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$Obese
== :
#object 'BPsub3' not found
A.K.
________________________________
From: Usha Gurunathan <usha.nat...@gmail.com>
To: arun <smartpink...@yahoo.com>
Sent: Sunday, January 13, 2013 1:51 AM
Subject: Re: [R] random effects model
HI AK
Thanks a lot for explaining that.
1. With the chi sq. ( in order to find out if the diffce is significant between
groups) do I have create a separate excel file and make a dataframe.How do I go
about it?
I have resent a mail to Jun Yan at a difft email ad( first add.didn't work,
mail not delivered).
2. With my previous query ( reg. Obese/Overweight/ Normal at age 14 Vs change
of blood pressure status at 21), even though I had compromised without the
age-specific regression, but I am still keen to explore why the age-specific
regression didn't work despite it looking okay. I have given below the syntax.
If you get time, could you kindly look at it and see if it could work by any
chance? Apologies for persisting with this query.
BP.stack3 <-
reshape(Copy.of.BP_2,idvar="CODEA",timevar="time",sep="_",varying=list(c("Obese14","Obese21"),c("Overweight14","Overweight21")),v.names=c("Obese","Overweight"),times=factor(c(1,2)),direction="long
BP.stack3
head(BP.stack3)
tail(BP.stack3)
names(BP.stack3)[c(2,3,4,5,6,7)] <-
c("Sex","MaternalAge","Education","Birthplace","AggScore","IntScore")
BP.stack3 <-
transform(BP.stack3,CODEA=factor(CODEA),Sex=factor(Sex,labels=c("Male","Female")),MaternalAge=factor(MaternalAge,labels=c("39years
or less","40-49 years","50 years or
older")),Education=factor(Education,labels=c("Primary/special","Started
secondary","Completed grade10", "Completed grade12",
"College","University")),Birthplace=factor(Birthplace,labels=c("Australia","Other
English-speaking","Other")))
table(BP.stack3$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 <smartpink...@yahoo.com> 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
>(ma...@math.uni-augsburg.de) 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 <usha.nat...@gmail.com>
>To: arun <smartpink...@yahoo.com>
>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 <smartpink...@yahoo.com> 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 <usha.nat...@gmail.com>
>>To: arun <smartpink...@yahoo.com>
>>Cc: R help <r-help@r-project.org>
>>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 <smartpink...@yahoo.com> 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
>>
>
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