Message-ID: <1357586084.51208.YahooMailNeo@web142604.mail.bf1.yahoo.com>
Date: 2013-01-07T19:14:44Z
From: arun
Subject: random effects model
In-Reply-To: <CAPgEEyj3LKnCbH0YEyGe0j+RQWwzs18k9EBF8t79nsG7mObjAg@mail.gmail.com>
HI,
Regarding question:2) Have you checked summary(m1)?
data(seizure)
???? ## Diggle, Liang, and Zeger (1994) pp166-168, compare Table 8.10
???? 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)
#Call:
#geese(formula = y ~ offset(log(t)) + x + trt + x:trt, id = id,
?# ? data = seiz.l, family = poisson, corstr = "exch")
#
#Mean Model:
# Mean Link:???????????????? log
# Variance to Mean Relation: poisson
?#Coefficients:
? # ??????????? estimate??? san.se?????? wald???????? p??????????????????
#(Intercept)? 1.34760922 0.1573571 73.3423807 0.0000000
#x??????????? 0.11183602 0.1159304? 0.9306116 0.3347040
#trt????????? 0.02753449 0.2217878? 0.0154127 0.9011982
#x:trt?????? -0.10472579 0.2134448? 0.2407334 0.6236769
--------------------------------------------------------------------
#p is the colname with the p values for the Coefficients
summary(m1)$mean["p"]
#??????????????????? p
#(Intercept) 0.0000000
#x?????????? 0.3347040
#trt???????? 0.9011982
#x:trt?????? 0.6236769
You didn't say specifically whether you got NA's in example data or your actual data.? I am getting the p-values in R 2.15.
1) I think it should work with binary response variable.
Another example in the same documentation:
?data(ohio)
str(ohio)
#'data.frame':??? 2148 obs. of? 4 variables:
# $ resp : int? 0 0 0 0 0 0 0 0 0 0 ...
# $ id?? : int? 0 0 0 0 1 1 1 1 2 2 ...
# $ age? : int? -2 -1 0 1 -2 -1 0 1 -2 -1 ...
# $ smoke: int? 0 0 0 0 0 0 0 0 0 0 ...
It is not even factors
???? fit <- geese(resp ~ age + smoke + age:smoke, id=id, data=ohio,
????????????????? family=binomial, corstr="exch", scale.fix=TRUE)
?summary(fit)$mean["p"]
?# ?????????????????? p
#(Intercept) 0.00000000
#age???????? 0.01523698
#smoke?????? 0.09478252
#age:smoke?? 0.42234200
# also tested after converting to factors
ohio1<-ohio
ohio1$smoke<-factor(ohio1$smoke)
?ohio1$age<-factor(ohio1$age)
str(ohio1)
#'data.frame':??? 2148 obs. of? 4 variables:
# $ resp : int? 0 0 0 0 0 0 0 0 0 0 ...
# $ id?? : int? 0 0 0 0 1 1 1 1 2 2 ...
# $ age? : Factor w/ 4 levels "-2","-1","0",..: 1 2 3 4 1 2 3 4 1 2 ...
# $ smoke: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
fit1<-geese(resp~age+smoke+age:smoke,id=id,data=ohio1,family=binomial,corstr="exch",scale.fix=TRUE)
?summary(fit1)$mean["p"]
#????????????????????? p
#(Intercept)? 0.00000000
#age-1??????? 0.60555454
#age0???????? 0.45322698
#age1???????? 0.01187725
#smoke1?????? 0.86262269
#age-1:smoke1 0.17239050
#age0:smoke1? 0.32223942
#age1:smoke1? 0.36686706
A.K.
----- Original Message -----
From: rex2013 <usha.nathan at gmail.com>
To: r-help at r-project.org
Cc:
Sent: Monday, January 7, 2013 6:15 AM
Subject: Re: [R] random effects model
Hi A.K
Below is the comment I get, not sure why.
BP.sub3 is the stacked data without the missing values.
BP.geese3 <- geese(HiBP~time*MaternalAge,data=BP.sub3,id=CODEA,
family=binomial, corstr="unstructured", na.action=na.omit)Error in
`contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
? contrasts can be applied only to factors with 2 or more levels
Even though age has 3 levels; time has 14 years & 21 years; HIBP is a
binary response outcome.
2) When you mentioned summary(m1)$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.
> >
> >
> >
> >
> >
> >
> >
> >
> > ------------------------------
> >? If you reply to this email, your message will be added to the
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> > below:
> >
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> > .
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