similar to: [R] length 1 offset in glm (& lm)

Hi, I have run into another problem using offsets, this time with the predict function, where there seems to be a contradiction again between the behavior and the help page. On the man page for predict.lm, it says Offsets specified by offset in the fit by lm will not be included in predictions, whereas those specified by an offset term in the formula will be. While it indicates nothings about

Hi, I'm trying to use an offset with glm. According to the glm man page offset ... This should be NULL or a numeric vector of length either one or equal to the number of cases. ... but with the following example, I get an error if the offset is of length 1 c1 <- structure(list(Contr = c(0.028, 0.043, 0.064, 0.097, 0.146, 0.219 + ), Correct = c(34L, 57L, 94L, 152L, 160L, 160L),

I have found the "simulate" method (incorporated in some packages) very handy. As far as I can tell the only class for which simulate is actually implemented in base R is lm ... this is actually a little dangerous for a naive user who might be tempted to try simulate(X) where X is a glm fit instead, because it defaults to simulate.lm (since glm inherits from the lm class), and the

Hi! Some time ago, I replaced calls to library() with calls to requireNamespace() in my package logmult, in order to follow the new CRAN policies. But I just noticed it broke jackknife/bootstrap using several workers via package parallel. The reason is that I'm running model replicates on the workers, and the formula includes non-standard terms like Mult() which are provided by gnm. If gnm

In previous versions of the gnm package, the terms component of "gnm" objects had a "classID" attribute. This caused problems when used with str as the following simple example illustrates: > x <- 1 > attr(x, "classID") <- "type1" > str(x) Class 'type1' Class 'type1' Class 'type1' Class 'type1' Class

Hi all, I'm trying to fit a nonlinear (logistic-like) regression, and I'd like to get some recommendations for which package to use. The expression I want to fit is something like: y ~ A * exp(X * Beta1) / (1 + exp(-(x + X * Beta2 - xmid)/scal)) Basically, it's a logistic function, but I want to be able to modify the saturation amplitude by a few parameters (Beta1) and shift the

Hi all, I'm not familiar with R programming and I'm trying to reproduce a result from a paper. Basically, I have a dataset which I would like to model in terms of successive increments, i.e. (y denote empirical values of y) y_1 = y1, y_2 = y1 + delta1, y_3 = y1 + delta1 + delta2. ... y_m = y1 + sum_2^m delta j where delta_j donote successive increments in the y-values, i.e. delta

Hello, Has anyone written R functions for applying self-controlled case series methods (http://statistics.open.ac.uk/sccs/). In fact only thing needed is to modify glm function to allow absorption of effect. Eg. in Poisson model individual effect is used as factor, but it is considered as nuisance term where parameter estimates are not needed. Could anyone point how absorbing individual

This bug is not quite fixed - the example from my original report now = works using R-2.2.1, but plot(Uniform, 6) does not. The bug is due to if (show[6]) { ymx <- max(cook, na.rm =3D TRUE) * 1.025 g <- hatval/(1 - hatval) # Potential division by zero here # plot(g, cook, xlim =3D c(0, max(g)), ylim =3D c(0, ymx),=20 main =3D main, xlab =3D

Hello R-helpers, I would like to fit a non-linear function to data (Discrete X axis, over-dispersed Poisson values on the Y axis). I found the functions gnlr in the gnlm package from Jim Lindsey: this can handle nonlinear regression equations for the parameters of Poisson and negative binomial distributions, among others. I also found the function nls2 in the software package

Hi everyone! In a package I'm developing, I have created a custom function to get jackknife standard errors for the parameters of a gnm model (which is essentially the same as a glm model for this issue). I'd like to add support for bootstrap using package boot, but I couldn't find how to proceed. The problem is, my data is a table object. Thus, I don't have one individual per

Hello everyone, I am currently teaching an intermediate stats. course at UCSD Extension using R. We are using Venables and Ripley as the primary text for the course, with Freund & Wilson's Statistical Methods as a secondary reference. I recently gave a homework assignment on logistic regression, and I had a question about glm. Let n be the number of trials, p be the estimated

Is there any logical reason why glm prints out the labels of factor levels after variable names when baseline contrasts (contr.treatment) are used but the codes for the levels when mean contrasts (contr.sum) are used? Jim -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Hi all, Trying to understand the logistic regression performed by glm (i.e. when family='binomial'), and I'm curious to know how it treats perfect success. That is, lets say I have the following summary data x=c(1,2,3,4,5,6) y=c(0,.04,.26,.76,.94,1) w=c(100,100,100,100,100,100) where x is y is the probability of success at each value of x, calculated across w observations.

Hi all, If presented with a singular design matrix, lm drops columns to make the design matrix non-singular. What algorithm is used to select which (and how many) column(s) to drop? Particularly, given a factor, how does lm choose levels of the factor to discard? Thanks for the help. Best, Anirban [[alternative HTML version deleted]]

In checking my vcdExtra package, the following NOTE newly appeared (R-Forge, using R version 3.0.1 Patched (2013-08-20 r63635)) Package in Depends field not imported from: ?gnm? These packages needs to imported from for the case when this namespace is loaded but not attached. In the DESCRIPTION file, I have Depends: R (>= 2.10), vcd, gnm (>= 1.0.3) In NAMESPACE: # we are a vcd

Full_Name: Heather Turner Version: 2.2.0 OS: Windows XP Submission from: (NULL) (137.205.240.44) Standardized residuals as calculated by rstandard.lm, rstandard.glm and plot.lm are Inf/NaN rather than zero when the un-standardized residuals are zero. This causes plot.lm to break when calculating 'ylim' for any of the plots of standardized residuals. Example:

glm(A~B+C+D+E+F,family = binomial(link = "logit"),data=tre,na.action=na.omit) Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels however, glm(A~B+C+D+E,family = binomial(link = "logit"),data=tre,na.action=na.omit) runs fine glm(A~B+C+D+F,family = binomial(link =

Hi R-Helpers: I?m trying to fit the same linear model to a bunch of variables in a data frame, so I was trying to adapt the codes John Fox, Spencer Graves and Peter Dalgaard proposed and discused yesterday on this e-mail list: for (y in df[, 3:5]) { mod = lm(y ~ Trt*Dose, data = x, contrasts = list(Trt = contr.sum, Dose = contr.sum)) Anova(mod, type = "III") } ## by John Fox or for

Dear R-helpers, I try to perform glm's with negative binomial distributed data. So I use the MASS library and the commands: model_1 = glm.nb(response ~ y1 + y2 + ...+ yi, data = data.frame) and predict(model_1, newdata = data.frame) So far, I think everything should be ok. But when I want to perform a glm with a subset of the data, I run into an error message as soon as I want to predict