search for: varseq

...1.0 Y4 1 1695.7 342 7883.1 0.0 I(X4^2) 1 1992.2 341 5890.9 0.0 I(Y4^2) 1 5687.4 340 203.5 0.0 Warning messages: 1: tests made without re-estimating theta in: anova.negbin(glm4.nb) 2: Algorithm did not converge in: method(x = x[, varseq <= i, drop = FALSE], y = object$y, weights = object$prior.weights, 3: Algorithm did not converge in: method(x = x[, varseq <= i, drop = FALSE], y = object$y, weights = object$prior.weights, 4: Algorithm did not converge in: method(x = x[, varseq <= i, drop = FALSE], y = object$y, weights =...

...ondF 1 0.1 8222 7398.5 0.7 Biom 1 0.2 8221 7398.3 0.7 LT:CondF 1 142.1 8220 7256.3 9.413e-33 LT:Biom 1 70.4 8219 7185.8 4.763e-17 Warning message: fitted probabilities numerically 0 or 1 occurred in: method(x = x[, varseq <= i, drop = FALSE], y = object$y, weights = object$prior.weights, I am having a little difficulty interpreting these results. The result from the fit tells me that all predictors are significant, while the anova indicates that besides LT (the main variable), only the interaction of the oth...

Is anybody working on a way to standardize the creation of "newdata" objects for predict methods? When using predict, I find it difficult/tedious to create newdata data frames when there are many variables. It is necessary to set all variables at the mean/mode/median, and then for some variables of interest, one has to insert values for which predictions are desired. I was at a

...orm) > anova(lm,nBoot=500) > plot(lm) However, because these are count data, I am interested in fitting a generalized linear model instead, but then I get following error: > glm<-manyglm(mvform,family="negative.binomial") > anova(glm,nBoot=500) Error in XvarIn[nterms - i, varseq > i] <- 0 : (subscript) logical subscript too long Also, when trying to create diagnostics plots, I get the following error. > plot(glm) Error in plot.window(...) : need finite 'xlim' values In addition: Warning messages: 1: In default.plot.manyglm(x, which = which, caption = c...

...om: (NULL) (138.246.7.137) In contrast to anova.lm, which works without storing components 'model', 'x' and 'y' in the fitted lm-object, anova.glm throws an error if y = FALSE and if there is more than one covariate in the corresponding glm() call: Error in method(x = x[, varseq <= i, drop = FALSE], y = object$y, weights = object$prior.weights, : NAs in V(mu) Reproduce this by e.g.: fit <- glm(1:10 ~ I(1:10) + I((1:10)^2), y = FALSE) anova(fit) This is not nice since it is not clear that the error inherits from the missing 'y' in the glm object. This d...

....nb) meanvar.plot(flormat~transect, col=as.numeric (vegtype)) #direct plot of mean-variace relationship anova(abund.nb, p.uni="adjusted") It's after running the last code line [>anova(abund.nb, p.uni="adjusted")], when I get thiis error: Error in XvarIn[nterms - i, varseq > i + minterm] <- 0 : (subscript) logical subscript too long Any idea what I'm doing wrong? I've tried shortening the length of all files, but didn't matter. Thanks so much for your time!! Cheers, Andr?s errorrunningmvabund.zip <http://r.789695.n4.nabble.com/file/n466...