HI All this question has been answered in the past here, however this method is not working for me .
I also would like to attain a table with predicted probabilities for new values of my dependent variable (Dose in this case)
the previous post is here :Predictions in logistic regression model by group
when i try the recommended code, i am running into the following error
Error in eval(lhs, parent, parent) : object 'fit_data' not found
I think i have loaded all the packages required . I dont understand why this wouldnt work ?
library(tidyverse) library(dplyr) library(broom) library(purrr) library(tidyr) df<-read.csv("IHC_Organ_Dose response_1a.csv") data.y<-data.frame(dose=0:10) model<-df %>% group_nest(organ) %>% mutate(model = map(data, ~glm(IHC~Dose, data = .x, family = "binomial")) ) %>% inner_join(fit_data %>% group_nest(organ), by = "organ") %>% mutate(fitted = map2(model, data.y, ~predict(.x, newdata = .y, type = "response"))) %>% unnest(c("fitted", "data.y")) %>% select(organ, dose, fitted) **Error in eval(lhs, parent, parent) : object 'fit_data' not found** # the other format i tried was as follows (option#2): library(purrr) library(broom) models <- df %>% group_by(organ) %>% nest() %>% mutate(model = map(data, ~ glm(IHC ~ Dose, data = ., family = binomial(logit)), augmented= map2(model, data, augment,type.predict="reponse"))) models %>%unnest(augment) # the error i get from option 2 is as follows #models %>% unnest(augmented) Error: Can't subset columns that don't exist. x The column #augmented doesn't exist