similar to: Dynamic Labels in Forms

Hi All, I'm perplexed by the way the unclass function displays a factor whose labels have been swapped with the relevel function. I realize it won't affect any results and that the relevel did nothing useful in this particular case. I'm just doing it to learn ways to manipulate factors. The display of unclass leaves me feeling that the relevel had failed. I've checked three books

Hello, I am trying to get the same values for the adjusted means and standard errors using R that are given in SAS for the following data. The model is Measurement ~ Age + Gender + Group. I can get the adusted means at the mean age by using predict. I do not know how to get the appropriate standard errors at the adjusted means for Gender using values from predict. So I attempted to get them

On Tue, Oct 13, 2015 at 12:35:07PM -0400, Aaron Ballman via llvm-dev wrote: > On Tue, Oct 13, 2015 at 12:21 PM, Renato Golin via llvm-dev > <llvm-dev at lists.llvm.org> wrote: > > On 13 October 2015 at 16:41, Rafael Espíndola <llvm-dev at lists.llvm.org> wrote: > >>> - *Be welcoming.* We strive to be a community that welcomes and supports > >>>

This is a follow-up on my note of Saturday. Let me start with two important clarifications - I think this would be a nice addition, but I've had exactly one use for it in the 15+ years of developing the survival package. - I have a work around for the current case. Prioritize accordingly. The ideal would be to change survexp as follows: fit <- survexp( ~ gender,

I'm working with a nested model (mixed). I have four factors: Patients, Tissue, sex, and tissue_stage. Totally I have 10 patients, for each patient, there are 2 tissues (Cancer vs. Normal). I think Tissue and sex are fixed. Patient is nested in sex,Tissue is nested in patient, and tissue_stage is nested in Tissue. I tried aov and lme as the following, > aov(gene ~ tissue + gender +

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 guys, I''m new to ruby and rails and I''m working on multi model forms, specifically 3. I''m using this http://guides.rubyonrails.org/getting_started.html as a start, and its got a 2 Model example but I cant seem to get the last one working. These are my models: Country name:string code:string (has_one :address) Address address_line1:string

Hello All R Users, Function loglm() in library MASS can be cajoled to accomodate structural zeros in a cross-classification table. An example from Fienberg demonstrates how this can be done. My question is: Can the function glm() perform the same task? Can glm() estimate a log-linear model with fixed zeros like loglm()? Thanks for your help, Andrew ## Fienberg, The Analysis of Cross-Classified

I am trying to import a SPSS.sav file into R. The attached file is not technically the file I am trying to import, but does replicate my problem. The actual file is much too large to attach. No matter what I do, I can not get R (base or Hmisc) to apply the value labels in the .sav file to the dataframe created in R. Here's the code that I am using. maine <- spss.get("test.sav") #

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 <-

On 13 October 2015 at 16:41, Rafael Espíndola <llvm-dev at lists.llvm.org> wrote: >> - *Be welcoming.* We strive to be a community that welcomes and supports >> people of all backgrounds and identities. This includes, but is not limited >> to members of any race, ethnicity, culture, national origin, colour, >> immigration status, social and economic class, educational

Hi, I'm analysing some anthropometric data on fifty odd skull bases. We know the gender of each skull, and we are trying to develop a predictor to identify the sex of unknown skulls. Rpart with cross-validation produces two models - one of which predicts gender for Males well, and Females poorly, and the other does the opposite (Females well, and Males poorly). In both cases the error

I'm trying to obtain within-group correlations on a subset of variables. I first selected my variables using the following command: mydata$x<-mydata[c("iq","education","achievement")] I'd like to look at correlations among those variables separately for men and women. My gender variable in mydata is coded 1 (women) and 0 (men). I have successfully used

Hello, The dataset "cats" contain information about the heart weight ("Hwt"), body weight ("Bwt") and gender ("Sex") of a group of 144 cats. I write the following piece of code: library(MASS)attach(cats)ratio <- Hwt/Bwtmale <- ratio[Sex == "M"]female <- ratio[Sex == "F"] My question is, when I look at the object

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

I apologize in advance if this is the wrong forum for this report/request, and for the fact that I have not read the code for ftable.formula in any detail. >From reading the documentation for ftable.formula, I expected that the following two calls to ftable would produce the same results: data(UCBAdmissions) ftable(UCBAdmissions, row.vars = "Dept", col.vars = c("Gender",

In response to those who asked for a better explanation of what the Vilno software does, here's a simple example that gives some idea of what it does. LABRESULTS is a dataset with multiple rows per patient , with lab sodium measurements. It has columns: PATIENT_ID, VISIT_NUM, and SODIUM. DEMO is a dataset with one row per patient, with demographic data. It has columns: PATIENT_ID, GENDER.

This may be a really obvious question but I just can't figure out how to do it. I have a small dataset that I am trying to compare to some controls. It is essential that the controls are matched on Cancer Stage (a numerical factor between 1 and 4), and then ideally on Age (integer), Gender (factor), Performance Status(factor). I'm using matchit to try and do this, but it seems to give

Let''s say I have three hypothetical MySQL tables: ? people, with columns id, gender, and source_id belongs_to :boys and :girls ? boys, with columns id and name has_many :people ? girls, with columns id and name has_many :people The gender column in people specifies which of the two source tables the source_id refers to. For example, if we have values: 1, boy, 1 in people,

Hi I am trying to determine the mean of a Weibull function that has been fit to a data set, adjusted for a categorical covariate , gender (0=male,1=female). Here is my code: library(survival) survdata<-read.csv("data.csv") ##Fit Weibull model to data WeiModel<-survreg(Surv(survdata$Time,survdata$Status)~survdata$gender) summary(WeiModel) P<-pweibull(n,