similar to: Specifying xlevels in effects library

I'm working on predict.survreg and am confused about xlevels. The model.frame method has the argument, but none of the standard methods (model.frame.lm, model.frame.glm) appear to make use of it. The documentation for model.matrix states: xlev: to be used as argument of model.frame if data has no "terms" attribute. But the terms attribute has no xlevels information in it, so I

Hi, I am trying to interpret the coefficients in the model: RateOfMotorPlay ~ TestNumber + Sex + TestNumber * Sex where there are thee different tests and Sex is (obviously) binary. My results are: Residuals: Min 1Q Median 3Q Max -86.90 -26.28 -7.68 22.52 123.74 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 29.430 6.248

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

Hello, I am trying to get the P values from the output of a summary for lm. lm <- lm(y ~ age + sex) s <- summary(lm) I thought that I might be able to get them using a combination of scan, grep and sub. But I got stuck on the first step - being able to process "s" as a text string. I could perhaps write it to file than scan it back but there is probably an easier way to do

Dear R devel I've been wondering about this for a while. I am sorry to ask for your time, but can one of you help me understand this? This concerns duplicated labels, not levels, in the factor function. I think it is hard to understand that factor() fails, but levels() after does not > x <- 1:6 > xlevels <- 1:6 > xlabels <- c(1, NA, NA, 4, 4, 4) > y <- factor(x,

Hi, Some try: > names(mi$xlevels) [1] "f" > all.vars(mi$formula) [1] "D" "x" "f" "Y" > names(mx$xlevels) [1] "f" > all.vars(mx$formula) [1] "D" "x" "f" When offset is indicated out of the formula, it does not work... Marc Le 07/03/2018 ? 06:20, Bendix Carstensen a ?crit?: > I would like

Dear list, I would like to expand a DF with all the missing levels of a variable. a <- c(2,2,3,4,5,6,7,8,9) a.cut <- cut(a, breaks=c(0,2,6,9,12), right=FALSE ) (x <- data.frame(a, a.cut)) # In 'x' the level "[0,2)" is "missing". AddMissingLevel <- function(xdf) { xfac <- factor( c("[0,2)", "[2,6)", "[6,9)",

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. >

Hello, Everybody: This may not be a "bug", but for me it is an unexpected outcome. A factor variable's levels do not retain their ordering after the levels function is used. I supply an example in which a factor with values "BC" "AD" (in that order) is unintentionally re-alphabetized by the levels function. To me, this is very bad behavior. Would you agree? #

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)

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

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

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 R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values

all.vars works fine, EXCEPT, it give a bit too much. I only want the regression variables, but in the following example I also get "k" the variable holding the chosen knots. Any machinery to find only "real" regression variables? cheers, Bendix library( splines ) y <- rnorm(100) x <- rnorm(100) k <- -1:1 ml <- lm( y ~ bs(x,knots=k) ) mg <- glm( y ~

I'm trying to develop some graphic methods for glm objects, but they only apply for models where all predictors are discrete factors. How can I test for this in a function, given the glm model object? That is, I want something that will serve as an equivalent of is.discrete.glm() in the following context: myplot.glm <- function(model, ...) { if (!inherits(model,"glm"))

I would like to extract the names, modes [numeric/factor] and levels of variables needed in a data frame supplied as newdata= argument to predict.glm() Here is a small example illustrating my troubles; what I want from (both of) the glm objects is the vector c("x","f","Y") and an indication that f is a factor: library( splines ) dd <- data.frame( D =

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

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 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