Comments for Christopher David Desjardins's Blog

Comment on About Me by Desjardins

Desjardins — Sun, 27 Nov 2011 04:16:03 +0000

Hi,
Your project sounds interesting. I’ll post soon about both my work with zero-inflated and overdispersed models and penalized regression.

Comment on About Me by Desjardins

Desjardins — Sun, 27 Nov 2011 04:14:13 +0000

desja004 AT umn DOT edu

I am not extremely knowledgeable about profile analysis but I’d be happy to help or add any thing that you think might be interesting (or if you found a bug).

Comment on About Me by Xiaoping Feng

Xiaoping Feng — Sun, 27 Nov 2011 03:40:26 +0000

Hello, I’m a PhD at UW-Madison, and now dealing with a project that needs the idea of Zero-inflation Poisson with lasso penalty. Hope to see your wonderful work soon!

Comment on About Me by Lara

Lara — Sat, 26 Nov 2011 21:54:16 +0000

Hi Chris,
Do you have an email I could talk with you through? I have a couple of stupid questions about your profile analysis R program.

Comment on About Me by Desjardins

Desjardins — Fri, 18 Nov 2011 14:03:07 +0000

Hi Oscar,
Have you solved this problem? I have been away for a while. If you haven’t please let me know. Otherwise, R-Sig-Mixed-Models mailing list is a great place to post questions about MCMCglmm as the author hangs around there and answers questions. Best.
Chris

https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

Comment on About Me by Desjardins

Desjardins — Fri, 18 Nov 2011 14:01:08 +0000

This could be caused by a different version of Wine. What version of Wine are you using? Also, may I suggest you consider either OpenBUGS or JAGS? Both of which are freely available for Linux?

Comment on About Me by Desjardins

Desjardins — Fri, 18 Nov 2011 13:59:44 +0000

Hi,
Have you tried the CourseNotes vignette that comes with MCMCglmm? They are really good and worth examining.

Comment on About Me by Oscar Inostroza

Oscar Inostroza — Wed, 28 Sep 2011 13:43:03 +0000

Dear Christopher Desjardins:

I come to you because in a material you posted to your blog (
Bayesian, MCMC, and Multilevel Modeling (a foray Into the Subjective)) at the end you say: “If you need help with syntax Please ask.”

I explain my situation:
I’m working on the evolution of the social structure of a particular insect. I want to evaluate whether the colonial size(independent variable, continous) of the species predicts the social structure(variable discrete with 4 states) in such insects. for this I fit a GLMM in the program MCMCglmm of R, since my response variable is discrete with 4 states. the problem lies is not good as well adjusted prior to model.

the model I’m trying to implement is the following:

model <- MCMCglmm (EstructuraSOC ~ Colonysize -1,
random = ~ animal
rcov = ~ us (trait): units,
family = "categorical"
Prior = list (R = list (fix = 1, V = 0.25 * (I + J), n = 3), G = list (G1 = list (V = diag (4), n = 4), G2 = list (V = diag (4), n = 4)))
data = Data,
pedigree= Apistree ,
nitt = 600000,
burnin = 100000,
thin = 100)

with I = J = 3 (since they are 4 categories). to implement this model throws me the following error:

V is the wrong dimension for Some priorG / priorR elements.

I have little experience in the use of R or less of MCMCglmm, so if you can help me in the structure of the model, as in the prior adjustment would be very nice.

Comment on About Me by Anna

Anna — Wed, 14 Sep 2011 14:45:28 +0000

Hello Christopher,
I managed to install Winbugs following you instruction,
but I get the annoying black box trap #101 error.
after typing
wine ~/.wine/drive_c/Program Files/WinBUGS14/WinBUGS14.exe
What I have missed? Thank you
Anna

Comment on About Me by nita yalina

nita yalina — Tue, 21 Jun 2011 08:40:38 +0000

Christopher David Desjardins, i’m a student who study technology management, i want to measure the technology acceptance which my company’s develop but i have a problem with the number of sample, i only have 43 sample so i have been suggested to use bayesian sem, but i don’t understand much about statistic. i’m a newbie in bayesian sem, i try to write a code base on yours but i have a problem with my winbugs syntax…could you help me? which one that i used fix? i will very grateful for your help…

model{
for(i in 1:N){
#model persamaan pengukuran

for(j in 1:P){
y[i,j]~dnorm(mu[i,j],psi [j]) I(thd [j,z[i,j]],thd[j,z[i,j]+1])
ephat[i,j]<-y[i,j] -mu[i,j]

}

#faktor Budaya Organisasi
mu[i,1]<-xi[i,1]
mu[i,2]<-lam[1]*xi[i,1]
mu[i,3]<-lam[2]*xi[i,1]

#faktor Kemampuan Pengguna
mu[i,4]<-xi[i,2]
mu[i,5]<-lam[3]*xi[i,2]
mu[i,6]<-lam[4]*xi[i,2]

#faktor Mekanisme Dukungan
mu[i,7]<-xi[i,3]
mu[i,8]<-lam[5]*xi[i,3]
mu[i,9]<-lam[6]*xi[i,3]

#faktor Desain Antarmuka
mu[i,10]<-xi[i,4]
mu[i,11]<-lam[7]*xi[i,4]
mu[i,12]<-lam[8]*xi[i,4]

#faktor Persepsi Kualitas
mu[i,13]<-xi[i,5]
mu[i,14]<-lam[9]*xi[i,5]
mu[i,15]<-lam[10]*xi[i,5]

#faktor Persepsi Kemudahan Kegunaan
mu[i,16]<-eta[i,1]
mu[i,17]<-lam[11]*eta[i,1]
mu[i,18]<-lam[12]*eta[i,1]

#faktor Persepsi Kegunaan
mu[i,19]<-eta[i,2]
mu[i,20]<-lam[13]*eta[i,2]
mu[i,21]<-lam[14]*eta[i,2]
mu[i,22]<-lam[15]*eta[i,2]

#faktor Sikap ke arah Penggunaan
mu[i,23]<-eta[i,3]
mu[i,24]<-lam[16]*eta[i,3]
mu[i,25]<-lam[17]*eta[i,3]

#faktor Persepsi Niat untuk Menggunakan
mu[i,26]<-eta[i,4]
mu[i,27]<-lam[18]*eta[i,4]
mu[i,28]<-lam[19]*eta[i,4]

#faktor Adopsi E-government
mu[i,29]<-eta[i,5]
mu[i,30]<-lam[20]*eta[i,5]

#model persamaan struktural
xi[i,1:5] ~dmnorm(u[1:5],phi[1:5,1:5])

eta[i,1]~dnorm(nu[i,1],pskp)
nu[i,1]<-gam[1]*xi[i,2]+gam[2]*xi[i,3]+gam[3]*xi[i,4]
dthat[i,1]<-eta[i,1]-nu[i,1]

eta[i,2]~dnorm(nu[i,2],pspk)
nu[i,2]<-gam[4]*xi[i,1]+beta[1]*eta[i,1]
dthat[i,2]<-eta[i,2]-nu[i,2]

eta[i,3]~dnorm(nu[i,3],pssp)
nu[i,3]<-beta[2]*eta[i,2]+beta[3]*eta[i,3]
dthat[i,3]<-eta[i,3]-nu[i,3]

eta[i,4]~dnorm(nu[i,4],psnm)
nu[i,4]<-beta[4]*eta[i,1]+beta[5]*eta[i,2]+gam[5]*xi[i,5]
dthat[i,4]<-eta[i,4]-nu[i,4]

eta[i,5]~dnorm(nu[i,5],psae)
nu[i,5]<-beta[6]*eta[i,4]
dthat[i,5]<-eta[i,5]-nu[i,5]
}#akhir dari i

for (i in 1:5) {u[i]<-0.0}

#lamda
var.lam[1]<-8.0*psi[2] var.lam[2]<-8.0*psi[3]

var.lam[3]<-8.0*psi[5] var.lam[4]<-8.0*psi[6]

var.lam[5]<-8.0*psi[8] var.lam[6]<-8.0*psi[9]

var.lam[7]<-8.0*psi[11] var.lam[8]<-8.0*psi[12]

var.lam[9]<-8.0*psi[14] var.lam[10]<-8.0*psi[15]

var.lam[11]<-8.0*psi[17] var.lam[12]<-8.0*psi[18] var.lam[13]<-8.0*psi[20]

var.lam[14]<-8.0*psi[21] var.lam[15]<-8.0*psi[22]

var.lam[16]<-8.0*psi[24] var.lam[17]<-8.0*psi[25]

var.lam[18]<-8.0*psi[27] var.lam[19]<-8.0*psi[28]
var.lam[20]<-8.0*psi[30]

for (i in 1:20) {lam[i] ~dnorm(1,var.lam[i])}
for (j in 1:P) {
psi[j] ~dgamma(10,8)
sgl[j]<-1/psi[j]
}

#gamma
gam[1]~dnorm(0.4,var.pk)
gam[2]~dnorm(0.4,var.kp)
gam[3]~dnorm(0.4,var.kp)
gam[4]~dnorm(0.4,var.kp)
gam[5]~dnorm(0.4,var.nm)

var.pk <-8.0*pspk pspk~dgamma(10,8) sgpk<-1/pspk
var.kp <-8.0*pskp pskp~dgamma(10,8) sgkp<-1/pskp
var.sp <-8.0*pssp pssp~dgamma(10,8) sgsp<-1/pssp
var.nm <-8.0*psnm psnm~dgamma(10,8) sgnm<-1/psnm
var.ae <-8.0*psae psae~dgamma(10,8) sgae<-1/psae

#beta
beta[1] ~dnorm(0.5,var.pk)
beta[2] ~dnorm(0.5,var.sp)
beta[3] ~dnorm(0.5,var.sp)
beta[4] ~dnorm(0.5,var.nm)
beta[5] ~dnorm(0.5,var.nm)
beta[6] ~dnorm(0.5,var.ae)

phi[1:5,1:5] ~dwish(R[1:5,1:5],21)
phx[1:5,1:5]<-inverse(phi[1:5,1:5])

}
#end of model

DATA
list(N=43, P=30,
R=structure(
.Data=c(10,0,0,0,0,
0,10,0,0,0,
0,0,10,0,0,
0,0,0,10,0,
0,0,0,0,10
),
.Dim=c(5,5)),
thd=structure(
.Data=c(-250,-1.99072046156042,-1.08241139430414,0.983052916910141,250,-250,-200,-1.47752529199845,0.452147411138078,250,-250,-200,-1.08241139430414,0.730448177619092,250,-250,-1.99072046156042,-0.585607161227169,1.08241139430414,250,-250,-1.99072046156042,-0.265404753825216,1.08241139430414,250,-250,-200,-0.656304990872144,0.892559673266593,250,-250,-200,-1.08241139430414,0.517723552818072,250,-250,-1.99072046156042,-1.47752529199845,0.585607161227169,250,-250,-200,-1.99072046156042,0.265404753825216,250,-250,-200,-1.67966118528897,0.656304990872144,250,-250,-1.99072046156042,-1.67966118528897,0.517723552818072,250,-250,-1.67966118528897,-0.892559673266593,0.730448177619092,250,-250,-200,-1.08241139430414,0.80884440410662,250,-250,-200,-1.67966118528897,0.730448177619092,250,-250,-1.67966118528897,-1.19379507272265,0.80884440410662,250,-250,-200,-1.32236537894944,0.98305291691014,250,-250,-200,-0.656304990872144,1.08241139430414,250,-250,-1.99072046156042,-1.32236537894944,0.585607161227169,250,-250,-200,-1.19379507272265,0.656304990872144,250,-250,-1.99072046156042,-1.67966118528897,0.80884440410662,250,-250,-1.99072046156042,-1.19379507272265,0.656304990872144,250,-250,-1.67966118528897,-1.19379507272265,0.452147411138078,250,-250,-200,-1.47752529199845,0.656304990872144,250,-250,-1.99072046156042,-1.47752529199845,0.585607161227169,250,-250,-200,-1.19379507272265,0.730448177619092,250,-250,-1.67966118528897,-0.80884440410662,0.892559673266593,250,-250,-1.99072046156042,-1.67966118528897,0.585607161227169,250,-250,-200,-0.983052916910141,0.80884440410662,250,-250,-200,-1.67966118528897,0.585607161227169,250,-250,-1.99072046156042,-1.08241139430414,0.892559673266593,250),
.Dim=c(30,5)),
z=structure(
.Data=c(3,3,2,3,2,2,4,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,2,2,3,3,3,3,3,2,3,3,3,3,2,3,2,2,2,2,2,2,2,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,2,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,4,3,3,3,3,3,2,3,4,2,4,4,4,4,3,3,3,3,2,2,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,2,2,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,2,2,2,3,3,3,3,3,4,3,2,3,3,3,3,4,3,2,4,3,3,3,3,3,3,3,3,3,2,3,2,2,2,2,3,3,4,3,3,2,3,3,3,3,2,4,3,3,4,4,3,3,2,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,2,3,4,4,2,2,3,3,4,4,4,4,3,3,3,3,3,4,3,3,3,3,3,4,4,4,3,4,4,4,4,3,3,3,3,3,3,4,3,3,3,3,2,3,3,1,4,4,4,2,3,3,3,3,3,3,1,3,3,3,3,2,3,3,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,2,3,2,4,3,2,2,2,2,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,3,3,3,4,4,4,4,3,3,2,4,3,3,3,2,3,4,2,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,3,3,3,3,2,4,4,4,3,3,3,3,3,3,3,3,4,4,3,3,3,3,4,3,1,4,3,4,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,2,3,3,3,3,3,2,3,2,3,2,3,4,3,3,3,3,3,4,4,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,4,3,3,4,3,3,3,3,3,3,2,2,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,3,4,4,3,4,4,4,4,3,3,3,3,3,3,3,3,3,4,4,3,3,3,3,3,3,3,3,3,2,3,4,3,2,3,1,3,3,3,3,2,2,2,3,3,2,3,2,2,2,4,4,4,2,3,2,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,2,2,3,3,2,3,3,2,2,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,3,3,2,2,1,1,2,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,2,2,2,2,2,2,3,2,1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,4,4,3,3,4,4,3,3,4,4,4,3,3,3,4,4,4,3,3,4,4,4,4,4,4,4,4,4,4,3,3,3,2,2,3,4,4,4,3,3,3,4,4,4,3,3,4,3,3,3,3,3,3,3,3,4,3,4,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,2,3,3,3,3,2,3,3,3,3,3,3,3,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,4,3,3,3,3,3,3,4,3,3,3,4,4,4,3,4,3,3,3,3,4,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,4,3,2,3,4,2,3,4,3,2,1,3,4,1,3,2,1,3,2,1,2,2,1,2,2,1,2,3,4,2,2,3,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3,3,2,3,4,4,4,4,4,4,3,3,4,4,4,4,3,3,4,4,4,4,3,3,4,3,4,3
),
.Dim=c(43,30)))
)

INITS
list(
lam=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),
psi=c(1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0),
pspk=1.0, pskp=1.0, pssp=1.0, psnm=1.0, psae=1.0,
gam=c(0,0,0,0,0),
beta=c(0,0,0,0,0,0),
phi=structure(.Data=c(1,0,0,0,0,
0,1,0,0,0,
0,0,1,0,0,
0,0,0,1,0,
0,0,0,0,1
),.Dim=c(5,5)),
xi=structure(.Data=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),
.Dim=c(43,5)),
eta=structure(.Data=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),
.Dim=c(43,5))
)