[R] random effects model
arun
smartpink111 at yahoo.com
Mon Jan 7 22:28:36 CET 2013
HI,
Just to add:
fit3<-geese(hibp~MaternalAge*time,id=CODEA,data=BP.stack5,family=binomial,corstr="exch",scale.fix=TRUE) #works
summary(fit3)$mean["p"]
# p
#(Intercept) 0.00000000
#MaternalAge4 0.49099242
#MaternalAge5 0.04686295
#time21 0.86164351
#MaternalAge4:time21 0.59258221
#MaternalAge5:time21 0.79909832
fit4<-geese(hibp~MaternalAge*time,id=CODEA,data=BP.stack5,family=binomial,corstr="unstructured",scale.fix=TRUE) #when the correlation structure is changed to "unstructured"
#Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
# contrasts can be applied only to factors with 2 or more levels
#In addition: Warning message:
#In is.na(rows) : is.na() applied to non-(list or vector) of type 'NULL'
Though, it works with data(Ohio)
fit1<-geese(resp~age+smoke+age:smoke,id=id,data=ohio1,family=binomial,corstr="unstructured",scale.fix=TRUE)
summary(fit1)$mean["p"]
# p
#(Intercept) 0.00000000
#age-1 0.60555454
#age0 0.45322698
#age1 0.01187725
#smoke1 0.86262269
#age-1:smoke1 0.17239050
#age0:smoke1 0.32223942
#age1:smoke1 0.36686706
By checking:
with(BP.stack5,table(MaternalAge,time))
# time
#MaternalAge 14 21
# 3 1104 864
# 4 875 667
# 5 67 53 #less number of observations
BP.stack6 <- BP.stack5[order(BP.stack5$CODEA, BP.stack5$time),]
head(BP.stack6) # very few IDs with MaternalAge==5
# X CODEA Sex MaternalAge Education Birthplace AggScore IntScore
#1493 3.1 3 2 3 3 1 0 0
#3202 3.2 3 2 3 3 1 0 0
#1306 7.1 7 2 4 6 1 0 0
#3064 7.2 7 2 4 6 1 0 0
#1 8.1 8 2 4 4 1 0 0
#2047 8.2 8 2 4 4 1 0 0
# Categ time Obese Overweight hibp
#1493 Overweight 14 0 0 0
#3202 Overweight 21 0 1 0
#1306 Obese 14 0 0 0
#3064 Obese 21 1 1 0
#1 Normal 14 0 0 0
#2047 Normal 21 0 0 0
BP.stack7<-BP.stack6[BP.stack6$MaternalAge!=5,]
BP.stack7$MaternalAge<-factor(as.numeric(as.character(BP.stack7$MaternalAge)
fit5<-geese(hibp~MaternalAge*time,id=CODEA,data=BP.stack7,family=binomial,corstr="unstructured",scale.fix=TRUE)
#Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
# contrasts can be applied only to factors with 2 or more levels
with(BP.stack7,table(MaternalAge,time)) #It looks like the combinations are still there
# time
#MaternalAge 14 21
# 3 1104 864
# 4 875 667
It works also with corstr="ar1". Why do you gave the option "unstructured"?
A.K.
----- Original Message -----
From: rex2013 <usha.nathan at gmail.com>
To: r-help at r-project.org
Cc:
Sent: Monday, January 7, 2013 6:15 AM
Subject: Re: [R] random effects model
Hi A.K
Below is the comment I get, not sure why.
BP.sub3 is the stacked data without the missing values.
BP.geese3 <- geese(HiBP~time*MaternalAge,data=BP.sub3,id=CODEA,
family=binomial, corstr="unstructured", na.action=na.omit)Error in
`contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels
Even though age has 3 levels; time has 14 years & 21 years; HIBP is a
binary response outcome.
2) When you mentioned summary(m1)$mean["p"] what did the p mean? i
used this in one of the gee command, it produced NA as answer?
Many thanks
On Mon, Jan 7, 2013 at 5:26 AM, arun kirshna [via R] <
ml-node+s789695n4654795h72 at n4.nabble.com> wrote:
> Hi,
>
> I am not very familiar with the geese/geeglm(). Is it from
> library(geepack)?
> Regarding your question:
> "
> Can you tell me if I can use the geese or geeglm function with this data
> eg: : HIBP~ time* Age
> Here age is a factor with 3 levels, time: 2 levels, HIBP = yes/no.
>
> From your original data:
> BP_2b<-read.csv("BP_2b.csv",sep="\t")
> head(BP_2b,2)
> # CODEA Sex MaternalAge Education Birthplace AggScore IntScore Obese14
> #1 1 NA 3 4 1 NA NA NA
> #2 3 2 3 3 1 0 0 0
> # Overweight14 Overweight21 Obese21 hibp14 hibp21
> #1 NA NA NA NA NA
> #2 0 1 0 0 0
>
> If I understand your new classification:
> BP.stacknormal<- subset(BP_2b,Obese14==0 & Overweight14==0 & Obese21==0 &
> Overweight21==0)
> BP.stackObese <- subset(BP_2b,(Obese14==1& Overweight14==0 &
> Obese14==1&Overweight14==1)|(Obese14==1&Overweight14==1 & Obese21==1 &
> Overweight21==0)|(Obese14==1&Overweight14==0 & Obese21==0 &
> Overweight21==0)|(Obese14==0 & Overweight14==0 & Obese21==1 &
> Overweight21==0)|(Obese14==0 & Overweight14==0 & Obese21==1 &
> Overweight21==1)|(Obese14==0 & Overweight14==1 & Obese21==1
> &Overweight21==1)|(Obese14==1& Overweight14==1 & Obese21==1&
> Overweight21==1)) #check whether there are more classification that fits to
> #Obese
> BP.stackOverweight <- subset(BP_2b,(Obese14==0 & Overweight14==1 &
> Obese21==0 & Overweight21==1)|(Obese14==0 &Overweight14==1 & Obese21==0 &
> Overweight21==0)|(Obese14==0 & Overweight14==0 & Obese21==0 &
> Overweight21==1))
> BP.stacknormal$Categ<-"Normal"
> BP.stackObese$Categ<-"Obese"
> BP.stackOverweight$Categ <- "Overweight"
> BP.newObeseOverweightNormal<-na.omit(rbind(BP.stacknormal,BP.stackObese,BP.stackOverweight))
>
> nrow(BP.newObeseOverweightNormal)
> #[1] 1581
> BP.stack3 <-
> reshape(BP.newObeseOverweightNormal,idvar="CODEA",timevar="time",sep="_",varying=list(c("Obese14","Obese21"),c("Overweight14","Overweight21"),c("hibp14","hibp21")),v.names=c("Obese","Overweight","hibp"),direction="long")
>
> library(car)
> BP.stack3$time<-recode(BP.stack3$time,"1=14;2=21")
> head(BP.stack3,2)
> # CODEA Sex MaternalAge Education Birthplace AggScore IntScore Categ
> time
> #8.1 8 2 4 4 1 0 0 Normal
> 14
> #9.1 9 1 3 6 2 0 0 Normal
> 14
> # Obese Overweight hibp
> #8.1 0 0 0
>
> Now, your formula: (HIBP~time*Age), is it MaternalAge?
> If it is, it has three values
> unique(BP.stack3$MaternalAge)
> #[1] 4 3 5
> and for time (14,21) # If it says that geese/geeglm, contrasts could be
> applied with factors>=2 levels, what is the problem?
> If you take "Categ" variable, it also has 3 levels (Normal, Obese,
> Overweight).
>
> BP.stack3$MaternalAge<-factor(BP.stack3$MaternalAge)
> BP.stack3$time<-factor(BP.stack3$time)
>
> library(geepack)
> For your last question about how to get the p-values:
> # Using one of the example datasets:
> data(seizure)
> seiz.l <- reshape(seizure,
> varying=list(c("base","y1", "y2", "y3", "y4")),
> v.names="y", times=0:4, direction="long")
> seiz.l <- seiz.l[order(seiz.l$id, seiz.l$time),]
> seiz.l$t <- ifelse(seiz.l$time == 0, 8, 2)
> seiz.l$x <- ifelse(seiz.l$time == 0, 0, 1)
> m1 <- geese(y ~ offset(log(t)) + x + trt + x:trt, id = id,
> data=seiz.l, corstr="exch", family=poisson)
> summary(m1)
>
> summary(m1)$mean["p"]
> # p
> #(Intercept) 0.0000000
> #x 0.3347040
> #trt 0.9011982
> #x:trt 0.6236769
>
>
> #If you need the p-values of the scale
> summary(m1)$scale["p"]
> # p
> #(Intercept) 0.0254634
>
> Hope it helps.
>
> A.K.
>
>
>
>
>
>
> ----- Original Message -----
> From: rex2013 <[hidden email]<http://user/SendEmail.jtp?type=node&node=4654795&i=0>>
>
> To: [hidden email] <http://user/SendEmail.jtp?type=node&node=4654795&i=1>
> Cc:
> Sent: Sunday, January 6, 2013 4:55 AM
> Subject: Re: [R] random effects model
>
> Hi A.K
>
> Regarding my question on comparing normal/ obese/overweight with blood
> pressure change, I did finally as per the first suggestion of stacking the
> data and creating a normal category . This only gives me a obese not obese
> 14, but when I did with the wide format hoping to get a
> obese14,normal14,overweight 14 Vs hibp 21, i could not complete any of the
> models.
> This time I classified obese=1 & overweight=1 as obese itself.
>
> Can you tell me if I can use the geese or geeglm function with this data
> eg: : HIBP~ time* Age
> Here age is a factor with 3 levels, time: 2 levels, HIBP = yes/no.
>
> It says geese/geeglm: contrast can be applied only with factor with 2 or
> more levels. What is the way to overcome this. Can I manipulate the data
> to
> make it work.
>
> I need to know if the demogrphic variables affect change in blood pressure
> status over time?
>
> How to get the p values with gee model?
>
> Thanks
> On Thu, Jan 3, 2013 at 5:06 AM, arun kirshna [via R] <
> [hidden email] <http://user/SendEmail.jtp?type=node&node=4654795&i=2>>
> wrote:
>
> > HI Rex,
> > If I take a small subset from your whole dataset, and go through your
> > codes:
> > BP_2b<-read.csv("BP_2b.csv",sep="\t")
> > BP.sub<-BP_2b[410:418,c(1,8:11,13)] #deleted the columns that are not
> > needed
> > BP.stacknormal<- subset(BP.subnew,Obese14==0 & Overweight14==0)
> > BP.stackObese <- subset(BP.subnew,Obese14==1)
> > BP.stackOverweight <- subset(BP.subnew,Overweight14==1)
> > BP.stacknormal$Categ<-"Normal14"
> > BP.stackObese$Categ<-"Obese14"
> > BP.stackOverweight$Categ <- "Overweight14"
> >
> BP.newObeseOverweightNormal<-rbind(BP.stacknormal,BP.stackObese,BP.stackOverweight)
>
> >
> > BP.newObeseOverweightNormal
> > # CODEA Obese14 Overweight14 Overweight21 Obese21 hibp21 Categ
> > #411 541 0 0 0 0 0 Normal14
> > #415 545 0 0 1 1 1 Normal14
> > #418 549 0 0 1 0 0 Normal14
> > #413 543 1 0 1 1 0 Obese14
> > #417 548 0 1 1 0 0 Overweight14
> > BP.newObeseOverweightNormal$Categ<-
> > factor(BP.newObeseOverweightNormal$Categ)
> > BP.stack3 <-
> >
> reshape(BP.newObeseOverweightNormal,idvar="CODEA",timevar="time",sep="_",varying=list(c("Obese14","Obese21"),c("Overweight14","Overweight21")),v.names=c("Obese","Overweight"),direction="long")
>
> >
> > library(car)
> > BP.stack3$time<-recode(BP.stack3$time,"1=14;2=21")
> > BP.stack3 #Here Normal14 gets repeated even at time==21. Given that you
> > are using the "Categ" and "time" #columns in the analysis, it will give
> > incorrect results.
> > # CODEA hibp21 Categ time Obese Overweight
> > #541.1 541 0 Normal14 14 0 0
> > #545.1 545 1 Normal14 14 0 0
> > #549.1 549 0 Normal14 14 0 0
> > #543.1 543 0 Obese14 14 1 0
> > #548.1 548 0 Overweight14 14 0 1
> > #541.2 541 0 Normal14 21 0 0
> > #545.2 545 1 Normal14 21 1 1
> > #549.2 549 0 Normal14 21 0 1
> > #543.2 543 0 Obese14 21 1 1
> > #548.2 548 0 Overweight14 21 0 1
> > #Even if I correct the above codes, this will give incorrect
> > results/(error as you shown) because the response variable (hibp21) gets
> > #repeated when you reshape it from wide to long.
> >
> > The correct classification might be:
> > BP_2b<-read.csv("BP_2b.csv",sep="\t")
> > BP.sub<-BP_2b[410:418,c(1,8:11,13)]
> >
> BP.subnew<-reshape(BP.sub,idvar="CODEA",timevar="time",sep="",varying=list(c("Obese14","Obese21"),c("Overweight14","Overweight21")),v.names=c("Obese","Overweight"),direction="long")
>
> >
> > BP.subnew$time<-recode(BP.subnew$time,"1=14;2=21")
> > BP.subnew<-na.omit(BP.subnew)
> >
> > BP.subnew$Categ[BP.subnew$Overweight==1 & BP.subnew$time==14 &
> > BP.subnew$Obese==0]<-"Overweight14"
> > BP.subnew$Categ[BP.subnew$Overweight==1 & BP.subnew$time==21 &
> > BP.subnew$Obese==0]<-"Overweight21"
> > BP.subnew$Categ[BP.subnew$Obese==1 & BP.subnew$time==14 &
> > BP.subnew$Overweight==0]<-"Obese14"
> > BP.subnew$Categ[BP.subnew$Obese==1 & BP.subnew$time==21 &
> > BP.subnew$Overweight==0]<-"Obese21"
> > BP.subnew$Categ[BP.subnew$Overweight==1 & BP.subnew$time==21&
> > BP.subnew$Obese==1]<-"ObeseOverweight21"
> > BP.subnew$Categ[BP.subnew$Overweight==1 & BP.subnew$time==14&
> > BP.subnew$Obese==1]<-"ObeseOverweight14"
> > BP.subnew$Categ[BP.subnew$Overweight==0 & BP.subnew$Obese==0
> > &BP.subnew$time==14]<-"Normal14"
> > BP.subnew$Categ[BP.subnew$Overweight==0 & BP.subnew$Obese==0
> > &BP.subnew$time==21]<-"Normal21"
> >
> > BP.subnew$Categ<-factor(BP.subnew$Categ)
> > BP.subnew$time<-factor(BP.subnew$time)
> > BP.subnew
> > # CODEA hibp21 time Obese Overweight Categ
> > #541.1 541 0 14 0 0 Normal14
> > #543.1 543 0 14 1 0 Obese14
> > #545.1 545 1 14 0 0 Normal14
> > #548.1 548 0 14 0 1 Overweight14
> > #549.1 549 0 14 0 0 Normal14
> > #541.2 541 0 21 0 0 Normal21
> > #543.2 543 0 21 1 1 ObeseOverweight21
> > #545.2 545 1 21 1 1 ObeseOverweight21
> > #548.2 548 0 21 0 1 Overweight21
> > #549.2 549 0 21 0 1 Overweight21
> >
> > #NOw with the whole dataset:
> > BP.sub<-BP_2b[,c(1,8:11,13)] #change here and paste the above lines:
> > head(BP.subnew)
> > # CODEA hibp21 time Obese Overweight Categ
> > #3.1 3 0 14 0 0 Normal14
> > #7.1 7 0 14 0 0 Normal14
> > #8.1 8 0 14 0 0 Normal14
> > #9.1 9 0 14 0 0 Normal14
> > #14.1 14 1 14 0 0 Normal14
> > #21.1 21 0 14 0 0 Normal14
> >
> > tail(BP.subnew)
> > # CODEA hibp21 time Obese Overweight Categ
> > #8485.2 8485 0 21 1 1 ObeseOverweight21
> > #8506.2 8506 0 21 0 1 Overweight21
> > #8520.2 8520 0 21 0 0 Normal21
> > #8529.2 8529 1 21 1 1 ObeseOverweight21
> > #8550.2 8550 0 21 1 1 ObeseOverweight21
> > #8554.2 8554 0 21 0 0 Normal21
> >
> > summary(lme.1 <- lme(hibp21~time+Categ+ time*Categ,
> > data=BP.subnew,random=~1|CODEA, na.action=na.omit))
> > #Error in MEEM(object, conLin, control$niterEM) :
> > #Singularity in backsolve at level 0, block 1
> > #May be because of the reasons I mentioned above.
> >
> > #YOu didn't mention the library(gee)
> > BP.gee8 <- gee(hibp21~time+Categ+time*Categ,
> > data=BP.subnew,id=CODEA,family=binomial,
> > corstr="exchangeable",na.action=na.omit)
> > #Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
> > #Error in gee(hibp21 ~ time + Categ + time * Categ, data = BP.subnew, id
> =
> > CODEA, :
> > #rank-deficient model matrix
> > With your codes, it might have worked, but the results may be inaccurate
> > # After running your whole codes:
> > BP.gee8 <- gee(hibp21~time+Categ+time*Categ,
> > data=BP.stack3,id=CODEA,family=binomial,
> > corstr="exchangeable",na.action=na.omit)
> > #Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
> > #running glm to get initial regression estimate
> > # (Intercept) time CategObese14
> > # -2.456607e+01 9.940875e-15 2.087584e-13
> > # CategOverweight14 time:CategObese14 time:CategOverweight14
> > # 2.087584e-13 -9.940875e-15 -9.940875e-15
> > #Error in gee(hibp21 ~ time + Categ + time * Categ, data = BP.stack3, id
> =
> > CODEA, :
> > # Cgee: error: logistic model for probability has fitted value very
> close
> > to 1.
> > #estimates diverging; iteration terminated.
> >
> > In short, I think it would be better to go with the suggestion in my
> > previous email with adequate changes in "Categ" variable (adding
> > ObeseOverweight14, ObeseOverweight21 etc) as I showed here.
> >
> > A.K.
> >
> >
> >
> >
> >
> >
> >
> >
> > ------------------------------
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