Sorry
I have corrected the mistakes:
BP.stack3 <-
reshape(Copy.of.BP_2,idvar="CODEA",timevar="time",sep="_",varying=list(c("Obese14","Obese21"),c("Overweight14","Overweight21")),v.names=c("Obese","Overweight"),times=factor(c(1,2)),direction="long")
BP.stack3
head(BP.stack3)
tail(BP.stack3)
names(BP.stack3)[c(2,3,4,5,6,7)] <-
c("Sex","MaternalAge","Education","Birthplace","AggScore","IntScore")
names(BP.stack3)[1:7]
BP.stack3 <-
transform(BP.stack3,CODEA=factor(CODEA),Sex=factor(Sex,labels=c("Male","Female")),MaternalAge=factor(MaternalAge,labels=c("39years
or less","40-49 years","50 years or
older")),Education=factor(Education,labels=c("Primary/special","Started
secondary","Completed grade10", "Completed grade12",
"College","University")),Birthplace=factor(Birthplace,labels=c("Australia","Other
English-speaking","Other")))
str(BP.stack3)
table(BP.stack3$Sex)
BP.stack3$Sex <- factor(BP.stack3$Sex,levels=levels(BP.stack3$Sex)[c(2,1)])
levels(BP.stack3$Sex)
BPsub6 <- subset(BP.stack3,subset=!(is.na(Sex)| is.na(Education)|is.na
(Birthplace)|is.na(Education)|is.na(hibp14)| is.na(hibp21)))
summary(BPsub6)
BPsub6$Categ[BPsub6$Overweight==1&BPsub6$time==1&BPsub6$Obese==0] <-
"Overweight14"
BPsub6$Categ[BPsub6$Overweight==1&BPsub6$time==2&BPsub6$Obese==0] <-
"Overweight21"
BPsub6$Categ[BPsub6$Obese==1&BPsub6$time==1&BPsub6$Overweight==0|BPsub6$Obese==1&BPsub6$time==1&BPsub6$Overweight==1
] <- "Obese14"
BPsub6$Categ[BPsub6$Obese==0&BPsub6$time==1&BPsub6$Overweight==0] <-
"Normal14"
BPsub6$Categ[BPsub6$Obese==0&BPsub6$time==2&BPsub6$Overweight==0] <-
"Normal21"
BPsub6$Categ[BPsub6$Obese==1&BPsub6$time==2&BPsub6$Overweight==0|BPsub6$Obese==1&BPsub6$time==2&BPsub6$Overweight==1]
<- "Obese21"
BPsub6$Categ <- factor(BPsub6$Categ)
BPsub6$time <- factor(BPsub6$time)
summary(BPsub6$Categ)
BPsub7 <- subset(BPsub6,subset=!(is.na(Categ)))
BPsub7 <- BPsub7[order(BPsub7$CODEA),]
BPsub7$hibp14 <- factor(BPsub7$hibp14)
levels(BPsub7$hibp14)
levels(BPsub7$Categ)
names(BPsub7)
head(BPsub7)
tail(BPsub7)
str(BPsub7)
library(gee)
BP.gee8 <- gee(hibp14~ time*Categ, data=BPsub7,id=CODEA,family=binomial,
corstr="exchangeable",na.action=na.omit)
summary(BP.gee8)
## Can you try this out please? I am not clear where the defect is with
model? One other previous model had no correlation between obese 14 and
time. With this one, i cannot find anything wrong as such, but still wont
work.
Thanks
On Mon, Jan 14, 2013 at 10:30 AM, arun kirshna [via R] <
ml-node+s789695n4655440...@n4.nabble.com> wrote:
>
>
>
> HI,
>
> I think I mentioned to you before that when you reshape the
> columns excluding the response variable, response variable gets repeated
> (in this case hibp14 or hibp21) and creates the error"
>
>
> I run your code, there are obvious problems in the code so I didn't reach
> up to BP.gee
>
> BP_2b<-read.csv("BP_2b.csv",sep="\t")
> BP.stack3 <-
> reshape(BP_2b,idvar="CODEA",timevar="time",sep="_",varying=list(c("Obese14","Obese21"),c("Overweight14","Overweight21")),v.names=c("Obese","Overweight"),times=factor(c(1,2)),direction="long")
>
>
> BP.stack3 <-
> transform(BP.stack3,CODEA=factor(CODEA),Sex=factor(Sex,labels=c("Male","Female")),MaternalAge=factor(MaternalAge,labels=c("39years
> or less","40-49 years","50 years or
> older")),Education=factor(Education,labels=c("Primary/special","Started
> secondary","Completed grade10", "Completed grade12",
> "College","University")),Birthplace=factor(Birthplace,labels=c("Australia","Other
> English-speaking","Other")))
> BP.stack3$Sex <-
> factor(BP.stack3$Sex,levels=levels(BP.stack3$Sex)[c(2,1)])
> BP.sub3a <- subset(BP.stack3,subset=!(is.na(Sex)| is.na(Education)|is.na
> (Birthplace)|is.na(Education)|is.na(hibp14)| is.na(hibp21)))
> nrow(BP.sub3a)
> #[1] 3364
> BP.sub5a <- BP.sub3a[order(BP.sub3a$CODEA),] # your code was BP.sub5a <-
> BP.sub3a[order(BP.sub5a$CODEA),]
>
> ^^^^^ was not defined before
> #Next line
> BPsub3$Categ[BPsub6$Overweight==1&BPsub3$time==1&BPsub3$Obese==0]<-
> "Overweight14" #It should be BP.sub3 and what is BPsub6, it was not
> defined previously.
> #Error in BPsub3$Categ[BPsub6$Overweight == 1 & BPsub3$time == 1 &
> BPsub3$Obese == :
> #object 'BPsub3' not found
>
>
>
>
>
>
> A.K.
>
>
> ________________________________
> From: Usha Gurunathan <[hidden
> email]<http://user/SendEmail.jtp?type=node&node=4655440&i=0>>
>
> To: arun <[hidden
> email]<http://user/SendEmail.jtp?type=node&node=4655440&i=1>>
>
> Sent: Sunday, January 13, 2013 1:51 AM
> Subject: Re: [R] random effects model
>
>
> HI AK
>
> Thanks a lot for explaining that.
>
> 1. With the chi sq. ( in order to find out if the diffce is significant
> between groups) do I have create a separate excel file and make a
> dataframe.How do I go about it?
>
> I have resent a mail to Jun Yan at a difft email ad( first add.didn't
> work, mail not delivered).
>
> 2. With my previous query ( reg. Obese/Overweight/ Normal at age 14 Vs
> change of blood pressure status at 21), even though I had compromised
> without the age-specific regression, but I am still keen to explore why the
> age-specific regression didn't work despite it looking okay. I have given
> below the syntax. If you get time, could you kindly look at it and see if
> it could work by any chance? Apologies for persisting with this query.
>
>
> BP.stack3 <-
> reshape(Copy.of.BP_2,idvar="CODEA",timevar="time",sep="_",varying=list(c("Obese14","Obese21"),c("Overweight14","Overweight21")),v.names=c("Obese","Overweight"),times=factor(c(1,2)),direction="long
>
> BP.stack3
> head(BP.stack3)
> tail(BP.stack3)
>
> names(BP.stack3)[c(2,3,4,5,6,7)] <-
> c("Sex","MaternalAge","Education","Birthplace","AggScore","IntScore")
>
> BP.stack3 <-
> transform(BP.stack3,CODEA=factor(CODEA),Sex=factor(Sex,labels=c("Male","Female")),MaternalAge=factor(MaternalAge,labels=c("39years
>
> or less","40-49 years","50 years or
> older")),Education=factor(Education,labels=c("Primary/special","Started
> secondary","Completed grade10", "Completed grade12",
> "College","University")),Birthplace=factor(Birthplace,labels=c("Australia","Other
>
> English-speaking","Other")))
>
> table(BP.stack3$Sex)
> BP.stack3$Sex <-
> factor(BP.stack3$Sex,levels=levels(BP.stack3$Sex)[c(2,1)])
>
> levels(BP.stack3$Sex)
> BP.sub3a <- subset(BP.stack3,subset=!(is.na(Sex)| is.na(Education)|is.na
> (Birthplace)|is.na(Education)|is.na(hibp14)| is.na(hibp21)))
> summary(BP.sub3a)
> BP.sub5a <- BP.sub3a[order(BP.sub5a$CODEA),]
> BPsub3$Categ[BPsub6$Overweight==1&BPsub3$time==1&BPsub3$Obese==0]
> <- "Overweight14"
> BPsub3$Categ[BPsub6$Overweight==1&BPsub3$time==2&BPsub3$Obese==0]
> <- "Overweight21"
> BPsub3$Categ[BPsub3$Obese==1&BPsub3$time==1&BPsub3$Overweight==0|BPsub3$Obese==1&BPsub3$time==1&BPsub3$Overweight==1
>
> ] <- "Obese14"
> BPsub3$Categ[BPsub3$Obese==0&BPsub3$time==1&BPsub3
> BPsub3$Categ[BPsub6$Overweight==1&BPsub3$time==1&BPsub3$Obese==0]
> <- "Overweight14"$Overweight==0]
> <- "Normal14"
> BPsub3$Categ[BPsub3$Obese==0&BPsub3$time==2&BPsub3$Overweight==0]
> <- "Normal21"
> BPsub3$Categ[BPsub3$Obese==1&BPsub3$time==2&BPsub3$Overweight==0|BPsub3$Obese==1&BPsub3$time==2&BPsub3$Overweight==1]
>
> <- "Obese21"
>
>
>
> BPsub3$Categ <- factor(BPsub3$Categ)
> BPsub3$time <- factor(BPsub3$time)
> summary(BPsub3$Categ)
> BPsub7 <- subset(BPsub6,subset=!(is.na(Categ)))
> BPsub7$time <-
> recode(BPsub7$time,"1=14;2=21")
> BPsub7$hibp14 <- factor(BPsub7$hibp14)
> levels(BPsub7$hibp14)
> levels(BPsub7$Categ)
> names(BPsub7)
> head(BPsub7) ### this was looking quite okay.
>
> tail(BPsub7)
> str(BPsub7)
>
> library(gee)
>
> BP.gee <- geese(hibp14~ time*Categ,
> data=BPsub7,id=CODEA,family=binomial,
> corstr="exchangeable",na.action=na.omit)
>
> Thanks again.
>
>
>
> On Sun, Jan 13, 2013 at 1:22 PM, arun <[hidden
> email]<http://user/SendEmail.jtp?type=node&node=4655440&i=2>>
> wrote:
>
> HI,
>
> >
> >table(BP_2b$Sex) #original dataset
> ># 1 2
> >#3232 3028
> > nrow(BP_2b)
> >#[1] 6898
> > nrow(BP_2bSexNoMV)
> >#[1] 6260
> > 6898-6260
> >#[1] 638 #these rows were removed from the BP_2b to create BP_2bSexNoMV
> >BP_2bSexMale<-BP_2bSexNoMV[BP_2bSexNoMV$Sex=="Male",]
> > nrow(BP_2bSexMale)
> >#[1] 3232
> > nrow(BP_2bSexMale[!complete.cases(BP_2bSexMale),]) #Missing rows with
> Male
> >#[1] 2407
> > nrow(BP_2bSexMale[complete.cases(BP_2bSexMale),]) #Non missing rows with
> Male
> >#[1] 825
> >
> >
> >You did the chisquare test on the new dataset with 6260 rows, right.
> >I removed those 638 rows because these doesn't belong to either male or
> female, but you want the % of missing value per male or female. So, I
> thought this will bias the results. If you want to include the missing
> values, you could do it, but I don't know where you would put that missing
> values as it cannot be classified as belonging specifically to males or
> females. I hope you understand it.
> >
> >Sometimes, the maintainer's respond a bit slow. You have to sent an
> email reminding him again.
> >
> >Regarding the vmv package, you could email Waqas Ahmed Malik ([hidden
> email] <http://user/SendEmail.jtp?type=node&node=4655440&i=3>) regarding
> options for changing the title and the the font etc.
> >You could also use this link (
> http://www.r-bloggers.com/visualizing-missing-data-2/ ) to plot missing
> value (?plot.missing()). I never used that package, but you could try.
> Looks like it gives more information.
> >
> >A.K.
> >
> >
> >
> >
> >
> >
> >
> >
> >________________________________
> >From: Usha Gurunathan <[hidden
> >email]<http://user/SendEmail.jtp?type=node&node=4655440&i=4>>
>
> >To: arun <[hidden
> >email]<http://user/SendEmail.jtp?type=node&node=4655440&i=5>>
>
> >Sent: Saturday, January 12, 2013 9:05 PM
> >
> >Subject: Re: [R] random effects model
> >
> >
> >Hi A.K
> >
> >So it is number of females missing/total female participants enrolled:
> 72.65%
> >Number of females missing/total (of males+ females) participants
> enrolled : 35.14%
> >
> >The total no. with the master data: Males: 3232, females: 3028 ( I got
> this before removing any missing values)
> >
> >with table(Copy.of.BP_2$ Sex) ## BP
> >
> >
> >If I were to write a table ( and do a chi sq. later),
> >
> >as Gender Study Non study/missing Total
> > Male 825 (25.53%) 2407 (74.47%) 3232
> (100%)
> > Female 828 (27.35%) 2200 (72.65%) 3028 (
> 100%)
> > Total 1653 4607
> 6260
> >
> >
> >The problem is when I did
> >>colSums(is.na(Copy.of.BP_2), the sex category showed N=638.
> >
> >I cannot understand the discrepancy.Also, when you have mentioned to
> remove NA, is that not a missing value that needs to be included in the
> total number missing. I am a bit confused. Can you help?
> >
> >## I tried sending email to gee pack maintainer at the ID with R site,
> mail didn't go through??
> >
> >Many thanks
> >
> >
> >
> >
> >
> >
> >On Sun, Jan 13, 2013 at 9:17 AM, arun <[hidden
> >email]<http://user/SendEmail.jtp?type=node&node=4655440&i=6>>
> wrote:
> >
> >Hi,
> >>Yes, you are right. 72.655222% was those missing among females.
> 35.14377% of values in females are missing from among the whole dataset
> (combined total of Males+Females data after removing the NAs from the
> variable "Sex").
> >>
> >>A.K.
> >>
> >>
> >>
> >>________________________________
> >>From: Usha Gurunathan <[hidden
> >>email]<http://user/SendEmail.jtp?type=node&node=4655440&i=7>>
>
> >>To: arun <[hidden
> >>email]<http://user/SendEmail.jtp?type=node&node=4655440&i=8>>
>
> >>Cc: R help <[hidden
> >>email]<http://user/SendEmail.jtp?type=node&node=4655440&i=9>>
>
> >>Sent: Saturday, January 12, 2013 5:59 PM
> >>
> >>Subject: Re: [R] random effects model
> >>
> >>
> >>
> >>Hi AK
> >>That works. I was trying to get similar results from any other package.
> Being a beginner, I was not sure how to modify the syntax to get my output.
> >>
> >>lapply(split(BP_2bSexNoMV,BP_
> >>2bSexNoMV$Sex),function(x)
> (nrow(x[!complete.cases(x[,-2]),])/nrow(x))*100) #gives the percentage of
> rows of missing #values from the overall rows for Males and Females
> >>#$Female
> >>#[1] 72.65522
> >>#
> >>#$Male
> >>#[1] 74.47401
> >>
> >>#iF you want the percentage from the total number rows in Males and
> Females (without NA's in the the Sex column)
> >> lapply(split(BP_2bSexNoMV,BP_2bSexNoMV$Sex),function(x)
> (nrow(x[!complete.cases(x[,-2]),])/nrow(BP_2bSexNoMV))*100)
> >>#$Female
> >>#[1] 35.14377
> >>#
> >>#$Male
> >>#[1] 38.45048
> >>
> >>How do I interpret the above 2 difft results? 72.66% of values were
> missing among female participants?? Can you pl. clarify.
> >>
> >>Many thanks.
> >>
> >>
> >>On Sun, Jan 13, 2013 at 3:28 AM, arun <[hidden
> >>email]<http://user/SendEmail.jtp?type=node&node=4655440&i=10>>
> wrote:
> >>
> >>lapply(split(BP_2bSexNoMV,BP_2bSexNoMV$Sex),function(x)
> (nrow(x[!complete.cases(x[,-2]),])/nrow(x))*100) #gives the percentage of
> rows of missing #values from the overall rows for Males and Females
> >>>#$Female
> >>>#[1] 72.65522
> >>>#
> >>>#$Male
> >>>#[1] 74.47401
> >>>
> >>>#iF you want the percentage from the total number rows in Males and
> Females (without NA's in the the Sex column)
> >>> lapply(split(BP_2bSexNoMV,BP_2bSexNoMV$Sex),function(x)
> (nrow(x[!complete.cases(x[,-2]),])/nrow(BP_2bSexNoMV))*100)
> >>>#$Female
> >>>#[1] 35.14377
> >>>#
> >>>#$Male
> >>>#[1] 38.45048
> >>
> >
>
> ______________________________________________
> [hidden email] <http://user/SendEmail.jtp?type=node&node=4655440&i=11>mailing
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