Why I got Vcov error in calculate confidence interval for regression coefficient?

General
#1

Hello, Dear all:
I am doing this regression lm by each patient ID. Then try to calculate confidence interval for coefficient. I got this error message in the end.
fitted_models = df31 %>% group_by(PatId) %>% do(model = lm(var1 ~ yr_findex, data = .))
t<- fitted_models%>%group_by(PatId) %>% do(confint(., "yr_findex", level=0.80))

Then I always this error as
Error in UseMethod("vcov") :
no applicable method for 'vcov' applied to an object of class "c('tbl_df', 'tbl', 'data.frame')"

Anyone knows why? I am a SAS newly converted R user...so still asking very simple questions here. Thanks a lot!

#2

The problem you had with calling confint is that your . object was a dataframe rathen than an lm object.

as I dont have your data I used iris as example data.

Here is one approach

library(tidyverse)
library(purrr)

fitted_models <- iris %>%
  group_by(Species) %>%
  do(model = lm(Petal.Length ~ Petal.Width, data = .)) %>%
  ungroup()

t <- purrr::map_dfr(
  fitted_models$model,
  ~ as_tibble(confint(., "Petal.Width", level = 0.80))
)

combined <- bind_cols(fitted_models, t)
1 Like
#3

thanks a lot! Let me try!

#8

thanks, I figure it out. Only run the models first and ungroup for CI calculation in the second step.

#9

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

#10
#4

Why I need to use "%>% ungroup()" in the end?
I tried without this line, it also works... just want to know what that means?

#6

Right now I have another issue. Since I need to extract the coefficient from model by ID.
I did this.
fitted_models = df31 %>% group_by(PatId) %>% do(model = lm(eGFR_value ~ yr_findex, data = .))
fit_est<-fitted_models %>% tidy(model)%>% subset(term %in% "yr_findex")

In the end, I merge the CI for coefficient by ID to fit_est. If I used ungroup(). Then I can't have fit_est created correctly...
How to run one model. Then extract the slope and calculate CI as the results?

thanks a lot!

#5

It removes the group attribute, so it effect how dplyr verbs operate on the tibble.

1 Like
#7

Please use iris as a representative data if you want me to run your code.