similar to: Using response variable in interaction as explanatory variable in glm crashes R

>>>>> Jan van der Laan <rhelp at eoos.dds.nl> >>>>> on Fri, 6 Oct 2017 12:13:39 +0200 writes: > It is actually model.matrix that crashes, not glm. Same > crash occurs with e.g. lm. > model.matrix(dob_mon ~ dob_day*dob_mon, data = tab) > also crashes R. Yes, segmentation fault. It only happens when these are *logical*

It is actually model.matrix that crashes, not glm. Same crash occurs with e.g. lm. model.matrix(dob_mon ~ dob_day*dob_mon, data = tab) also crashes R. Jan On 06-10-17 12:08, Jan van der Laan wrote: > > The following code crashes R (I know I shouldn't try to estimate such > a model; this was a bug in some code of mine). I also tried with > R-devel; same result. > >

Hello everyone, I am running a mixed effects model where I have two fixed factors, one with 2 levels and one with 4, and their interaction. Let's say these are my factors and their levels: FirstFactor: 1, 2 SecondFactor: A, B, C, D For the interaction, I am interested in the four two-way comparisons, not the two four-way comparisons. In other words, I want to test whether 1A is

Hi Livia and everyone, Did you ever get a response on this question from last year (Jan 2008)? I am also looking for more explanatory documentation on the ui and ci parameters for the function constrOptim(). The examples provided in the R help and the full reference manual are not working for me. Goodle and Nabble searches have not resulted in any explanation

(Oops first mistake was posting to the wrong area) I am not sure what is needed to be posted in terms of what I have done but will explain nonetheless. I am using the msm.package and trying to specify my reference category for an outcome covariate. The following command line works: ## age of respondent - using year5a: categorical preg_fyear5a.msm<-msm(outcome~ipi, subject=id, data,

Hello! I want to do a non-linear regression with 2 explanatory variables (something like : length ~ a * time * exp( b* temperature)), having a data set (length, time, temperature). Which function could I use (I tried nls but I think it doesn't work) Thanks a lot! Janice

One of the assumptions, on which the (General) Linear Modelling is based is that the response variable is measured with some uncertainties (or weighted), but the explanatory variables are fixed. Is it possible to extend the model by assigning the weights to the explanatory variables as well? Is there a package for doing such a model fit? Thanks

Hi, I just wanted to check I'm not re-inventing the wheel here. I'm developing a new algorithm for backfitting (i.e. additive models) and for computing partial residuals, where partial residuals are still computed even where there are missing values. Noting additive models here contain both linear terms and smooth terms. If I am re-inventing the wheel could some one please let me know.

Dear all, I have a very large dataset (1712351 , 20) and would like to plot only the arrows that represent the contribution of each variables. On the sample below I woild like to plot only the explanatory variables (Murder, Assault..) and not the sites. prcomp(USArrests) # inappropriate prcomp(USArrests, scale = TRUE) prcomp(~ Murder + Assault + Rape, data = USArrests, scale = TRUE)

? To whoever it may correspond, My name is Oscar Saenz and I am working on my thesis in Spain. I am using GAMs in "R" and, now that I have estimated my models, I need to plot the predicted smooth functions against the dependent variable (just as in Carlslaw et al. 2007, fig. 1*). Otherwise, I'd like to know how to plot the smoothed predictor variable values against the partial

How should I analysis it in R ???? all the resposes variables are ordinal from 0 to 10. and the explanatory variable is a factor ... -- View this message in context: http://old.nabble.com/Multivariate-problems-.-.-.-with-200-resposes-variables-and-1-explanatory-variable-tp26522912p26522912.html Sent from the R help mailing list archive at Nabble.com.

Hi everyone I am new to the list and read all the instructions, hope i get it right. I have the following linear model: model_sqrt<-lm(sqrt(mortality)~richness +Acer_davidii+Ailanthus_altissima+Alniphyllum_fortunei +Betula_luminifera+Castanea_henryi+Castanopsis_carlesii +Castanopsis_eyrei+Castanopsis_fargesii+Castanopsis_sclerophylla

Hello, I would like to evaluate the relationship between flows and phytoplankton abundance (or Chlorophyll a concentrations) using a changepoint analysis.? Specifically, I have two study questions: Study Question 1: Are there certain flow thresholds that result in spikes in phytoplankton abundance? Study Question 2: Are the duration of certain flows important for phytoplankton abundance (e.g.,

Hi, I would like to regress y (dependent variable) on x (independent variable) and y(-1). I have create the y(-1) variable in this way: ly<-lag(y, -1) Now if I do the following regression lm (y ~ x + ly) the results I obtain are not correct. Can someone tell me the code to use in R in order to perform a regression using as explanatory variable a lagged dependent variable? My best regards,

Hi R helpers! I have used a glht as a post-hoc test on an lmer with: -2 treatments (A & B) -1 categorical explanatory variable (song type) -1 response variable (latency to respond) I wanted to make comparisons between the categorical variables depending on treatment. At the moment the glht simply returns comparisons of each of the (3) categorical explanatory variables with each other

Dear helpers, does anyone know a function to fit a model with: - y mean that is regressed on a set of explanatory variables - y variace behaving as a garch or as a garch in mean Thank you so much for your help, Carlo

Hello I'm very new to R (which is excellent), so apologies if this has already been raised. In the spdep package, I'm trying to undertake an autoregressive mixed model using the lagsarlm function. This is working fine, but there does not appear to be a method of including an explanatory variable without it automatically being included as a lagged term. I'm after something along the

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Hi there, I'm trying to make a scatterplot of removed versus duration for each type of bee. No matter what I try, I can't seem to get my code to work. Any help would be appreciated. Thanks! My r-code: dat$BEE <- with(dat, factor(BEE, c(1,2))) plot(REMOVED~DURATION,pch=BEE, col=BEE) REMOVED DURATION BEE 1 0.07 2 QUEEN 2 0.10 5 QUEEN 3 0.11 7 QUEEN 4 0.12 11 QUEEN 5 0.15 12 QUEEN 6 0.19