix(0, aug, ncol(dataX))
colnames(augX)<-colnames(dataX)
dataX<-rbind(dataX, augX)
? ? }
? }
smooth.spec.object<-interpret.gam(formula)$smooth.spec[[1]]
sm<-smoothCon(smooth.spec.object, data=data,
knots=NULL,absorb.cons=TRUE, dataX=dataX)[[1]]
Sed<-eigen(sm$S[[1]])
? Su<-Sed$vectors
? Sd<-Sed$values
? nonzeros <- which(Sd > sqrt(.Machine$double.eps))
? if(p){
Zn<-sm$X%*%Su[,nonzeros, drop=FALSE]%*%diag(1/sqrt(Sd[nonzeros]))
? }else{
Zn<-sm$X[,-nonzeros, drop=FALSE]
? }
return(Zn[1:(nrow(data)-aug),,drop=FALSE])
}
*$spline terms*
*
*
newdatab1$l_lfvcspo<-spl2(~s(f_lfv_c,k=10), data=newdatab1, p=F)
newdatab1$l_lfvcspn<-spl2(~s(f_lfv_c,k=10), data=newdatab1)
summary(newdatab1$l_lfvcspo)
summary(newdatab1$l_lfvcspn)
summary(newdatab1)
dim(newdatab1)
str(newdatab1)
*#PRIOR*
k<-13 # number of fixed effects
prior<-list(B=list(V=diag(k)*1e4, mu=rep(0,k)),
? ? ? ? ? ? R=list(V=1, nu=0),
G=list(G1=list(V=1, nu=0),
?G2=list(V=1, nu=0),
?G3=list(V=1, nu=0)))
prior$B$mu[k]<-1 # assuming the offset term is last
prior$B$V[k,k]<-1e-4
*# MCMC model*
mcmc <- MCMCglmm(y ~ age+x2+x8+x9+x3+l_lfvcspo+x4+x5+x6+x7+offset,
? ? ? ? ? ? ? ? ?random =~ STUDYID+class+idv(l_lfvcspn),
? ? ? ? ? ? ? ? ?data = newdatab1,
? ? ? ? ? ? ? ? ?family = "poisson", prior=prior)
summary(mcmc)
This model has worked, so I would like to thank you so much for all
your help so far. I guess I just want to make sure I understand how
to model the fixed effects in MCMCglmm and I also would like to make
sure that my model is correct.
I truly appreciate all your invaluable?help!
Best regards,
Dani NM
------------------------------------------------------------------------
*From:* Jarrod Hadfield <j.hadfield at ed.ac.uk>
*Sent:* Tuesday, September 26, 2017 12:01:19 PM
*To:* dani; Ben Bolker
*Cc:* Matthew; r-sig-mixed-models at r-project.org
*Subject:* Re: [R-sig-ME] MCMCglmm Poisson with an offset term and
splines
Hi Dani,
It is still not possible for us to diagnose the problem. You need to
provide code+data that reproduces the error. f_lfv_c does not appear
in newdatab.
Cheers,
Jarrod
On 25/09/2017 18:08, dani wrote:
Hello again, Thank you so much for your prompt response. I apologize for the silly questions, I am a true beginner and I am ashamed of my ignorance. I guess I should explain what I did: I used the spl2 function and obtained the fixed and random factors corresponding to the variable I needed the smoother for (named?f_lfv_c). newdatab$l_lfvcspo<-spl2(~s(f_lfv_c,k=10), data=newdatab, p=F) newdatab$l_lfvcspn<-spl2(~s(f_lfv_c,k=10), data=newdatab) I am not sure how to attach the random effects corresponding to the l_lfvcspn. I get this array of 8 variables and I am really not sure how to include them in the model. Should I get forget about spl2 and simply add the variable f_lfv_c as a fixed term and spl(f_lfv) in the idv random term? Also, it seems to me that I have 11 fixed effects, I am not sure what to do. I am really sorry about all these silly?questions, I really do not understand how these things work, but I would like to know more about this. Best regards! Sent from Outlook <http://aka.ms/weboutlook> ------------------------------------------------------------------------ *From:* Jarrod Hadfield <j.hadfield at ed.ac.uk> *Sent:* Monday, September 25, 2017 8:51:09 AM *To:* dani; Ben Bolker *Cc:* Matthew; r-sig-mixed-models at r-project.org *Subject:* Re: [R-sig-ME] MCMCglmm Poisson with an offset term and splines Hi, The example is not reproducible: l_lfvcspn does not exist. The error is telling you that you don't have 11 fixed effects in the model. Change k to the number of fixed effects in the model. Jarrod On 25/09/2017 16:39, dani wrote:
mc_spl1gna <- MCMCglmm(y ~ age+x2+x8+x9+x3+l_lfvcspo+x4+x5+x6+x7+offset, ? ? ? ? ? ?random =~ STUDYID+class+idv(l_lfvcspn), ? ? ? ? ? ?data ? = newdatab, ? ? ? ? ? ?family = "poisson"
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