similar to: regression with categorical nuisance variable

*Dear experts,* Please excuse me for disturbing... Right now I am struggling with GLM a bit... Would you be so kind to provide me a solution on using nuisance variables. The problem is that I have data on Depression (volumetric measurements of different brain regions) and I want to include age, gender and education as nuisance parameters in the model. In the other words I would like to model the

I currently have a data set in which gender is inputed as "Male" and "Female" , and I'm trying to convert this into "1" and "0". I found a website which reccomended using two commands: data$scode[data$sex=="M"] <- "1" data$scode[data$sex=="F"] <- "2" to convert to numbers, and: data$scode <-

Dear R experts, I'm beginner. My question about ANOVA for unequal sample sized data should be obsolete but I can not clarify it. I have a dataset from 23 males and 18 females. I measured one condition('cond') with 4 levels. So I'd like to see main effect of gender, cond and gender by cond interaction and also postHoc test. (In fact, I have to do anova 90 times) * 1. Question

Hi R-helpers, I was trying to put gender='Male' in newdata to create a expected survival curve for a pseudo cohort by using survfit based on Cox regression. My codes are shown below: fit<- coxph(Surv(end, status2)~gender, data=wlwsn1) Summary(fit) coef exp(coef) se(coef) z p genderMale 0.204 1.23 0.0912 2.23 0.025

Hi, I am doing multiple regression, and have several X variables that are categorical. I read that I can use dummy or contrast codes for that, but are there any special rules when there're unequal #observations in each groups (4 females vs 7 males in a "gender" variable)? Also, can R generate these codes for me? THanks.

Dear R-experts, Please, excuse me for disturbing... I tried to find the answer to my question in archives without any result... I assume, this is going to look like a stupid question... I have dataset of different brain structures within two groups of subjects. But unfortunately these groups are not matched by age and gender. Would you be so kind to suggest me how (using which formula) can I

> On Apr 16, 2016, at 2:03 AM, Akhilesh Singh <akhileshsingh.igkv at gmail.com> wrote: > > Dear All, > > I have got your core message, that it is my responsibility to determine whether any particular function in my version of R satisfies the language requirements at the time of your use. Jim Albert and Maria Rizzo must have used their code, which was permitted in the R-code

Dear All, I have got your core message, that it is my responsibility to determine whether any particular function in my version of R satisfies the language requirements at the time of your use. Jim Albert and Maria Rizzo must have used their code, which was permitted in the R-code of their time (2012). Therefore, I have now modified my R-code, as per R-3..2.4 version, according to my requirement

Dear List, When I subset a data.frame, the levels are not re-adjusted (see example). Why is this? Am I missing out on some basic stuff here? Thanks Ulrik > m <- data.frame(gender = c("M", "M","F"), ht = c(172, 186.5, 165), wt = c(91,99, 74)) > dim(m) [1] 3 3 > levels(m$gender) [1] "F" "M" > s <- subset(m, m$gender ==

Hi everyone, I'm having some difficulty getting "simple effects" for the ols() function in the rms package. The example below illustrates my difficulty -- I'll be grateful for any help. #make up some data exD <- structure(list(Gender = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("F", "M"), class = "factor"),

> On Apr 15, 2016, at 1:16 AM, Akhilesh Singh <akhileshsingh.igkv at gmail.com> wrote: > > Dear All, > > Thanks for your help. However, I would like to draw your attention to the > following: > > Actually, I was replicating the Example 2.3, using the dataset > "brainsize.txt" given in Section 2.3.3 ("Summarize by group") at page 55, > of a

I am desperately trying to impute missing data using 'imp.mix' but always run into this yucky error message to which I cannot find the solution. It's the first time I am using mix and I'm trying really hard to understand, but there's just this one step I don't get...perhaps someone knows the answer? Thanks! Jens My code runs:

Dear All, Thanks for your help. However, I would like to draw your attention to the following: Actually, I was replicating the Example 2.3, using the dataset "brainsize.txt" given in Section 2.3.3 ("Summarize by group") at page 55, of a famous book "R by Example" written by "Jim Albert and Maria Rizzo" published in Springers (2012) in a Use R! Series. The

Dear Helpers, I just started working with R and I'm a bit overloaded with information. My data is from marsupials reindroduced in a area. I have weight(wt), hind foot lenghts(pes) as continues variables and origin and gender as categorial. condition is just the residuals i took from the model. > names(dat1) [1] "wt" "pes" "origin" "gender"

Hello ALL! I have a problem to plot factor (lets say gender) as a line, or at least both line and point, from ols model: ols1 <- ols(Y ~ gender, data=dat, x=T, y=T) plot(ols1, gender=NA, xlab="gender", ylab="Y", ylim=c(5,30), conf.int=FALSE) If I convert gender into discrete numeric predictor, and use forceLines=TRUE, plot is not nice and true, since it shows values

Hi All: I was recently working with a dataset on arsenic poisoning. Among the variables in the dataset, I used the following three variables to produce crosstabulations (variable names: FOLSTAT, GENDER, ASBIN; all three were categorical variables, FOLSTAT denoted follow up status for the subjects and had seven levels, GENDER denoted sex (two levels: male,female), and ASBIN denoted binarized

Hello R gurus, I have a data set from which i have to extract the gender and age matched rows from controls and disease group disease<-paste(rep(c('y','n'),11)) gender<-paste(rep(c('m','f'),11)) mcp<-rnorm(700,1400) age<-rnorm(32,34) dat<-data.frame(disease=disease,sex=gender,Dr_age=age[1:22],MCP=mcp[1:22]) I have other categorical variables also to

Hi All I received a raw data set with one record per tennis player (both male and female) and then i cured it by aggregation i.e by 4 age groups, 2 gender levels and 6 income levels. Gender and Income are categorical variables. Please advise me how to use 'R' to model this data set (Actually, i want to know the right regression technique and steps to do that, including removing

Hello, I am trying to create individual concentration-time spaghetti plots sorted by dose, and within each dose to show two different colors for a categorical variable (gender). I can’t find a way to add the color for gender. Groups=ID enables individual lines for each subject. If I use groups=gender, there will be two colors, but the lines between subjects are connected. This is what I have

. HI there, I'd like to show demonstrate how the chi-squared distribution works, so I've come up with a sample data frame of two categorical variables y<-data.frame(gender=sample(c('Male', 'Female'), size=100000, replace=TRUE, c(0.5, 0.5)), tea=sample(c('Yes', 'No'), size=100000, replace=TRUE, c(0.5, 0.5))) And I'd like to create a list of 100