HI,
BP_2b<-read.csv("BP_2b.csv",sep="\t")
BP_2bNM<-na.omit(BP_2b)
BP.stack3 <-
reshape(BP_2bNM,idvar="CODEA",timevar="time",sep="",varying=list(c("Obese14","Obese21"),c("Overweight14","Overweight21"),c("hibp14","hibp21")),v.names=c("Obese","Overweight","HiBP"),times=factor(c(1,2)),direction="long")
library(car)
BP.stack3$Obese<- recode(BP.stack3$Obese,"1='Obese';0='Not Obese'")
BP.stack3$Overweight<- recode(BP.stack3$Overweight,"1='Overweight';0='Not
Overweight'")
library(ggplot2)
ggplot(BP.stack3,aes(x=factor(HiBP),fill=Obese))+geom_bar(position="fill")
ggplot(BP.stack3,aes(x=factor(HiBP),fill=Overweight))+geom_bar(position="fill")
You could try lmer() from lme4.
library(lme4)
fm1<-lmer(HiBP~time+(1|CODEA), family=binomial,data=BP.stack3) #check codes,
not sure
print(dotplot(ranef(fm1,post=TRUE),
scales = list(x = list(relation = "free")))[[1]])
qmt1<- qqmath(ranef(fm1, postVar=TRUE))
print(qmt1[[1]])
A.K.
________________________________
From: Usha Gurunathan <usha.nat...@gmail.com>
To: arun <smartpink...@yahoo.com>
Cc: R help <r-help@r-project.org>
Sent: Monday, January 14, 2013 6:32 AM
Subject: Re: [R] random effects model
Hi AK
I have been trying to create some plots. All being categorical variables, I am
not getting any luck with plots. The few ones that have worked are below:
barchart(~table(HiBP)|Obese,data=BP.sub3) ## BP.sub3 is the stacked data
without missing values
barchart(~table(HiBP)|Overweight,data=BP.sub3)
plot(jitter(hibp14,factor=2)~jitter(Obese14,factor=2),col="gray",cex=0.7,
data=Copy.of.BP_2) ## Copy.of.BP_2 is the original wide format
## not producing any good plots with mixed models as well.
summary(lme.3 <- lme(HiBP~time, data=BP.sub3,random=~1|CODEA,
na.action=na.omit))
anova(lme.3)
head(ranef(lme.3))
print(plot(ranef(lme.3))) ##
Thanks for any help.
On Mon, Jan 14, 2013 at 4:33 AM, arun <smartpink...@yahoo.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$Obese == :
> #object 'BPsub3' not found
>
>
>
>
>
>
>
>A.K.
>
>
>________________________________
>From: Usha Gurunathan <usha.nat...@gmail.com>
>To: arun <smartpink...@yahoo.com>
>
>Sent: Sunday, January 13, 2013 1:51 AM
>
>Subject: Re: [R] random effects model
>
>
>
>HI AK
>
>Thanks a lot for explaining that.
>
>1. With the chi sq. ( in order to find out if the diffce is significant
>between groups) do I have create a separate excel file and make a
>dataframe.How do I go about it?
>
>I have resent a mail to Jun Yan at a difft email ad( first add.didn't work,
>mail not delivered).
>
>2. With my previous query ( reg. Obese/Overweight/ Normal at age 14 Vs change
>of blood pressure status at 21), even though I had compromised without the
>age-specific regression, but I am still keen to explore why the age-specific
>regression didn't work despite it looking okay. I have given below the syntax.
>If you get time, could you kindly look at it and see if it could work by any
>chance? Apologies for persisting with this query.
>
>
>BP.stack3 <-
>reshape(Copy.of.BP_2,idvar="CODEA",timevar="time",sep="_",varying=list(c("Obese14","Obese21"),c("Overweight14","Overweight21")),v.names=c("Obese","Overweight"),times=factor(c(1,2)),direction="long
>BP.stack3
>head(BP.stack3)
>tail(BP.stack3)
>
> names(BP.stack3)[c(2,3,4,5,6,7)] <-
>c("Sex","MaternalAge","Education","Birthplace","AggScore","IntScore")
>
>BP.stack3 <-
>transform(BP.stack3,CODEA=factor(CODEA),Sex=factor(Sex,labels=c("Male","Female")),MaternalAge=factor(MaternalAge,labels=c("39years
>or less","40-49 years","50 years or
>older")),Education=factor(Education,labels=c("Primary/special","Started
>secondary","Completed grade10", "Completed grade12",
>"College","University")),Birthplace=factor(Birthplace,labels=c("Australia","Other
>English-speaking","Other")))
>
>table(BP.stack3$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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