Hi AK
Regarding the missing values, I would like to find out the patterns of
missing values in my data set. I know the overall values for each variable.
using
colSums(is.na(df))
but what I wanted is to find out the percentages
with each level of the variable with my dataset, as in if there is more
missing data in females or males etc?.
I installed "mi" package, but unable to produce a plot with it( i would
also like to produce a plot). I searched the responses in the relevant
sections in r but could n't find an answer.
Thanks,
On Wed, Jan 9, 2013 at 12:31 PM, arun kirshna [via R] <
ml-node+s789695n465499...@n4.nabble.com> wrote:
> 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 <[hidden
> email]<http://user/SendEmail.jtp?type=node&node=4654996&i=0>>).
>
> 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 <[hidden
> email]<http://user/SendEmail.jtp?type=node&node=4654996&i=1>>
>
> To: [hidden email] <http://user/SendEmail.jtp?type=node&node=4654996&i=2>
> 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]
> <[hidden email] <http://user/SendEmail.jtp?type=node&node=4654996&i=3>>
> 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 <[hidden
> > email]<http://user/SendEmail.jtp?type=node&node=4654996&i=4>>
>
> > To: arun <[hidden
> > email]<http://user/SendEmail.jtp?type=node&node=4654996&i=5>>
>
> > 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 <[hidden
> > email]<http://user/SendEmail.jtp?type=node&node=4654996&i=6>>
> 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$MaternalAge)
>
> >>
> >>
> fit5<-geese(hibp~MaternalAge*time,id=CODEA,data=BP.stack7,family=binomial,corstr="unstructured",scale.fix=TRUE)
>
> >> #Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
> >> # contrasts can be applied only to factors with 2 or more levels
> >>
> >> with(BP.stack7,table(MaternalAge,time)) #It looks like the
> combinations
> >> are still there
> >> # time
> >> #MaternalAge 14 21
> >> # 3 1104 864
> >> # 4 875 667
> >>
> >> It works also with corstr="ar1". Why do you gave the option
> >> "unstructured"?
> >> A.K.
> >>
> >>
> >>
> >>
> >>
> >>
> >> ----- Original Message -----
> >> From: rex2013 <[hidden
> >> email]<http://user/SendEmail.jtp?type=node&node=4654996&i=7>>
>
> >> To: [hidden email]<http://user/SendEmail.jtp?type=node&node=4654996&i=8>
> >> Cc:
> >> Sent: Monday, January 7, 2013 6:15 AM
> >> Subject: Re: [R] random effects model
> >>
> >> Hi A.K
> >>
> >> Below is the comment I get, not sure why.
> >>
> >> BP.sub3 is the stacked data without the missing values.
> >>
> >> BP.geese3 <- geese(HiBP~time*MaternalAge,data=BP.sub3,id=CODEA,
> >> family=binomial, corstr="unstructured", na.action=na.omit)Error in
> >> `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
> >> contrasts can be applied only to factors with 2 or more levels
> >>
> >> Even though age has 3 levels; time has 14 years & 21 years; HIBP is a
> >> binary response outcome.
> >>
> >> 2) When you mentioned summary(m1)$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] <
> >> [hidden email] <http://user/SendEmail.jtp?type=node&node=4654996&i=9>>
> 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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