search for: gender

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor Gender.Age is Gender*Age Genotype.Age is Genotype*Age Gender.Genotype.Age is Gender*Genotype*Age If I have the intercept (but not the slope) as a random effect the fit converges OK fi...

dear all, I'm building new dataframes from bigger one's using e.g. columns F76, F83, F90: JJ<-data.frame( c( as.character(rep( gender,3))) , c( F76,6- F83, F90) ) Looking into JJ one has: c.as.character.rep.gender..8... c.6...F73..F78..F79..F82..6...F84..F94..F106..F109 1 w 2 2 w 3 3...

Dear R-user, I have a data like this below, age <- c("young","mid","old") married <- c("no","yes") income <- c("low","high","medium") gender <- c("female","male") I want to make some of combination data like these, age.income.dat <- expand.grid(age, married[-c(2)], income, gender[-c(2)]) age.income.dat age.married.dat <- expand.grid(age, married, income[-c(2:3)], gender[-c(2)]) age.married.dat...

Dear R people, I am having a problem with modeling the following SAS code in R: Class ID Gr Hemi Region Gender Model Y = Gr Region Hemi Gender Gr*Hemi Gr*Region Hemi*Region Gender*Region Gender*Hemi Gr*Hemi*Region Gender*Hemi*Region Gr*Gender*Hemi*Region Random Intercept Region Hemi /Subject = ID (Gr Gender) I.e., ID is a random effect nested in Gr and Gender, leading to ID-specific Intercept, Region an...

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 n...

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"), UCS = c(3.11111111111111, 3.5, 3.77777777777778, 2.875, 3.55555555555556, 2.44444444444444, 2.57142857142857, 3.11111111111111, 3.28571428571429,...

Hi, I have a data set similar to the following State Gender Quantity TX Male 1 NY Female 2 TX Male 3 NY Female 4 I need to calculate cumulative sum of the quantity by State and Gender. The expected output is State Gender Quantity CumQuantity TX Male 1 1 TX Male 3 4 NY Female 2 2 NY Female 4 6 I highly appreciate if someone can give me some hints on solv...

Hi, I am trying a two way anova with unequal sample sizes but results are not as expected: I take the example from Applied Linear Statistical Models (Neter et al. pp889-897, 1996) growth rate gender bone development 1.4 1 1 2.4 1 1 2.2 1 1 2.4 1 2 2.1 2 1 1.7 2 1 2.5 2 2 1.8 2 2 2 2 2 0.7 3 1 1.1 3 1 0.5 3 2 0.9 3 2 1.3 3 2 expected results are source of variation SS df MS F gender 0.12 1 0.12 0.74 bone development 4.1897 2 2.0949 12.89** intera...

Hi, I want to use my some session data when I validate som data in the model. The specific problem I have is that I present different forms data based on gender, and then dependent of the gender, there''s different fields that needs validation. I''m wondering what''s the preffered way of doing this. The session data is not present in the model, i.e: class Myclass < ActiveRecord::Base belongs_to :member def validate error...

...#39;m learning R and I'm stuck on a basic concept: how to specify a logical condition once and then perform multiple transformations under that condition. The program below is simplified to demonstrate the goal. Its results are exactly what I want, but I would like to check the logical state of gender only once and create both (or any number of) scores at once. mystring<- ("id,group,gender,q1,q2,q3,q4 01,1,f,2,2,5,4 02,2,f,2,1,4,5 03,1,f,2,2,4,4 04,2,f,1,1,5,5 05,1,m,4,5,4, 06,2,m,5,4,5,5 07,1,m,3,3,4,5 08,2,m,5,5,5,4") mydata<-read.table(textConnection(mystring)...

Hi there, > Value=c(709,679,699,657,594,677,592,538,476,508,505,539) > Lard=rep(c("Fresh","Rancid"),each=6) > Gender=rep(c("Male","Male","Male","Female","Female","Female"),2) > Food=data.frame(Value,Lard,Gender) > Food Value Lard Gender 1 709 Fresh Male 2 679 Fresh Male 3 699 Fresh Male 4 657 Fresh Female 5 594 Fresh...

"&&" seems to behave strangely and gives different results from "&" e.g. in a data frame selection (regardless whether terms are bracketed)? ===========Script======================= test=data.frame(gender=c("F","M","M","F","F"),side=c("R","L","R","L","R")) test test[test$gender=="F" & test$side=="R",] test[test$gender=="F" && test$side=="R",] ======...

I am trying to plot an interaction in a multilevel model. Here is some sample data. In the following example, it is longitudinal (i.e., repeated measures), so the outcome, score (at each of the three time points), is nested within the individual. I am interested in the interaction between gender and happiness predicting score. id <- c(1,1,1,2,2,2,3,3,3) age <- c(10,15,20,10,15,20,10,15,20) gender <- c(1,1,1,0,0,0,1,1,1) happiness <- c(50,30,25,70,65,80,70,40,60) score <- c(180,140,110,240,220,280,150,140,130) mydata <- data.frame(id,age,gender,happiness,score) I am loo...

Dear R-Community! For example I have a study with 4 treatment groups (10 subjects per group) and 4 visits. Additionally, the gender is taken into account. I think - and hope this is a goog idea (!) - this data can be analysed using lme as below. In a balanced design everything is fine, but in an unbalanced design there are differences depending on fitting y~visit*treat*gender or y~gender*visit*treat - at least with anova (see...

Hi Folks, I have data that looks like this: freq gender xBar 1000 m 2.32 1000 f 3.22 2000 m 4.32 2000 f 4.53 3000 m 3.21 3000 f 3.44 4000 m 4.11 4000 f 3.99 I want to plot two lines (with symbols) for the two groups "m" and "f". I have tried the following: plot(xBar[gender=="m"]~freq[gender=="f&quo...

Hello: I need to create a six barplots from data that looks pretty close to what appears below. There are two grouping variables (age and gender) and three dependent variables for each grouping variables. I'm not really familiar with trellis graphics, perhaps there is something that can do what I need there, i don't know. The thing is: I *need* these to appear on one row, with some way of differentiating between the three barplot...

Hello, Say I have 2 columns, bmi and gender, the first being all the values and the second being male or female. How would I subset this into males only and females only? I have searched these fora and read endlessly about select[] and split() functions but to no avail. Also the table is not ordered. bmi gender -> bmi ge...

All, I have been trying to get the following code to work: A.quantiles <- quantile(foo.frame$A, probs = seq(from = 0.05, to = 0.95, by = 0.05)) base.quantiles <- quantile(Efficacy205$BASELINE_RANK, probs = seq(from = 0.05, to = 0.95, by = 0.05)) gender <- levels(Efficacy205$GENDER) contrast.1 <- contrast(Model.1, list(TPCODE= 'A', AGE = age.quantiles, BASELINE_RANK = base.quantiles, GENDER = gender), list(TPCODE = 'D', AGE = age.quantiles,...

Hello, I have the following dataset. Please note that there are missing values on records 4 and 5: id,age,weight,height,gender 1,22,180,72,m 2,13,100,67,f 3,5,40,40,f 4,6,42,,f 5,12,98,66, 6,50,255,60,m I'm using the "By" function like this: list1 <- by(dataset[c("weight", "height")], dataset[c("age", "gender")], colMeans,...

...a certain situational condition exists ("stereotype threat"). N = 160, 80 female & 80 male participants, 82 under stereotype threat and 78 not. I realize that it makes no sense to report/interpret a main effect of stereotype threat in the confirmed presence of the interaction effect GENDER:STTHREAT, because a main effect of stereotype threat would actually be caused by the interaction (an error-bar plot illustrating this can be found here if one scrolls a little downwars: http://myowelt.blogspot.com/2008/05/obtaining-same-anova-results-in-r-as-in.html) . I thus tend to use Type-II...