similar to: Formula evaluation, environments and attached packages

Hi Milan, I expect I may be able to do something about the way the terms are evaluated, to ensure the evaluation is done in the gnm namespace (while still ensuring the variables can be found!). In the meantime, I think the following will work: Mult <- gnm::Mult f <- Freq ~ Eye + Hair + Mult(Eye, Hair) gnm::gnm(f, family=poisson, data=dat) Hope that helps, Heather On Wed, Apr 29, 2015,

This is a wrapper around gnm by Turner and Firth to make fitting log-multiplicative models as convenient as possible: it provides simple functions, good starting values, jackknife or bootstrap standard errors, and direct plotting of the results. In addition, it makes it possible to identify scores from RC(M) association models, which gnm does not allow without computing the SVD yourself. It will

This is a wrapper around gnm by Turner and Firth to make fitting log-multiplicative models as convenient as possible: it provides simple functions, good starting values, jackknife or bootstrap standard errors, and direct plotting of the results. In addition, it makes it possible to identify scores from RC(M) association models, which gnm does not allow without computing the SVD yourself. It will

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

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

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

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

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

Hi everyone, I'm trying to perform the following regressions in order to compare linear vs. sigmoidal fit of the relationship between my dependent variable (y) and one explaining parameter (x2), both including the confounding effects of a third variable (x1): quasi-pois-lin <- glm(y ~ x1 + x2, family = quasipoisson(link="identity"), data=fit) quasi-pois-sig <- gnm(y ~ x1 +

Hi, I have a data set with a continuous predictor X, a factor A and a continuous dependent variable Y. I am trying to build a linear model of the form: Y = (b0 + b1*X1)*B(A) where B(A) is a constant for each level of the factor A. I am not quite sure how to formulate the appropriate model formula. If I write: Y ~ ( 1 + X)/A , I get estimates for as many constants and slopes as the number of

This post about length 1 offsets on R help seems to have been ignored (sorry deleted original email - is there a way to continue thread in this case?) https://stat.ethz.ch/pipermail/r-help/2009-February/189352.html It does seem to be a bug, in that glm does not behave as documented. In fact the same bug applies to lm as well. I don't think the suggested fix works though - Y isn't

Hi! I've been playing with MKL for a few days and I noticed the instructions in the R Installation Administration manual [1] no longer apply. It seems that since version 10.0 (the one used by the manual), libmkl_lapack.so has been renamed/split (although the official explanations seem to imply this was already the case in 10.0 [2]). As a consequence, the instructions for dynamic linking no

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

Dear R-users, I am working with R version 2.10.1. Say I have is a simple function like this: > my.fun <- function(x, mult) mult*sum(x) Now, I want to apply this function along with some other (say 'max') to a simple data.frame, like: > dat <- data.frame(x = 1:4, grp = c("a","a","b","b")) Ideally, the result would look something like

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

I received the following email note re: the vcdExtra package > A vcd update has shown that packages TIMP and vcdExtra are not > declaring their dependence on colorspace/MASS: see > > http://cran.r-project.org/web/checks/check_results_vcdExtra.html But, I can't see what to do to avoid this, nor understand what has changed in R devel. Sure enough, CRAN now reports errors in

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

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

On 21/03/2010, at 10:38 PM, Skip Montanaro wrote: >> I don't know anything about Solaris, but your paste doesn't actually >> contain any errors, just warnings (unless I'm reading "ld: warning: >> relocation error:" wrong). It might help to run make without -j until >> it fails, and then use `make VERBOSE=1` to print the exact commands >>

Dear all, I want to obtain the following result [[1]] , , 1, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 , , 2, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 ................ , , 9, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 , , 10, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 [[2]] , , 1, 1 [,1] [,2] [1,] 1 1 [2,] 1 1 , , 2, 1 [,1] [,2] [1,] 1