search for: outcome3

...s <- c(18,17,15,20,10,20,25,13,12) Glm> outcome <- gl(3,1,9) Glm> treatment <- gl(3,3) Glm> f <- glm(counts ~ outcome + treatment, family=poisson()) Glm> f Call: glm(formula = counts ~ outcome + treatment, family = poisson()) Coefficients: (Intercept) outcome2 outcome3 treatment2 treatment3 3.045e+00 -4.543e-01 -2.930e-01 -4.210e-16 -3.997e-16 Degrees of Freedom: 8 Total (i.e. Null); 4 Residual Null Deviance: 10.58 Residual Deviance: 5.129 AIC: 56.76 Glm> anova(f) Analysis of Deviance Table Model: poisson, link: log Response: counts...

Hello R users I like to extract z values for x1 and x2. I know how to extract coefficents using model$coef but I don't know how to extract z values for each of independent variable. I looked around using names(model) but I couldn't find how to extract z values. Any help would be appreciated. Thanks TM ######################################################### >summary(model) Call:

...summary(glm.D93) Call: glm(formula = counts ~ outcome + treatment, family = poisson()) Deviance Residuals: 1 2 3 4 -0.67125 0.96272 -0.16965 -0.21999 5 6 7 8 -0.95552 1.04939 0.84715 -0.09167 9 -0.96656 Coefficients: Estimate Std. Error (Intercept) 3.045e+00 1.709e-01 outcome2 -4.543e-01 2.022e-01 outcome3 -2.930e-01 1.927e-01 treatment2 8.717e-16 2.000e-01 treatment3 4.557e-16 2.000e-01 z value Pr(>|z|) (Intercept) 17.815 <2e-16 *** outcome2 -2.247 0.0246 * outcome3 -1.520 0.1285 treatment2 4.36e-15 1.0000 treatment3 2.28e-15 1.0000 The final pdf output file is mostly fine: but not all of...

Anyone know how to get p-values for the t-values from the coefficients produced in vglm? Attached is the code and output ? see comment added to output to show where I need p-values + print(paste("********** Using VGAM function gamma2 **********")) + modl2<- vglm(MidPoint~Count,gamma2,data=modl.subset,trace=TRUE,crit="c") + print(coef(modl2,matrix=TRUE))

This is a basics beginner question. I attempted fitting a a Poisson GLM to data that is non-integer ( I believe Poisson is suitable in this case, because it is modelling counts of infections, but the data collected are all non-negative numbers with 2 decimal places). My question is, since R doesn't return an error with this glm fitting, is it important that the data is non-integer. How does

...ounts ~ outcome + treatment, family=poisson()) > anova(glm.D93) > summary(glm.D93,dispersion=0) #current code ...... Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.045e+00 0.000e+00 Inf <2e-16 outcome2 -4.543e-01 0.000e+00 -Inf <2e-16 outcome3 -2.930e-01 0.000e+00 -Inf <2e-16 treatment2 1.924e-08 0.000e+00 Inf <2e-16 treatment3 8.383e-09 0.000e+00 Inf <2e-16 (Dispersion parameter for poisson family taken to be 0) Null deviance: 10.5814 on 8 degrees of freedom Residual deviance: 5.1291 on 4 d...

...ould you perhaps help me? The full output is the following: > library(haplo.stats) > datafile.dat<-read.table("datafile.dat",header=TRUE) > attach(datafile.dat) > names(datafile.dat) [1] "SEXE" "AGE" "OUTCOME1" "OUTCOME2" "OUTCOME3" "OUTCOME4" [7] "OUTCOME5" "SNP1X" "SNP1Y" "SNP2X" "SNP2Y" "SNP3X" [13] "SNP3Y" "SNP4X" "SNP4Y" "SNP5X" "SNP5Y" "SNP6X" [19] "SNP6Y" "SNP7X" &q...

I keep coming back to this problem of singular fits in rlm (MASS library), but cannot figure out a good solution. I am fitting a linear model with a factor variable, like lm( Y ~ factorVar) and this works fine. lm knows to construct the contrast matrix the way I would expect, which puts the first factor as the baseline level. But when I try rlm( Y ~ factorVar) I get the message "'x'

I am attempting to run a glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual

I have a variable which is roughly age categories in decades. In the original data, it came in coded: > str(xxx) 'data.frame': 58271 obs. of 29 variables: $ issuecat : Factor w/ 5 levels "0 - 39","40 - 49",..: 1 1 1 1... snip I then defined issuecat as ordered: > xxx$issuecat<-as.ordered(xxx$issuecat) When I include issuecat in a glm model, the result