similar to: MICE data analysis with glmulti

Dear List, I'm using glmulti for model averaging in R. There are ~10 variables in my model, making exhaustive screening impractical - I therefore need to use the genetic algorithm (GA) (call: method = "g"). I need to include random effects so I'm using glmulti as a wrapper for lme4. Methods for doing this are available here

Dear all, I have a question about the glmulti package. I want to include some variables in all models. To that end I applied the wrapper function as shown in the examples (http://www.inside-r.org/packages/cran/glmulti/docs/glmulti). To include the variable "Geslacht" in all models: > glm.redefined = function(formula, data, always="", ...)

I have many "sample" datasets (e.g. sample 5, sample 6, etc), each identified by a number as a suffix. These datasets are saved as individual R objects on my hard drive. (e.g."Wind.5.r" . "Wind.6.r","Solar.5.r","Solar.6.r") For example purposes, I have written code that creates similar data files using the "airquality" dataset. (see

This seems to be due to changes made by Martin Maechler in 2008. Presumably this fixed something, but it escapes my memory. However, it seems to have broken the equivalence between within.list and within.data.frame, so now within.list <- within.data.frame does not suffice. The crux of the matter seems to be that both the following constructions work for data frames > aq <-

> On 26 Jun 2017, at 19:04 , Martin Maechler <maechler at stat.math.ethz.ch> wrote: > >>>>>> peter dalgaard <pdalgd at gmail.com> >>>>>> on Mon, 26 Jun 2017 13:43:28 +0200 writes: > >> This seems to be due to changes made by Martin Maechler in >> 2008. Presumably this fixed something, but it escapes my >> memory. >

Dear list, I am trying to do an automated model selection of a glmm (function glmer; package: lme4) containing a large number of predictors. As far as i understand, glmulti is able to devide the process into chuncks and proceed by parallel processing on on multiple cores. Unfortunately this does not seem to work and i could not really fid any advice on the matter on other forums. Specifically i

>>>>> "PD" == Peter Dalgaard <pdalgd at gmail.com> >>>>> on Mon, 26 Jun 2017 20:12:38 +0200 writes: >> On 26 Jun 2017, at 19:04 , Martin Maechler >> <maechler at stat.math.ethz.ch> wrote: >> >>>>>>> peter dalgaard <pdalgd at gmail.com> on Mon, 26 Jun

Hello, I'm using the "glmulti" package to run models of all the possible combinations of my variables. However, I am only interested in a few interactions between my variables. I have tried the equivalent of: mod1<-lm(y~a+b+c+a:b) glmulti(mod1, level=1) mod2<-lm(y~a+b+c+a:b) glmulti(mod2, level=2) and glmulti("y", c("a", "b", "c"),

Hello, I'm using the "glmulti" package to run models of all the possible combinations of my variables. However, I am only interested in a few interactions between them. I have tried the equivalent of: 1) mod1<-lm(y~a+b+c+a:b) glmulti(mod1, level=1) 2) mod2<-lm(y~a+b+c+a:b) glmulti(mod2, level=2) 3) glmulti("y", c("a",

Dear R, The glmulti package no longer loads through the library() command, apparently because of a problem with rJava. I have today reinstalled R from scratch (updated to v2.14.0) and reinstalled all packages from scratch and updated them all too. The problem is the same as I found on v2.13.2. See session below for the error. I tried install.packages(rJava) as advised by the error report but it

Sorry, I was not clear enough. The reason I want to use mice::mice() rather than library(mice); mice() is that I want to call it from my own package. But the reprex works from the command line as well, straight after launching R: mice::mice(airquality) #> Error in check.method(setup, data): The following functions were not found: mice.impute.pmm, mice.impute.pmm The mice.impute functions

Dear Ole One of the experts may be able to diagnose this without extra information but I suspect you have not got the right magic in your NAMESPACE file in your package. You may need to re-read section 1.5.1 of the Writing R extensions manual. Michael On 04/10/2017 13:47, Ole H?st wrote: > I want to call the mice function from the MICE package from my own package. > However I run into

> library(reshape2) > x = melt(airquality, id=c('month', 'day')) With reshape I can cast with multiple functions: > library(reshape) > cast(x, month+variable~., c(mean,sd)) month variable mean sd 1 5 ozone 23.615385 22.224449 2 5 solar.r 181.296296 115.075499 3 5 wind 11.622581 3.531450 4 5 temp 65.548387

I want to call the mice function from the MICE package from my own package. However I run into this issue, which can be reproduced on the command line: mice::mice(airquality)#> Error in check.method(setup, data): The following functions were not found: mice.impute.pmm, mice.impute.pmm I have no problems when doing library(mice) mice(airquality) Is this a bug or am I missing something?

>>>>> peter dalgaard <pdalgd at gmail.com> >>>>> on Mon, 26 Jun 2017 13:43:28 +0200 writes: > This seems to be due to changes made by Martin Maechler in > 2008. Presumably this fixed something, but it escapes my > memory. Yes: The change set (svn -c46441) also contains the following NEWS entry BUG FIXES o

Hello Given the following data for a data set called airquality. To identify the nature of the objects from the data set airquality example "Ozone" would it be best to use the command is. like is.character(airquality$Ozone) ....... I tried attributes(airquality$Ozone) but it came up null. Would there be a better way to identify these objects. Thanking you in advance for your

Dear List, I am using both the clm() and clmm() functions from the R package 'ordinal'. I am fitting an ordinal dependent variable with 5 categories to 9 continuous predictors, all of which have been normalised (mean subtracted then divided by standard deviation), using a probit link function. From this global model I am generating a confidence set of 200 models using clm() and the

The function 'regsubsets' appears to calculate a BIC value that is different from that returned by the function 'BIC'. The latter is explained in the documentation, but I can't find an expression for the statistic returned by 'regsubsets'. Incidentally, both of these differ from the BIC that is given in Ramsey and Schafer's, The Statistical Sleuth. I assume

IIUC, this would be an isssue with MICE (or rather "mice"), which isn't Ole's. It could be a namespace issue, but it could also be that some start-up code is not executed if library() is bypasses (see .onAttach et al.). -pd > On 4 Oct 2017, at 17:00 , Michael Dewey <lists at dewey.myzen.co.uk> wrote: > > Dear Ole > > One of the experts may be able to

> On Jun 21, 2017, at 9:11 AM, Evan Cooch <evan.cooch at gmail.com> wrote: > > Suppose I have the following sort of dataframe, where each column name has a common structure: prefix, followed by a number (for this example, col1, col2, col3 and col4): > > d = data.frame( col1=runif(10), col2=runif(10), col3=runif(10),col4=runif(10)) > > What I haven't been able to