[R] random effects model
arun
smartpink111 at yahoo.com
Wed Jan 9 03:18:47 CET 2013
HI,
In your dataset, the "exchangeable" or "compound symmetry" may work as there are only two levels for time. In experimental data analysis involving a factor time with more than 2 levels, randomization of combination of levels of factors applied to the subject/plot etc. gets affected as time is unidirectional. I guess your data is observational, and with two time levels, it may not hurt to use "CS" as option, though, it would help if you check different options.
In the link I sent previously, QIC was used. http://stats.stackexchange.com/questions/577/is-there-any-reason-to-prefer-the-aic-or-bic-over-the-other
I am not sure whether AIC/BIC is better than QIC or viceversa.
You could sent email to the maintainer of geepack (Jun Yan <jun.yan at uconn.edu>).
Regarding the reference links,
You can check this link "www.jstatsoft.org/v15/i02/paper" . Other references are in the paper.
"
4.3. Missing values (waves)
In case of missing values, the GEE estimates are consistent if the values are missing com-
pletely at random (Rubin 1976). The geeglm function assumes by default that observations
are equally separated in time. Therefore, one has to inform the function about different sep-
arations if there are missing values and other correlation structures than the independence or
exchangeable structures are used. The waves arguments takes an integer vector that indicates
that two observations of the same cluster with the values of the vector of k respectively l have
a correlation of rkl ."
Hope it helps.
A.K.
----- Original Message -----
From: rex2013 <usha.nathan at gmail.com>
To: r-help at r-project.org
Cc:
Sent: Tuesday, January 8, 2013 5:29 PM
Subject: Re: [R] random effects model
Hi
Thanks a lot, the corstr "exchangeable"does work. Didn't strike to me
for so long. Does the AIC value come out with the gee output?
By reference, I meant reference to a easy-read paper or web address
that can give me knowledge about implications of missing data.
Ta.
On 1/8/13, arun kirshna [via R]
<ml-node+s789695n4654902h89 at n4.nabble.com> wrote:
>
>
> HI,
> BP.stack5 is the one without missing values.
> na.omit(....). Otherwise, I have to use the option na.action=.. in the
> ?geese() statement
>
> You need to read about the correlation structures. IN unstructured option,
> more number of parameters needs to be estimated, In repeated measures
> design, when the underlying structure is not known, it would be better to
> compare using different options (exchangeable is similar to compound
> symmetry) and select the one which provide the least value for AIC or BIC.
> Have a look at
>
> http://stats.stackexchange.com/questions/21771/how-to-perform-model-selection-in-gee-in-r
> It's not clear to me "reference to write about missing values".
> A.K.
>
>
>
>
> ----- Original Message -----
> From: Usha Gurunathan <usha.nathan at gmail.com>
> To: arun <smartpink111 at yahoo.com>
> Cc:
> Sent: Monday, January 7, 2013 6:12 PM
> Subject: Re: [R] random effects model
>
> Hi AK
>
> 2)I shall try putting exch. and check when I get home. Btw, what is
> BP.stack5? is it with missing values or only complete cases?
>
> I guess I am still not clear about the unstructured and exchangeable
> options, as in which one is better.
>
> 1)Rgding the summary(p): NA thing, I tried putting one of my gee equation.
>
> Can you suggest me a reference to write about" missing values and the
> implications for my results"
>
> Thanks.
>
>
>
> On 1/8/13, arun <smartpink111 at yahoo.com> wrote:
>> 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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