CODEWITHSUNDEEP
rpurrrbroom
wytham
wytham

Reputation: 98

felm doesn't work with broom::augment/purrr but works with tidy

I'm trying to run regressions within a nested data frame as described here. For my purposes, I'm using felm from the lfe package because I have many levels of fixed effects.

If I re-do the example in the link above using felm instead of lm, it works for the most part until I try to use broom::augment.

library(tidyverse)
library(broom)
library(gapminder)
library(lfe)

by_country <- gapminder %>% 
  group_by(continent, country) %>% 
  nest()

country_felm <- function(data){
  felm(lifeExp ~ year, data = data)
}

by_country <- by_country %>% 
    mutate(model = purrr::map(data, country_felm)
    )

Everything works up to this point except that I had to use a function instead of a formula in purrr::map in the last line of code, possibly another felm quirk.

Now if I try to use broom to extract the model output, it works for glance and tidy, but not for augment. 

by_country %>% unnest(model %>% purrr::map(broom::glance))
by_country %>% unnest(model %>% purrr::map(broom::tidy))
by_country %>% unnest(model %>% purrr::map(broom::augment))

Trying to use augment results in the following error message:

Error in mutate_impl(.data, dots) : 
  argument must be coercible to non-negative integer
In addition: Warning message:
In seq_len(nrow(x)) : first element used of 'length.out' argument

Upvotes: 0

Views: 299

Answers (1)

aosmith
aosmith

Reputation: 36086

It looks like augment is having trouble finding the data for the data argument, which is generally the dataset used for fitting.

The problem is easier to see if working with a single one of these models rather than all of them at once.

This doesn't work, with your given error:

augment(by_country$model[[1]])

But explicitly passing the data to the data argument does:

augment(by_country$model[[1]], data = by_country$data[[1]])

A work-around is therefore to pass the dataset to augment as the second argument. This can be done via purrr:map2, looping through both the model and data columns at the same time.

by_country %>%
    unnest(model %>% purrr::map2(., data, augment))

Upvotes: 1

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