similar to: update fit (removing insignificant variables)

[this address is IPv6-only, and usually doesn't work, so don't.] There's another patch I have up my sleeve, that it would be nice if I didn't need to apply it by hand. Look: vorbis-tools/oggenc/encode.c --- encode.c-DIST Fri Jul 19 08:35:48 2002 +++ encode.c Fri Jan 9 07:46:27 2004 @@ -346,7 +342,7 @@ seconds = (int)(remain_time - (double)((int)remain_time/60)*60);

Hi list, I am building a regression model with categorical predictor variable coded by treatment contrasts. The summary of the regression model shows that some levels are significant while others are not. The significant ones show that they are statistically significant from the basis factor (at level 0). How do we generally deal with those insignificant levels? If they are used for

I am wondering if in a block experimental design (ex. triple square lattice), the block effect was not significant, is it all right not to include the block effect in an empirical model (even though the sampling was done from different blocks)? Or we are forced to control for the block effect in block designs anyway? Thanks, Julia

Hi, I am trying to specify a multinomial logit model using the multinom function from the nnet package. Now I add another independent variable and it halves the AIC as given by summary(multinom()). But when I call Anova(multinom()) from the car package, it tells me that this added variable is insignificant (Pr(>Chisq)=0.39). Thus, the improved AIC suggests to keep the variable but the Anova

Hello. I have a large dataset with sales pr month for 56 products with 10 months and i have tried to see how the sales are correlated using cor() This has given me a 56X56 matrix with the R value for each product pair. Most of these correlations are insignificant, and i want only to retain the instances were the R value is significant (for 10 observations it should be above 0.64) Can someone

I posted this question way down at teh end of another thread realted to an error in step, but that was stupid since it really is another matter altogether. I should have posted it separately, as I have now done. The code below creates a data.frame comprising three marginally noisy surfaces. The code below (including a fix courtesy of David Winsemius that avoids a step function error through use

Hello, I am a new R user and I am trying to estimate some generalized linear models (glm). I am trying to compare a model with a gaussian distribution and an identity link function, and a poisson model with a log link function. My problem is that while the gaussian model has significantly lower (i.e. "better") AIC (Akaike Information Criterion) most of the coefficients are not