mumin

问题 I am running a dredge of a linear mixed effect model in the MuMin package in R, The model is quite big (see below) > Monster <- lmer(Fw.FratioFall ~ Average_mintemp_winter + (Average_mintemp_winter^2) > + percentage_woody_coverage + (percentage_woody_coverage^2) > + kmRoads.km2 + (kmRoads.km2^2) + Fracking > + WELLS_ACTIVED + (WELLS_ACTIVED^2) + BadlandsCoyote.1000_mi > + (BadlandsCoyote.1000_mi^2) + cougar_presence + COYOTE_springsurveys > + (COYOTE_springsurveys^2) + d3.1 + (d3.1^2) + WT

问题 I'm trying to display the results of a logistic regression. My model was fit using glmer() from the lme4 package, I then used MuMIn for model averaging. Simplified version of my model using the mtcars dataset: glmer(vs ~ wt + am + (1|carb), database, family = binomial, na.action = "na.fail") My desired output is two plots that show the predicted probability that vs =1, one for wt , which is continuous, one for am , which is binomial. UPDATED: I got this much working after comments from

问题 Is there a way to directly plot model average summary outputs from MuMIn model.avg() for different variables with confidence bands. Previously I had been using ggplot and ggpredict to plot terms from the actual models, but I haven't been able to find a way to plot the results of the averaged models. Clearly I can plot the slope and intercept manually, but getting accurate confidence bands and plotting from confint() is not ideal and I have yet to get confidence bands from the intervals that

问题 I'm using MuMin to perform model averaging using glmmTMB to build the global model, which all works fine. I would then like to produce effect displays of results using the visreg or effects package. Both of these work fine on regular models produced using glmmTMB but not with the results of model averaging. I appreciate the output from avg.model in MuMin is a list of component models and coefficients, which isn't compatible with visreg or effects . I've tried taking a summary of the averaged

问题 I have a set of linear mixed models, and have created an average model. I'd like to plot the model fits for two levels of a factor, included in the average model. A simple example: library(lme4) library(MuMIn) mtcars2 <- mtcars mtcars2$vs <- factor(mtcars2$vs) gl <- lmer(mpg ~ am + disp + hp + qsec + (1 | cyl), mtcars2, REML = FALSE, na.action = 'na.fail') d <- dredge(gl) av <- model.avg(d, subset = cumsum(weight) <= 0.95) summary(av) Call: model.avg(object = d, subset = cumsum(weight) <= 0

问题 I am using the 'MuMIn' package in R to select models and calculate effect sizes of the input variables (rain, brk, onset, wid). To make my effect size comparable between variables, I standardised them using standardize function in arm package. Here is the code that I am following: For reference, please refer to the appendix of this paper: http://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2010.02210.x/full Grueber et al. 2011: Multimodel inference in ecology and evolution: challenges and

问题 I am using the 'MuMIn' package in R to select models and calculate effect sizes of the input variables (rain, brk, onset, wid). To make my effect size comparable between variables, I standardised them using standardize function in arm package. Here is the code that I am following: For reference, please refer to the appendix of this paper: http://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2010.02210.x/full Grueber et al. 2011: Multimodel inference in ecology and evolution: challenges and

问题 I am wondering whether anyone could help me trouble shoot this error from a task with dredge package: Fixed term is "(Intercept)" |============================================================================ 63% Error in while ((iComb <- iComb + 1L) < ncomb) { : missing value where TRUE/FALSE needed In addition: Warning message: In iComb + 1L : NAs produced by integer overflow Timing stopped at: 90799.82 18.545 90796.9 > I see that it probably has to do with my TRUE/FALSE subset matrix but I

问题 I am trying to use dredge from the R package MuMIn with a global binomial glmer model. I find that I need to specify the optimizer with control = glmerControl(optimizer="bobyqa") for convergence. However, when I go to use dredge , I get an error. If I reduce the number of predictors in the model, I can remove the bobyqa specification, get convergence, and use dredge. Any way I can get dredge to go with glmerControl(optimizer="bobyqa") ? test.glob=glmer(exploitpark~X + as.factor(Y) + Z + A + B