Hi AK Got an error message with
library(ggplot2)>
ggplot(BP.stack1,aes(x=factor(HiBP),fill=Obese))+geom_bar(position="fill")Error
in rename(x, .base_to_ggplot, warn_missing = FALSE) :
could not find function "revalue">
ggplot(BP.stack1,aes(x=factor(HiBP),fill=Overweight))+geom_bar(position="fill")Error
in rename(x, .base_to_ggplot, warn_missing = FALSE) :
could not find function "revalue"
I got the dot plot, thanks for that.
I have attached some plots, not sure how to interpret, they had
unusual patterns.Is it because of missing data? I tried removing the
missing data too. They still appeared the same. Do I need to transform
the data?
Thanks in advance.
On Tue, Jan 15, 2013 at 8:54 AM, arun <smartpink...@yahoo.com> wrote:
> 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
> >>>
> >>
> >
>
<<attachment: QQ plot HIBP ~Overweight.png>>
<<attachment: Fitted vs residuals HiBP ~time+Sex+Maternal Age, random intercept.png>>
<<attachment: QQ plot hibp21 BP.sub4.png>>
<<attachment: HiBP ~Overweight qq plots residuals,random effects.png>>
______________________________________________ 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.
