![]() What do hjust and vjust do when making a plot using ggplot?.Removing outliers from linear regression when using multiple models.R - Models in caret don't finish when using # of cores > 64 in foreach & doParallel.Why does R's attributes() function fail when using explicit arguments?.R CRAN Check fail when using parallel functions.Models fail to converge when using MuMIn::dredge.Let's explore the model fit by hand: there's only one explicit parameter (the among-state standard deviation). Look more carefully: mf <- transform(ame(stdz.model), t <- dredge(stdz.model,trace=true)Īnd try it out: test1 <- lmer(formula = yld.res ~ z.brk + z.onset + (1 | state), Let's find one of the models that breaks: options(warn=1) No huge pairwise correlations among predictors: cor(as.matrix(dd)) ![]() Look at coefficients of standardized model: library(dotwhisker)ĭwplot(stdz.model)+geom_vline(xintercept=0,lty=2) Not much going on here, but also nothing too bizarre-looking. Ggplot(mm,aes(value,yld.res,colour=state))+geom_point()+įacet_wrap(~variable,scale="free")+geom_smooth(method="lm") Stdz.model <- standardize(global.model,standardize.y = false)Ĭheck out data: library(ggplot2) theme_set(theme_bw()) Global.model <- lmer(yld.res ~ rain + brk+ act + onset + Replicating your setup: dd <- read.csv("sotmpdat.csv") the likelihood curve is completely flat at the edge of the estimated space, which is screwing up the convergence checks (this is unusual, but there's nothing wrong with it). i don't see anything fishy in the data or the models.
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