I am currently using plot_model to plot a binomial generalized linear mixed effects model from an AIC model selection process. Overall, plot_model works well, but I am having trouble customizing the legend. This largely stems from the fact that some code needs to be within the plot_model function, while other code needs to be in… Read More How to change the aesthetics of the legend when using plot_model in R?

I encountered strange behavior when using lapply to bootstrap a GLM. Each iteration of the lapply uses a different weight, but the formula variable is the same. Thus, the latter was kept outside the anonymous function. Below is a reproducible toy example. The following code runs as expected: library(dplyr) data_adult <-read.csv("https://raw.githubusercontent.com/guru99-edu/R-Programming/master/adult.csv&quot;) data_adult$Y <- (data_adult$hours.per.week >… Read More lapply and function arguments with different scope

I’m writing a small "engine", and the time has finally come to implement transformations. However, when I try to glGetUniformLocation, it return -1. Here is my rendering method: void GFXRenderer::submit(EntityBase* _entity, GPUProgram _program) { if(_entity->mesh.has_value()) { mat4 mod_mat(1.0); //mod_mat = translate(mod_mat, _entity->transform.position); /* mod_mat = scale(mod_mat, _entity->transform.scale); mod_mat = rotate(mod_mat, radians(_entity->transform.rotation.x), vec3(1.0, 0.0, 0.0)); mod_mat… Read More glGetUniformLocation returning -1 on OpenGL 4.6

I am trying to iterate over multiple columns for a glm function in R. view(mtcars) names <- names(mtcars[-c(1,2)]) for(i in 1:length(names)){ print(paste0("Starting iterations for ",names[i])) model <- glm(mpg ~ cyl + paste0(names[i]), data=mtcars, family = gaussian()) summary(model) print(paste0("Iterations for ",names[i], " finished")) } however, I am getting the following error: [1] "Starting iterations for disp"… Read More Iterating and looping over multiple columns in glm in r using a name from another variable

I’m trying to plot the observed vs. the estimated line of a negative binomial regression model with random effects, where this curve takes into account the structure of the adopted model. The data and the model were fitted as follows: d.AD <- data.frame(treatment = gl(12,12), treatment2 = gl(4,1,36), counts = rpois(144, 4.03), treatment3 = gl(3,4,36),… Read More Curves predicted using the negative binomial model with random effects