Hi Arun
Thanks for the answer.1) I was not aware of the "tail" function.
2)Regarding the 2nd question answer, I am trying to understand the
BP.stacknormal$Categ<-"Normal"
BP.stackObese$Categ<-"Obese"
what does the"category" imply? Also, when we bind two subsets(rbind), the
result of regression has "time=14 years" and "Normal weight" as the
reference category, is that correct.
Since the question is Obese/Overweight14 Vs Normal weight14 in relation to
HIBP at 21.
I did the following:
BP.stacknormal<-subset(Copy.of.BP_2,Obese14==0 & Overweight14==0)
nrow(BP.stacknormal)
BP.stackObese<-subset(Copy.of.BP_2,Obese14==1 )
nrow(BP.stackObese)
BP.stackOverweight<-subset(Copy.of.BP_2, Overweight14==1)
nrow(BP.stackOverweight)
How to do the rest of the regression since there is no "time" category in
the original data.
BP.stacknormal$Categ<-"Normal14"
BP.stackObese$Categ<-"Obese14"
BP.newObeseNormal<-rbind(BP.stacknormal,BP.stackObese)
BP.newObeseNormal$*time<-factor(BP.newObeseNormal$time)*
BP.newObeseNormal$Categ<- factor(BP.newObeseNormal$Categ)
BPlogit2<-glm(hibp21~*time*+Categ+time*Categ,data=BP.newObeseNormal,family="binomial")
summary(BPlogit2)
Or should I stack BP.newObeseNormal and then regress.
How acceptable is missing values?
I do need random effects model. i can put in nlme, with ID as the random
effect? I cant seem to apply geese model for getting the contrasts as there
are only two levels in all of BP, time,Obese/overweight etc. which leaves
me with only glm and random effects model. Is my conclusion correct?
Just to clarify a couple of things, for different questions:a) Is
overweight/obesity associated with high blood pressure for adolescents and
young adults:
Can a log.regression of Overweight/obese with HiBP in the stacked data be
sufficient?
b) What variables,measured at baseline or age 14, are associated with high
blood pressure at ages 14 and 21?
I was thinking of doing a correlation between factors( as numeric).Could
this answer the question.
(p.s, i shall try postingthese questions int he general email too.)
Happy N.Y
Thank you so much, Usha
On Mon, Dec 31, 2012 at 10:15 AM, arun kirshna [via R] <
ml-node+s789695n4654269...@n4.nabble.com> wrote:
> HI Usha,
> I tried the codes on the full dataset. This is what I get:
> BP_2b<-read.csv("BP_2b.csv",sep="\t")
> #head(BP_2b,2)
> # CODEA Sex MaternalAge Education Birthplace AggScore IntScore Obese14
> #1 1 NA 3 4 1 NA NA NA
> #2 3 2 3 3 1 0 0 0
> # Overweight14 Overweight21 Obese21 hibp14 hibp21
> #1 NA NA NA NA NA
> #2 0 1 0 0 0
> BP.stack1<-
> reshape(BP_2b,sep="",timevar=
> "time",direction="long",varying=list(names(BP_2b[8:9]),names(BP_2b[10:11]),names(BP_2b[12:13])),v.names=c("Obese","Overweight","HiBP"),idvar="CODEA")
>
>
> BP_2b<-BP_2b[,c(1:8,11,9:10,12:13)] #Obese21 was not adjacent to Obese14.
> So, I rearranged the columns to make it adjacent.
> str(BP_2b)
> #'data.frame': 6898 obs. of 13 variables:
> # $ CODEA : int 1 3 4 7 8 9 10 11 12 13 ...
> # $ Sex : int NA 2 2 2 2 1 NA 1 1 2 ...
> # $ MaternalAge : int 3 3 3 4 4 3 3 3 3 3 ...
> # $ Education : int 4 3 6 6 4 6 3 4 4 4 ...
> # $ Birthplace : int 1 1 1 1 1 2 1 1 2 2 ...
> # $ AggScore : int NA 0 NA 0 0 0 NA 0 NA NA ...
> # $ IntScore : int NA 0 NA 0 0 0 NA 0 NA NA ...
> # $ Obese14 : int NA 0 NA 0 0 0 NA NA NA NA ...
> # $ Obese21 : int NA 0 NA 1 0 0 NA 0 NA NA ...
> # $ Overweight14: int NA 0 NA 0 0 0 NA NA NA NA ...
> # $ Overweight21: int NA 1 NA 1 0 0 NA 0 NA NA ...
> # $ hibp14 : int NA 0 NA 0 0 0 NA NA NA NA ...
> # $ hibp21 : int NA 0 NA 0 0 0 NA 0 NA NA ...
>
> BP.stack2<-
> reshape(BP_2b,sep="",timevar=
> "time",direction="long",varying=list(names(BP_2b[8:9]),names(BP_2b[10:11]),names(BP_2b[12:13])),v.names=c("Obese","Overweight","HiBP"),idvar="CODEA")
>
> identical(BP.stack1,BP.stack2)
> #[1] FALSE
>
> library(car)
> BP.stack2$time <- recode(BP.stack2$time,("1=14;2=21"))
> head(BP.stack2,2)
> # CODEA Sex MaternalAge Education Birthplace AggScore IntScore time
> Obese
> #1.1 1 NA 3 4 1 NA NA 14
> NA
> #3.1 3 2 3 3 1 0 0 14
> 0
> # Overweight HiBP
> #1.1 NA NA
> #3.1 0 0
> tail(BP.stack2,2)
> # CODEA Sex MaternalAge Education Birthplace AggScore IntScore time
> Obese
> #8555.2 8555 1 3 6 2 0 0
> 21 NA
> #8556.2 8556 1 3 4 1 0 0
> 21 0
> # Overweight HiBP
> #8555.2 NA NA
> #8556.2 0 NA
> nrow(BP.stack2)
> #[1] 13796
> length(BP.stack2$time[BP.stack2$time==14])
> #[1] 6898
> length(BP.stack2$time[BP.stack2$time==21])
> #[1] 6898
>
> #or
> names(BP_2b)[grep("\\d+",names(BP_2b))]<-gsub("(\\D+)(\\d+)","\\1_\\2",names(BP_2b)[grep("\\d+",names(BP_2b))])
>
> BP.stack3<-reshape(BP_2b,dir="long",varying=8:13,sep="_")
> head(BP.stack3,2)
> # CODEA Sex MaternalAge Education Birthplace AggScore IntScore time
> Obese
> #1.14 1 NA 3 4 1 NA NA 14
> NA
> #2.14 3 2 3 3 1 0 0
> 14 0
> # Overweight hibp id
> #1.14 NA NA 1
> #2.14 0 0 2
> tail(BP.stack3,2)
> # CODEA Sex MaternalAge Education Birthplace AggScore IntScore time
> Obese
> #6897.21 8555 1 3 6 2 0 0
> 21 NA
> #6898.21 8556 1 3 4 1 0 0
> 21 0
> # Overweight hibp id
> #6897.21 NA NA 6897
> #6898.21 0 NA 6898
> nrow(BP.stack3)
> #[1] 13796
> length(BP.stack3$time[BP.stack3$time==21])
> #[1] 6898
> length(BP.stack3$time[BP.stack3$time==14])
> #[1] 6898
>
> #So, I guess this solves the problem. Regarding your second question,
> "Do children who are obese or overweight at age 14 experience different
> change in blood pressure by 21 years, compared to those with normal
> weight at age 14?"
>
> In
> the Obese or Overweight columns, I am not sure what "0" means. For
> example, in the overweight column, does "0" means it is normal, or it
> has to be both "0" for overweight and obese for a particular ID (CODEA)
> in each of the age groups? I guess it should be the latter. If that is
> the case, subset data in to normal, Obese, Overweight and then do the
> comparisons.
> For example:
> BP.stacknormal<-subset(BP.stack3,Obese==0 & Overweight==0)
> nrow(BP.stacknormal)
> [1] 4371
> head(BP.stacknormal,3)
> # CODEA Sex MaternalAge Education Birthplace AggScore IntScore time
> Obese
> #2.14 3 2 3 3 1 0 0
> 14 0
> #4.14 7 2 4 6 1 0 0
> 14 0
> #5.14 8 2 4 4 1 0 0
> 14 0
> # Overweight hibp id
> #2.14 0 0 2
> #4.14 0 0 4
> #5.14 0 0 5
>
> Similarly, you need to subset for Obese==1 , Overweight==1
> BP.stackObese <-subset(BP.stack3,Obese==1)
> nrow(BP.stackObese)
> #[1] 530
>
> Regarding the analysis, I think logistic regression should fit in this
> case because the response variable (hibp) is binary.
> #Just a simple logistic regression
> BP.stacknormal$Categ<-"Normal"
> BP.stackObese$Categ<-"Obese"
> BP.newObeseNormal<-rbind(BP.stacknormal,BP.stackObese)
> BP.newObeseNormal$time<-factor(BP.newObeseNormal$time)
> BP.newObeseNormal$Categ<- factor(BP.newObeseNormal$Categ)
>
>
> BPlogit<-glm(hibp~time+Categ,data=BP.newObeseNormal,family="binomial")
> #or
> BPlogit<-glm(hibp~time+Categ,data=BP.newObeseNormal,family=binomial(logit))
>
> summary(BPlogit)
> #
> #Call:
> #glm(formula = hibp ~ time + Categ, family = "binomial", data =
> BP.newObeseNormal)
> #
> #Deviance Residuals:
> # Min 1Q Median 3Q Max
> #-0.8630 -0.5452 -0.5452 -0.4965 2.0759
>
> #Coefficients:
> # Estimate Std. Error z value Pr(>|z|)
> #(Intercept) -1.83099 0.05768 -31.745 <2e-16 ***
> #time21 -0.20038 0.09290 -2.157 0.031 *
> #CategObese 1.03506 0.12046 8.593 <2e-16 ***
> #---
> #Signif. codes: 0 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â
> â 1
>
> For
> your question, I guess you need to add interaction term also in the
> model. I am not sure whether you need random effects in the model. If
> that is the case, you need ?lmer() from library(lme4). You could also
> post the question in R mixed models ([hidden
> email]<http://user/SendEmail.jtp?type=node&node=4654269&i=0>)
> mailing list.
> A.K.
>
>
>
>
> ----- Original Message -----
> From: usha2013 <[hidden
> email]<http://user/SendEmail.jtp?type=node&node=4654269&i=1>>
>
> To: [hidden email] <http://user/SendEmail.jtp?type=node&node=4654269&i=2>
> Cc:
> Sent: Friday, December 28, 2012 2:02 AM
> Subject: Re: [R] help with reshaping wide to long format
>
> Hi, Sorry, but how did you bring it out?
>
> Thanks
>
> On Fri, Dec 28, 2012 at 8:48 AM, arun kirshna [via R] <
> [hidden email] <http://user/SendEmail.jtp?type=node&node=4654269&i=3>>
> wrote:
>
> > Hi,
> > bp.sub<- structure(list(CODEA = c(1L, 3L, 4L, 7L, 8L, 9L, 10L, 11L, 12L,
> > 13L, 14L, 16L, 17L), C45 = c(NA, 2L, 2L, 2L, 2L, 1L, NA, 1L,
> > 1L, 2L, 1L, 2L, 1L), ragek = c(3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L,
> > 3L, 3L, 3L, 3L, 3L), ra80 = c(4L, 3L, 6L, 6L, 4L, 6L, 3L, 4L,
> > 4L, 4L, 4L, 4L, 4L), ra98 = c(1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
> > 2L, 2L, 2L, 3L, 1L), CBCLAggressionAt1410 = c(NA, 0L, NA, 0L,
> > 0L, 0L, NA, 0L, NA, NA, 0L, 0L, NA), CBCLInternalisingAt1410 = c(NA,
> > 0L, NA, 0L, 0L, 0L, NA, 0L, NA, NA, 0L, 0L, NA), Obese14 = c(NA,
> > 0L, NA, 0L, 0L, 0L, NA, NA, NA, NA, 0L, 0L, NA), Obese21 = c(NA,
> > 0L, NA, 1L, 0L, 0L, NA, 0L, NA, NA, 0L, 0L, NA), Overweight14 = c(NA,
> > 0L, NA, 0L, 0L, 0L, NA, NA, NA, NA, 0L, 0L, NA), Overweight21 = c(NA,
> > 1L, NA, 1L, 0L, 0L, NA, 0L, NA, NA, 1L, 0L, NA), hibp14 = c(NA,
> > 0L, NA, 0L, 0L, 0L, NA, NA, NA, NA, 1L, 1L, NA), hibp21 = c(NA,
> > 0L, NA, 0L, 0L, 0L, NA, 0L, NA, NA, 1L, NA, NA)), .Names = c("CODEA",
> > "C45", "ragek", "ra80", "ra98", "CBCLAggressionAt1410",
> > "CBCLInternalisingAt1410",
> > "Obese14", "Obese21", "Overweight14", "Overweight21", "hibp14",
> > "hibp21"), row.names = c(NA, 13L), class = "data.frame")
> >
> > BP.stack1<- reshape(bp.sub,sep="",timevar=
> >
> "time",direction="long",varying=list(names(bp.sub[8:9]),names(bp.sub[10:11]),names(bp.sub[12:13])),v.names=c("Obese","Overweight","HiBP"),idvar="CODEA")
>
> >
> > BP.stack1$time <- recode(BP.stack1$time,("1=14;2=21"))
> > BP.stack1
> > # CODEA C45 ragek ra80 ra98 CBCLAggressionAt1410
> > CBCLInternalisingAt1410
> > #1.1 1 NA 3 4 1 NA
> > NA
> > #3.1 3 2 3 3 1 0
> > 0
> > #4.1 4 2 3 6 1 NA
> > NA
> > #7.1 7 2 4 6 1 0
> > 0
> > #8.1 8 2 4 4 1 0
> > 0
> > #9.1 9 1 3 6 2 0
> > 0
> > #10.1 10 NA 3 3 1 NA
> > NA
> > #11.1 11 1 3 4 1 0
> > 0
> > #12.1 12 1 3 4 2 NA
> > NA
> > #13.1 13 2 3 4 2 NA
> > NA
> > #14.1 14 1 3 4 2 0
> > 0
> > #16.1 16 2 3 4 3 0
> > 0
> > #17.1 17 1 3 4 1 NA
> > NA
> > #1.2 1 NA 3 4 1 NA
> > NA
> > #3.2 3 2 3 3 1 0
> > 0
> > #4.2 4 2 3 6 1 NA
> > NA
> > #7.2 7 2 4 6 1 0
> > 0
> > #8.2 8 2 4 4 1 0
> > 0
> > #9.2 9 1 3 6 2 0
> > 0
> > #10.2 10 NA 3 3 1 NA
> > NA
> > #11.2 11 1 3 4 1 0
> > 0
> > #12.2 12 1 3 4 2 NA
> > NA
> > #13.2 13 2 3 4 2 NA
> > NA
> > #14.2 14 1 3 4 2 0
> > 0
> > #16.2 16 2 3 4 3 0
> > 0
> > #17.2 17 1 3 4 1 NA
> > NA
> > # time Obese Overweight HiBP
> > #1.1 14 NA NA NA
> > #3.1 14 0 0 0
> > #4.1 14 NA NA NA
> > #7.1 14 0 0 0
> > #8.1 14 0 0 0
> > #9.1 14 0 0 0
> > #10.1 14 NA NA NA
> > #11.1 14 NA NA NA
> > #2.1 14 NA NA NA
> > #13.1 14 NA NA NA
> > #14.1 14 0 0 1
> > #16.1 14 0 0 1
> > #17.1 14 NA NA NA
> > #1.2 21 NA NA NA
> > #3.2 21 0 1 0
> > #4.2 21 NA NA NA
> > #7.2 21 1 1 0
> > #8.2 21 0 0 0
> > #9.2 21 0 0 0
> > #10.2 21 NA NA NA
> > #11.2 21 0 0 0
> > #12.2 21 NA NA NA
> > #13.2 21 NA NA NA
> > #14.2 21 0 1 1
> > #16.2 21 0 0 NA
> > #17.2 21 NA NA NA
> >
> names(bp.sub)[grep("\\d+",names(bp.sub))]<-gsub("(\\D+)(\\d+)","\\1_\\2",names(bp.sub)[grep("\\d+",names(bp.sub))])
>
> >
> > BP.stack2<-reshape(bp.sub,dir="long",varying=8:13,sep="_")
> > Bp.stack2
> > CODEA C_45 ragek ra_80 ra_98 CBCLAggressionAt_1410
> > 1.14 1 NA 3 4 1 NA
> > 2.14 3 2 3 3 1 0
> > 3.14 4 2 3 6 1 NA
> > 4.14 7 2 4 6 1 0
> > 5.14 8 2 4 4 1 0
> > 6.14 9 1 3 6 2 0
> > 7.14 10 NA 3 3 1 NA
> > 8.14 11 1 3 4 1 0
> > 9.14 12 1 3 4 2 NA
> > 10.14 13 2 3 4 2 NA
> > 11.14 14 1 3 4 2 0
> > 12.14 16 2 3 4 3 0
> > 13.14 17 1 3 4 1 NA
> > 1.21 1 NA 3 4 1 NA
> > 2.21 3 2 3 3 1 0
> > 3.21 4 2 3 6 1 NA
> > 4.21 7 2 4 6 1 0
> > 5.21 8 2 4 4 1 0
> > 6.21 9 1 3 6 2 0
> > 7.21 10 NA 3 3 1 NA
> > 8.21 11 1 3 4 1 0
> > 9.21 12 1 3 4 2 NA
> > 10.21 13 2 3 4 2 NA
> > 11.21 14 1 3 4 2 0
> > 12.21 16 2 3 4 3 0
> > 13.21 17 1 3 4 1 NA
> > CBCLInternalisingAt_1410 time Obese Overweight hibp id
> > 1.14 NA 14 NA NA NA 1
> > 2.14 0 14 0 0 0 2
> > 3.14 NA 14 NA NA NA 3
> > 4.14 0 14 0 0 0 4
> > 5.14 0 14 0 0 0 5
> > 6.14 0 14 0 0 0 6
> > 7.14 NA 14 NA NA NA 7
> > 8.14 0 14 NA NA NA 8
> > 9.14 NA 14 NA NA NA 9
> > 10.14 NA 14 NA NA NA 10
> > 11.14 0 14 0 0 1 11
> > 12.14 0 14 0 0 1 12
> > 13.14 NA 14 NA NA NA 13
> > 1.21 NA 21 NA NA NA 1
> > 2.21 0 21 0 1 0 2
> > 3.21 NA 21 NA NA NA 3
> > 4.21 0 21 1 1 0 4
> > 5.21 0 21 0 0 0 5
> > 6.21 0 21 0 0 0 6
> > 7.21 NA 21 NA NA NA 7
> > 8.21 0 21 0 0 0 8
> > 9.21 NA 21 NA NA NA 9
> > 10.21 NA 21 NA NA NA 10
> > 11.21 0 21 0 1 1 11
> > 12.21 0 21 0 0 NA 12
> > 13.21 NA 21 NA NA NA 13
> > A.K.
> >
> >
> >
> >
> >
> > ------------------------------
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