Plot estimated function?-CodePudding

I need help interpreting this (it's part of an assignment which I'm stuck with):

"Create a plot for each subject where you plot the estimated function (on the target.contrast range from 0-1) based on the fitted values of a model (use glm) that models correct as dependent on target.contrast."

The variables:

subject: values 1:36 naming each subject
target contrast: Continuous variable having values in the range 0-1
correct: Binary variable: either 0 or 1

Length of data set is 5,000 rows.

Now to my question: What do you think is meant by 'estimated function'? Random intercepts? Estimated values? I'm quite confused.

So far, I've made this:

# modelling correct as dependent on target.contrast
model1 <- glmer(correct ~ target.contrast   (1 | subject), data = exp2_staircase, family = binomial)

plot(exp2_staircase$target.contrast, model1$fitted.values, xlab='Target contrast',
     ylab='Probability of being correct/incorrect',
     main='Logistic regression - fitted values')

CodePudding user response：

Since the requirement is to use a glm for each group (here: manufacturer), we can do exactly this:

library(tidyverse)

estimates <-
  mpg %>%
  nest(-manufacturer) %>%
  mutate(
    model = data %>% map(~  glm(displ ~ hwy, data = .x)),
    estimates = model %>% map2(data, ~ .x %>% predict(newdata = .y))
  ) %>%
  select(manufacturer, data, estimates) %>%
  unnest(estimates) %>%
  unnest(data) %>%
  mutate(displ = estimates) %>%
  distinct(manufacturer, hwy, .keep_all = TRUE)
#> Warning: All elements of `...` must be named.
#> Did you want `data = c(model, displ, year, cyl, trans, drv, cty, hwy, fl, class)`?
    
bind_rows(
  mpg %>% mutate(type = "truth"),
  estimates %>% mutate(type = "predicted")
) %>%
  filter(manufacturer %in% c("audi", "volkswagen")) %>%
  ggplot(aes(hwy, displ, color = type, shape = manufacturer))  
    geom_point()

^{Created on 2021-10-03 by the reprex package (v2.0.1)}

Random factors are only used, if the prediction is based on new groups the model has not seen yet.