Extract estimated coefficients and the standard error using tidyverse

2
votes

I have a dataframe like so: 

set.seed(560)
df<-data.frame(lag= rep(1:40, each=228), psit= rep(rnorm(228, 20, 
10)),var=rnorm(9120, 50, 10))

For each subset of lag I would like to run a linear regression where psit is predicted by var (e.g. lm(psit~var, df)). I would like to output the coefficient information for each value. Specifically, the beta Estimate and the Std. error into a datatframe. Then calculate the standardized effect size. The output should be:

 output<-data.frame(lag= rep(1:40, each=1), estimate= rep(rnorm(40, 
 .5, 0.01)),std.error=rnorm(40,0.01, 0.01))
 output$strd.effect <- output$estimate /output$std.error

I have tried:

models <- 
 df %>%
 group_by(lag) %>%
 do(model = lm(psit ~ var,data = .))


coeff<- 
  models %>%
  ungroup()%>%
  group_by(variable) %>%
  do(glance(estimate=summary(model[i]$coeff[,1],
  std.error=summary(model[i]$coeff[,2])

coeff<-
    coeff %>%
    group_by(variable) %>%
    mutate(std.effect=estimate[[i]]/coeff[[i]]
1
rlinear-regressiontidyverse

1 Answers

2
votes

The broom pacakge can help here. Try

models %>% 
   rowwise() %>% 
   do({cbind(broom::tidy(.$model), lag=.$lag)}) %>% 
   filter(term=="var") %>% 
   mutate(std.effect=estimate/std.error ) %>% 
   select(lag, estimate, std.error, std.effect)