similar to: Interpreting coefficients in linear models with interaction terms

Having spent the last few weeks trying to decipher R, I feel I may finally be getting somewhere, but i'M still in need of some advice and all my tutors seem to be on holiday! Basically a bit of background, I have data collected on a population of Lizards which includes age,sex, and body condition. I collected data myself this year and I have data previously collected from 1999, 2002 and

Dear list, I am new to this list and I am new to the world of R. Additionally I am not very firm in statistics either but have to deal. So here is my problem: I have a dataset (which I attach at the end of the post) with a binomial response variable (alive or not) and three fixed factors (trapping,treat,sex). I do have repeated measures and would like to include one (enclosure) random factor. I

Dear helpers, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Dear all, How to obtain the odds ratio (OR) and 95% confidence interval (CI) with 1 standard deviation (SD) change of a continuous variable in logistic regression? for example, to investigate the risk of obesity for stroke. I choose the happening of stroke (positive) as the dependent variable, and waist circumference as an independent variable. Then I wanna to obtain the OR and 95% CI with

Hi i would like to use some graphs or tables to explore the data and make some sensible guesses of what to expect to see in a glm model to assess if toxin concentration and sex have a relationship with the kill rate of rats. But i cant seem to work it out as i have two predictor variables~help?Thanks.:) Here's my data. >

Hi, R or S+ users, I want to make a simple transformation for the model, but for the whole design matrix. The model is distance ~ age * Sex, where Sex is a factor. So the design matrix may look like the following: (Intercept) age SexFemale age:SexFemale 1 1 8 0 0 2 1 10 0 0 3 1 12 0 0 4

Hi folks... check this out.. > GLU<-lme(gluc~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ + haartd+hivdur+factor(arv), + random= ~rt|id, na.action=na.omit) > intervals(GLU)$fixed lower est. upper (Intercept) 67.3467070345 7.362307e+01 7.989944e+01 rt *0.0148050160* 6.249304e-02 1.101811e-01 cd4

Hi, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for females using the dose.p() command in the MASS

Dear all: "predict.glm" provides an example to perform logistic regression when the response variable is a tow-columned matrix. I find some paradox about the degree of freedom . > summary(budworm.lg) Call: glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375

library(effects) mod <- lm(Measurement ~ Age + Sex, data=d) e <-effect("Sex",mod) The effect is evaluated at the mean age. > e Sex effect Sex F M 43.33083 44.48531 > > e$model.matrix (Intercept) Age SexM 1 1 130.5859 0 23 1 130.5859 1 To evaluate the effect at Age=120 I tried: e

Full_Name: Soren Feodor Nielsen Version: 2.5.0 OS: linux-gnu Submission from: (NULL) (130.225.103.21) The print-out of xtable in the following example is wrong; instead of yielding the correct ci's for the second model it repeats the ci's from the first model. require(xtable) require(MASS) data(cats) b1<-lm(Hwt~Sex,cats) b2<-lm(Hwt~Sex+Bwt,cats)

I am struiggling a bit with this function 'hatvalues'. I would like a little more undrestanding than taking the black-box and using the values. I looked at the Fortran source and it is quite opaque to me. So I am asking for some help in understanding the theory. First, I take the simplest case of a single variant. For this I turn o John Fox's book, "Applied Regression Analysis

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

Dear R Users, I am having trouble with lmer. I am looking at recombinant versus non recombinant individuals. In the response variable recombinant individuals are coded as 1's and non-recombinant as 0's. I built a model with 2 fixed factors and 1 random effect. Sex (males/females) is the first fixed effect and sexual genotype (XY, YY, WX and WY) the second one. Sexual Genotype is

Hi, I would like to know whether it is possible to obtain a value of significance for random effects when aplying the lme or related functions. The default output in R is just a variance and standard deviation measurement. I feel it would be possible to obtain the significance of these random effects by comparing models with and without these effects. However, I'm not used to perform

Hello, I am new to R and have tried to search similar questions but could not find exactly what I am looking for, but I apologize if the question was already asked. I have 10 different treatments and want to know whether they affect the sex ratios of insect emergence. After running the glms I got this table: Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL

I have a question about some strange results I get when using lmer to build a logistic mixed effects model. I have a data set of about 30k points, and I'm trying to do backwards selection to reduce the number of fixed effects in my model. I've got 3 crossed random effects and about 20 or so fixed effects. At a certain point, I get a model (m17) where the fixed effects are like this

Hi All, I'm fitting an proportional odds model using vglm() from VGAM. My response variable is the severity of diseases, going from 0 to 5 (the severity is actually an ordered factor). The independent variables are: 1 genetic marker, time of medical observation, age, sex. What I *need* is a p-value for the genetic marker. Because I have ~1.5 million markers I'd rather not faffing

Hello! I am trying to run a GLMM using LME4, and keep getting the warning message: "In mer_finalize(ans) : false convergence (8)" I am quite new to R, and in looking into this thus far, it appears that there are a variety of reasons why this might occur, such as needing to standardize some parameters or if all subjects in one combination of parameters all have the same outcome. I also