lme4

问题 As many other people, I'm having troubles running a model which uses glmer function from package lme4. Here is my model: model = glmer(depvar ~ variety*cover+amplitude+time+ (1|pp) + (1|stim), data = datafile, family=poisson) And here is the warning I get: Warning message: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00606839 (tol = 0.001, component 1) I read at this link that if I add control=glmerControl(optimizer="bobyqa

问题 As many other people, I'm having troubles running a model which uses glmer function from package lme4. Here is my model: model = glmer(depvar ~ variety*cover+amplitude+time+ (1|pp) + (1|stim), data = datafile, family=poisson) And here is the warning I get: Warning message: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00606839 (tol = 0.001, component 1) I read at this link that if I add control=glmerControl(optimizer="bobyqa

问题 Here is my data frame (my real DF has way more data points): rearing.temp<-c("15", "15", "15", "15", "19", "19", "19", "19") source<-c("field", "field", "woods", "woods", "field", "field", "woods", "woods") runway.temp<-c("40","20","40","20","40","20","40","20") velocity<-c("2.3", "2.1", "1.9", "1.9", "2.3", "2.2", "2.3", "2.0") snail<-data.frame(rearing.temp, source, runway.temp, velocity) Here is my model: mod <- lmer(velocity ~ runway.temp*source*rearing.temp + (1|family) + (1|collection

问题 I am using lmerTest::lmer() to perform linear regression with repeated measures data. My model contains a fixed effect (factor with 5 levels) and a random effect (subject): library(lmerTest) model_lm <- lmer(likertscore ~ task.f + (1 | subject), data = df_long) I would like to include the total number of observations, the number of subjects, total R^2, and the R^2 of the fixed effects in the regression table which I generate with modelsummary() . I tried to extract these and build a gof_map

问题 I am using lmerTest::lmer() to perform linear regression with repeated measures data. My model contains a fixed effect (factor with 5 levels) and a random effect (subject): library(lmerTest) model_lm <- lmer(likertscore ~ task.f + (1 | subject), data = df_long) I would like to include the total number of observations, the number of subjects, total R^2, and the R^2 of the fixed effects in the regression table which I generate with modelsummary() . I tried to extract these and build a gof_map

问题 I would like to study differences in fat between 2 visits with a linear mixed effects model. So everything would start as lme(fat~ , now... for the coefficients, I have some that will change from visit 1 to visit 2, as they are hypertension status, diabetis status, bmi, waist circunference, smoking_status etc. And other variables that won't change from visit 1 to visit 2, as they are gender or ethnicity. Note that the following variables are dummy ( hypertension status, diabetis status,

问题 I am writing a sub-routine to return output of longitudinal mixed-effects models. I want to be able to pass elements from lists of variables into lme/lmer as the outcome and predictor variables. I would also like to be able to specify contrasts within these mixed-effects models, however I am having trouble with getting the contrasts() argument to recognise the strings as the variable names referred to in the model specification within the same lme/lme4 call. Here's some toy data, set.seed(345

问题 I have a data frame with the following structure: > t <- read.csv("combinedData.csv")[,1:7] > str(t) 'data.frame': 699 obs. of 7 variables: $ Awns : int 0 0 0 0 0 0 0 0 1 0 ... $ Funnel : Factor w/ 213 levels "MEL001","MEL002",..: 1 1 2 2 2 3 4 4 4 4 ... $ Plant : int 1 2 1 3 8 1 1 2 3 5 ... $ Line : Factor w/ 8 levels "a","b","c","cA",..: 2 2 1 1 1 3 1 1 1 1 ... $ X : int 1 2 3 4 7 8 9 10 11 12 ... $ ID : Factor w/ 699 levels "MEL_001-1b","MEL_001-2b",..: 1 2 3 4 5 6 7 8 9 10 ... $ BobWhite

问题 I have a data frame with the following structure: > t <- read.csv("combinedData.csv")[,1:7] > str(t) 'data.frame': 699 obs. of 7 variables: $ Awns : int 0 0 0 0 0 0 0 0 1 0 ... $ Funnel : Factor w/ 213 levels "MEL001","MEL002",..: 1 1 2 2 2 3 4 4 4 4 ... $ Plant : int 1 2 1 3 8 1 1 2 3 5 ... $ Line : Factor w/ 8 levels "a","b","c","cA",..: 2 2 1 1 1 3 1 1 1 1 ... $ X : int 1 2 3 4 7 8 9 10 11 12 ... $ ID : Factor w/ 699 levels "MEL_001-1b","MEL_001-2b",..: 1 2 3 4 5 6 7 8 9 10 ... $ BobWhite

问题 I am analysing some whale tourism data and am trying to construct linear mixed effect models in the nlme package to see if any of my explanatory variables affect encounter time between whales and tourists. (I am also open to running this model in lme4 .) My variables are: mins : encounter time (response variable) Id : individual whale ID (random effect) Vessel : vessel Id (random effect) Sex : sex of the animal Length : length of the animal Year Month (nested within Year ). So my random