lme4

问题 Context of application I have a model with random slopes and intercepts. There are numerous levels of the random effects. The new data (to be predicted) may or may not have all of these levels. To make this more concrete, I am working with music revenue at the album level ( title ). Each album may come in multiple types format2 (CD, vinyl, e-audio, etc). I have measurements for revenue for each album at each type of album. The model is specified as: lmer(physical~ format2+ (0+format2|title))

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

问题 I am fitting a linear mixed effects model using lme4 : library(lme4) data(Orthodont) dent <- Orthodont d.test <- lmer(distance ~ age + (1|Subject), data=dent) If we say generically Y = X * B + Z * d + e is the form of a linear mixed effects model, then I am trying to get Var(Y) = Z * Var(d) * Z^t + Var(e) from the results of the model. Is the following formulation the right way to do this? k <- table(dent$Subject)[1] vars <- VarCorr(d.test) v <- as.data.frame(vars) sigma <- attr(vars, "sc") s

问题 In the afex package we can find this example of ANOVA analysis: data(obk.long, package = "afex") # estimate mixed ANOVA on the full design: # can be written in any of these ways: aov_car(value ~ treatment * gender + Error(id/(phase*hour)), data = obk.long, observed = "gender") aov_4(value ~ treatment * gender + (phase*hour|id), data = obk.long, observed = "gender") aov_ez("id", "value", obk.long, between = c("treatment", "gender"), within = c("phase", "hour"), observed = "gender") My question

问题 I have a transplant experiment for four locations and four substrates (taken from each location). I have determined survival for each population in each location and substrate combination. This experiment was replicated three times. I have created a lmm as follows: Survival.model <- lmer(Survival ~ Location + Substrate + Location:Substrate + (1|Replicate), data=Transplant.Survival,, REML = TRUE) I would like to use the predict command to extract predictions, for example: Survival.pred <-

问题 I have a data frame with 5 variables: Lot / Wafer / Serial Number / Voltage / Amplification. In this data frame there are 1020 subsets grouped by Serial_number. Each subset has a certain number of measurement data points (Amplification against voltage). I fit the data with summary(fit2.lme <- lmer(log(log(Amplification)) ~ poly(Voltage, 3) + (poly(Voltage, 1) | Serial_number), + data = APD)) which yields: Linear mixed model fit by REML ['lmerMod'] Formula: log(log(Amplification)) ~ poly

问题 I have been trying to fit a penalized spline regression model in R using the connection between penalized splines and linear mixed models. While I am quite familiar with the R function lme , using its competitor lmer presents some difficulties. Here is a toy example I would like to generalize to lmer : require(nlme) grid <- seq(0, 1, len = 100) y <- rep(0, length(grid)) for(i in 1:length(grid)){ y[i] <- sin(3*pi*grid[i]) + rnorm(1, 0, 1) } X <- cbind(rep(1, length(grid)), grid, grid^2, grid^3

问题 I am having difficulty installing the lme4 and ggplot packages I've tried to install them by selecting a CRAN mirror (have tried several) and then selecting the packages, and have also tried the following; install.packages("lme4",repos="http://r-forge.r-project.org") and install.packages("ggplot2", dependencies=TRUE) but however I try to call these packages I get the warning message package ‘lme4’ is not available (for R version 2.15.2) or package ‘ggplot2’ is not available (for R version 2

问题 I am trying to use the Python statsmodels linear mixed effects model to fit a model that has two random intercepts, e.g. two groups. I cannot figure out how to initialize the model so that I can do this. Here's the example. I have data that looks like the following (taken from here): subject gender scenario attitude frequency F1 F 1 pol 213.3 F1 F 1 inf 204.5 F1 F 2 pol 285.1 F1 F 2 inf 259.7 F1 F 3 pol 203.9 F1 F 3 inf 286.9 F1 F 4 pol 250.8 F1 F 4 inf 276.8 I want to make a linear mixed

问题 For simple glm objects, I can use predict(fit, type = "terms") to retrieve a matrix with fitted values for each term. What is the equivalent for lmer resp. glmer fitted models? As far as I can see, the predict.merMod function does not support type = terms . 回答1: What is the equivalent for lmer resp. glmer fitted models? I do not think there is one. Though, you can easily make one as follows ##### # fit model with one terms which is a matrix library(lme4) fit <- lmer(Reaction ~ cbind(Days,