search for: factor5

...ollowings: I'm doing factor analysis and I need to extract the loading values and the Proportion Var and Cumulative Var values one by one. Here is what I am doing: > fact <- factanal(na.omit(gnome_freq_r2),factors=5); > fact$loadings Loadings: Factor1 Factor2 Factor3 Factor4 Factor5 b1freqr2 0.246 0.486 0.145 b2freqr2 0.129 0.575 0.175 0.130 0.175 b3freqr2 0.605 0.253 0.166 0.138 0.134 b4freqr2 0.191 0.220 0.949 b5freqr2 0.286 0.265 0.113 0.891 0.190 b6freqr2 0.317 0.460 0.151 b7...

...deviance (pseudo R^2) of my models. I am assuming that: pseudo R^2= 1 - dev(model) / dev (null) where dev(model) is the deviance for the fitted model and dev(null) is the deviance for the null model (with the intercept only). library(nnet) full.model<- multinom(cbind(factor1, factor2 ,., factor5) ~ x1 + x2 + x3, weights=total, data=mydata) null.model<- multinom(cbind(factor1, factor2 ,., factor5) ~ +1, weights=total, data=mydata) Then I calculated pseudoR^2 = 1 - full.model$deviance / null.model$deviance I'm obtaining very low values for pseudoR^2 (there is not much diffe...

Dear *, I would like to obtain for each factor of my anova model the "response variable vs factor" plot with means and 95% Tukey HSD intervals. I would appreciate any information on how to do that. Cheers -------------------------------------------------------------------- Max MANFRIN Tel.: +32 (0)2 650 3168 IRIDIA - CoDE, CP 194/6

It has always been my understanding that deviance for GLMs is defined by; D = -2(loglikelihood(model) - loglikelihood(saturated model)) and this can be calculated by (or at least usually is); D = -2(loglikelihood(model)) As is done so in the code for 'polr' by Brian Ripley (in the package 'MASS') where the -loglikehood is minimised using optim; res <-