Hi, For your first question, M1<-as.table(rbind(c(825,2407),c(828,2200))) dimnames(M1)<- list(gender=c("Male","Female"), MV=c("Study","NonStudy/missing")) M1 # MV #gender Study NonStudy/missing # Male 825 2407 # Female 828 2200
Xsq<-chisq.test(M1) Xsq # Pearson's Chi-squared test with Yates' continuity correction #data: M1 #X-squared = 2.5684, df = 1, p-value = 0.109 I will take a look at your second question later. 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? 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 >> > ______________________________________________ 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.