similar to: Test For Difference of Betas By Group in car

I don?t understand why you don?t include the full text of the error. ? David Sent from my iPhone > On Jan 19, 2025, at 10:00?AM, Sparks, John via R-help <r-help at r-project.org> wrote: > > ?Hello R-Helpers, > > I was looking into how to test whether the beta coefficient from a regression would be the same for two different groups contained in the dataset for the

Sent from my iPhone > On Jan 19, 2025, at 1:57?PM, David Winsemius <dwinsemius at comcast.net> wrote: > > ?I don?t understand why you don?t include the full text of the error. > > ? > David > Sent from my iPhone > >> On Jan 19, 2025, at 10:00?AM, Sparks, John via R-help <r-help at r-project.org> wrote: >> >> ?Hello R-Helpers, >>

Hi, I'm sure this is simple, but I haven't been able to find this in TFM, say I have some data in R like this (pasted here: http://pastebin.com/raw.php?i=sjS9Zkup): > head(df) gender age smokes disease Y 1 female 65 ever control 0.18 2 female 77 never control 0.12 3 male 40 state1 0.11 4 female 67 ever control 0.20 5 male 63 ever state1 0.16

I am working with a RCB 2x2x3 ANCOVA, and I have noticed a difference in the calculation of sum of squares in a Type III calculation. Anova output is a follows: > Anova(aov(MSOIL~Forest+Burn*Thin*Moisture+ROCK,data=env3l),type=3) Anova Table (Type III tests) Response: MSOIL Sum Sq Df F value Pr(>F) (Intercept) 22.3682 1 53.2141 3.499e-07 *** Forest

Hi there I'm trying to fit a logistic regression model to data that looks very similar to the data in the sample below. I don't understand why I'm getting this error; none of the data are proportional and the weights are numeric values. Should I be concerned about the warning about non-integer successes in my binomial glm? If I should be, how do I go about addressing it? I'm

For some time I have been looking for a convenient way of performing post-hoc analysis to Repeated Measures ANOVA, that would be acceptable if sphericity is violated (i.e. leaving aside post-hoc to lme models). The best solution I found was John Fox's proposal to similar requests in R-help: http://tolstoy.newcastle.edu.au/R/e2/help/07/09/26518.html

Dear list, I use Sweave almost exclusively for writing papers, and I have become quite spoiled by the excellent xtable export facilities. Has anybody written an xtable method for the Anova function in CAR, or has anybody used a different set of functions to import Anova results into a table in an Sweave document? If not, any handy hints on how to write a good homebrew based on the output of Anova

Hi everyone, I'm sure this is pretty basic but I couldn't find a clear example of how to do this. I'm running a regression, say: reg <- lm(Y ~ x1 + year) where x1 is a continuous variable and year is a factor with various year levels. Individually, each year factor variable is not significant, but I have a suspicion they are jointly significant. I can't figure out how to run

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

Dear All, I am learning the ropes about logistic regression in R. I found some interesting examples http://bit.ly/Vq4GgX http://bit.ly/W9fUTg http://bit.ly/UfK73e but I am a bit lost. I have several questions. 1) For instance, what is the difference between glm.out = glm(response ~ poverty + gender, family=binomial(logit), data=mydata) and glm.out = glm(response ~ poverty * gender,

Dear list, I would like to run an ANCOVA using car::Anova with repeated measures factors, but I can't figure out how to do it. My (between-subjects) covariate always interacts with my within-subject factors. As far as I understand ANCOVA, covariates usually do not interact with the effects of interest but are simply additive (or am I wrong here?). More specifically, I can add a covariate as

Forgive me if this is a trivial question, but I couldn't find it an answer in former forums. I'm trying to reproduce some SAS results where they set two parameters equal. For example: y = b1X1 + b2X2 + b1X3 Notice that the variables X1 and X3 both have the same slope and the intercept has been removed. How do I get an estimate of this regression model? I know how to remove the intercept

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

Hello Helpers, I have some problems with fitting the model for my data... -->my Literatur says (crawley testbook)= Non-normality of errors-->I get a banana shape Q-Q plot with opening of banana downwards Structure of data: origin wt pes gender 1 wild 5.35 147.0 male 2 wild 5.90 148.0 male 3 wild 6.00 156.0 male 4 wild 7.50 157.0 male 5 wild 5.90

Dear all If I run the model and get the estimated parameter a11. Then I want to use the estimated parameter to calculate the elasticities by using the formula e11=a11/mw1-1. What I have done is using the command of linear. Hypothesis. > formulas1=dWfresh~dlnPfresh+dlnPfrozen+dlnPsmoke+dlnQP+cosL1+sinL1+cosL2 >

Hi all, Could somebody be so kind to explain to me what is the saturated model on which deviance and degrees of freedom are calculated when fitting a binomial glm? Everything makes sense if I fit the model using as response a vector of proportions or a two-column matrix. But when the response is a factor and counts are specified via the "weights" argument, I am kind of lost as far as

Hi R-helpers, I was trying to put gender='Male' in newdata to create a expected survival curve for a pseudo cohort by using survfit based on Cox regression. My codes are shown below: fit<- coxph(Surv(end, status2)~gender, data=wlwsn1) Summary(fit) coef exp(coef) se(coef) z p genderMale 0.204 1.23 0.0912 2.23 0.025

Dear R fans, I have got a difficult sounding problem. For fitting a linear model using continuous response and then for re-fitting the model after excluding every single variable, the following functions can be used. library(MASS) model = lm(perf ~ syct + mmin + mmax + cach + chmin + chmax, data = cpus) dropterm(model, test = "F") But I am not sure whether any similar functions is

Dear friends, Sorry for this somewhat generically titled posting but I had a question with using contrasts in a manova context. So here is my question: Suppose I am interested in doing inference on \beta in the case of the model given by: Y = X %*% \beta + e where Y is a n x p matrix of observations, X is a n x m design matrix, \beta is m x p matrix of parameters, and e is a

Hello, I am work with a linear regression model: y=ax+b with the function of lm. y= observed migration distance of butterflies x= predicted migration distance of butterflies Usually the result will show if the linear term a is significantly different from zero based on the p-value. Now I would like to test if the linear term is significantly different from one. (because I want to know