search for: sexmales

Dear R users, I am using lmer combined with AIC model selection and averaging (in the MuMIn package) to try and assess how isotope values (which indicate diet) vary within a population of animals. I have multiple measures from individuals (variable 'Tattoo') and multiple individuals within social groups within 4 locations (A, B, C ,D) crucially I am interested if there are

Greetings, I am working in the sem package on a model with 3 exogenous variables (2 are nominal-categorical), and 4 endogenous, continuous variables. To use sem with the nominal variables, I created dummy variables. Now, in my sem output I have estimates for path coefficients for the relationship between each level of the nominal variables and the endogenous variables they are associated

Hello R experts I have two linear regressions for sexes (Male, Female, Unknown). All have a good correlation between body length (response variable) and head length (explanatory variable). I know it is not recommended, but for a good practical reason (the purpose of study is to find a single conversion factor from head length to body length), the regressions need to go through the origin (0

Hi I would really appreciate some help with my code for coxme... My data set I'm interested in survival of animals after an experiment with 4 treatments, which was performed on males and females. I also have two random factors: Response variable: survival (death) Factor 1: treatment (4 levels) Factor 2: sex (male / female) Random effects 1: person nested within day (2 people did

Am I correct in assuming that the output below essentially translates to "Males have a mean time that is significantly lower than Females"? Is this the correct way to interpret the fact that the coefficient is negative? Assume the variale sex is treated as a factor with Female =0 and Male=1. survmodel<-survreg(survobj~sex,data=data1, dist="weibull")

Hello Everyone! It's been a while since I last posted a question! Hope everyone has been doing well! ~~~ CONTEXT ~~~ I have recently entered a beginner-level competition on kaggle. The goal of the competition is to build a model that predicts who did/did not survive on the Titanic. I decided to use random forests as I have been wanting to learn the algorithm and the competition

...om that object. Is there a way to retrieve the AIC from a coxme object? Please cc me on your response. Thanks, Chris 1> str(fit1.full) List of 20 $ coefficients :List of 2 ..$ fixed : Named num [1:5] 0.197 -1.132 -0.499 0.114 -0.329 .. ..- attr(*, "names")= chr [1:5] "sexMALES" "lifedxmCONTROL" "lifedxmMAJOR" "sexMALES:lifedxmCONTROL" ... ..$ random:List of 1 .. ..$ famid: Named num 0.964 .. .. ..- attr(*, "names")= chr "Intercept" $ frail :List of 1 ..$ famid: Named num [1:97] -0.8144 -0.6222 -0.0...

I have a process where I am creating a effects plot similar to the cowles effect example. I would like to add the point estimates to the effects plot, can someone show me the correct syntax. I have included the "R" effects example, so you can show me the correct syntax. Thanks mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial)

Dear list members, I fitted a glm model (See output below). My outcome is death, and weight (continuous), ClutchSize (3-level factor), EggVolume (continuous), Sex (obviously 2-level factor), and SiblingCompetence (2-level factor) are my covariates. I'd like to obtain the odds of death for a range of Weights, EggVolumes, and different combinations of ClutchSize. I've tried using the

Dear R-Help, My problem/bug came to light,when fitting a linear model using stepwise selection. I'd started with the straightfoward command step(lm(y~., dataset)) This worked fine, but because this starts with all the possible explanatory variables, it results in a model with too many explanatory variables. Hence I wanted to start with just a constant and do forward selection, to get a

I'm very new to R and modeling but need some help with visualization of glms. I'd like to make a graph of my glms to visualize the different effects of different parameters. I've got a binary response variable (bird sightings) and use binomial glms. The 'main' response variable is a measure of distance to a track and the parameters I'm testing for are vegetation parameters

Dear all, I am facing some problem with how to fit a "Baseline category Logit model" with R. Basically I am considering famous "Alligator" data as discussed by Agresti. This data can also be found here: https://onlinecourses.science.psu.edu/stat504/node/174 (there is also an accompanying R file, however the underlying R code could not load the data properly!!!) Below are

Unsubscribe -----Original Message----- From: r-help-request at r-project.org Date: Wed, 05 May 2010 12:00:09 To: <r-help at r-project.org> Subject: R-help Digest, Vol 87, Issue 5 Send R-help mailing list submissions to r-help at r-project.org To subscribe or unsubscribe via the World Wide Web, visit https://stat.ethz.ch/mailman/listinfo/r-help or, via email, send a message with subject