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+s789695n4654902...@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.nat...@gmail.com> > To: arun <smartpink...@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 <smartpink...@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$MaternalAge) >> >> fit5<-geese(hibp~MaternalAge*time,id=CODEA,data=BP.stack7,family=binomial,corstr="unstructured",scale.fix=TRUE) >> #Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : >> # contrasts can be applied only to factors with 2 or more levels >> >> with(BP.stack7,table(MaternalAge,time)) #It looks like the combinations >> are still there >> # time >> #MaternalAge 14 21 >> # 3 1104 864 >> # 4 875 667 >> >> It works also with corstr="ar1". Why do you gave the option >> "unstructured"? >> A.K. >> >> >> >> >> >> >> ----- Original Message ----- >> From: rex2013 <usha.nat...@gmail.com> >> To: r-help@r-project.org >> Cc: >> Sent: Monday, January 7, 2013 6:15 AM >> Subject: Re: [R] random effects model >> >> Hi A.K >> >> Below is the comment I get, not sure why. >> >> BP.sub3 is the stacked data without the missing values. >> >> BP.geese3 <- geese(HiBP~time*MaternalAge,data=BP.sub3,id=CODEA, >> family=binomial, corstr="unstructured", na.action=na.omit)Error in >> `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : >> contrasts can be applied only to factors with 2 or more levels >> >> Even though age has 3 levels; time has 14 years & 21 years; HIBP is a >> binary response outcome. >> >> 2) When you mentioned summary(m1)$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+s789695n4654795...@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 >>> discussion >>> > below: >>> > >>> >>> > . >>> > NAML< >>> http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=macro_viewer&id=instant_html%21nabble%3Aemail.naml&base=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace&breadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> >>> >>> > >>> >>> >>> >>> >>> -- >>> View this message in context: >>> http://r.789695.n4.nabble.com/random-effects-model-tp4654346p4654776.html >>> Sent from the R help mailing list archive at Nabble.com. >>> [[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> [hidden email] >>> <http://user/SendEmail.jtp?type=node&node=4654795&i=3>mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >>> >>> ______________________________________________ >>> [hidden email] >>> <http://user/SendEmail.jtp?type=node&node=4654795&i=4>mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >>> >>> ------------------------------ >>> If you reply to this email, your message will be added to the >>> discussion >>> below: >>> http://r.789695.n4.nabble.com/random-effects-model-tp4654346p4654795.html >>> To unsubscribe from random effects model, click >>> here<http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by_code&node=4654346&code=dXNoYS5uYXRoYW5AZ21haWwuY29tfDQ2NTQzNDZ8MjAyMjE1NDI0> >>> . >>> NAML<http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=macro_viewer&id=instant_html%21nabble%3Aemail.naml&base=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace&breadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> >>> >> >> >> >> >> -- >> View this message in context: >> http://r.789695.n4.nabble.com/random-effects-model-tp4654346p4654826.html >> Sent from the R help mailing list archive at Nabble.com. >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >> > > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > > > > _______________________________________________ > If you reply to this email, your message will be added to the discussion > below: > http://r.789695.n4.nabble.com/random-effects-model-tp4654346p4654902.html > > To unsubscribe from random effects model, visit > http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by_code&node=4654346&code=dXNoYS5uYXRoYW5AZ21haWwuY29tfDQ2NTQzNDZ8MjAyMjE1NDI0 -- View this message in context: http://r.789695.n4.nabble.com/random-effects-model-tp4654346p4654986.html Sent from the R help mailing list archive at Nabble.com. 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