What is the best way to fit different model formulae that vary by the row of a data frame with the list-columns data structure in tidyverse?
In R for Data Science, Hadley presents a terrific example of how to use the list-columns data structure and fit many models easily (http://r4ds.had.co.nz/many-models.html#gapminder). I am trying to find a way to fit many models with slightly different formulae. In the below example adapted from his original example, what is the best way to fit a different model for each continent?
library(gapminder)
library(dplyr)
library(tidyr)
library(purrr)
library(broom)
by_continent <- gapminder %>%
group_by(continent) %>%
nest()
by_continent <- by_continent %>%
mutate(model = map(data, ~lm(lifeExp ~ year, data = .)))
by_continent %>%
mutate(glance=map(model, glance)) %>%
unnest(glance, .drop=T)
## A tibble: 5 × 12
# continent r.squared adj.r.squared sigma statistic p.value df
# <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#1 Asia 0.4356350 0.4342026 8.9244419 304.1298 6.922751e-51 2
#2 Europe 0.4984659 0.4970649 3.8530964 355.8099 1.344184e-55 2
#3 Africa 0.2987543 0.2976269 7.6685811 264.9929 6.780085e-50 2
#4 Americas 0.4626467 0.4608435 6.8618439 256.5699 4.354220e-42 2
#5 Oceania 0.9540678 0.9519800 0.8317499 456.9671 3.299327e-16 2
## ... with 5 more variables: logLik <dbl>, AIC <dbl>, BIC <dbl>,
## deviance <dbl>, df.residual <int>
I know I can do it by iterating through by_continent (not efficient as it estimates each model for every continent:
formulae <- list(
Asia=~lm(lifeExp ~ year, data = .),
Europe=~lm(lifeExp ~ year + pop, data = .),
Africa=~lm(lifeExp ~ year + gdpPercap, data = .),
Americas=~lm(lifeExp ~ year - 1, data = .),
Oceania=~lm(lifeExp ~ year + pop + gdpPercap, data = .)
)
for (i in 1:nrow(by_continent)) {
by_continent$model[[i]] <- map(by_continent$data, formulae[[i]])[[i]]
}
by_continent %>%
mutate(glance=map(model, glance)) %>%
unnest(glance, .drop=T)
## A tibble: 5 × 12
# continent r.squared adj.r.squared sigma statistic p.value df
# <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#1 Asia 0.4356350 0.4342026 8.9244419 304.1298 6.922751e-51 2
#2 Europe 0.4984677 0.4956580 3.8584819 177.4093 3.186760e-54 3
#3 Africa 0.4160797 0.4141991 7.0033542 221.2506 2.836552e-73 3
#4 Americas 0.9812082 0.9811453 8.9703814 15612.1901 4.227928e-260 1
#5 Oceania 0.9733268 0.9693258 0.6647653 243.2719 6.662577e-16 4
## ... with 5 more variables: logLik <dbl>, AIC <dbl>, BIC <dbl>,
## deviance <dbl>, df.residual <int>
But is it possible to do this without following back to loop in base R (and avoiding fitting models I don't need)?
What I tried is something like this:
by_continent <- by_continent %>%
left_join(tibble::enframe(formulae, name="continent", value="formula"))
by_continent %>%
mutate(model=map2(data, formula, est_model))
But I don't seem to be able to come up with an est_model function that works. I tried this function (h/t: https://gist.github.com/multidis/8138757) that doesn't work:
est_model <- function(data, formula, ...) {
mc <- match.call()
m <- match(c("formula","data"), names(mc), 0L)
mf <- mc[c(1L, m)]
mf[[1L]] <- as.name("model.frame")
mf <- eval(mf, parent.frame())
data.st <- data.frame(mf)
return(data.st)
}
(Admittedly, this is a contrived example. My actual case is that I have substantial observations missing key independent variables in my data, so I want to fit one model with all variables on complete observations and another with only a subset of the variables on the rest observations.)
UPDATE
I came up with an est_model function that works (though probably not efficient):
est_model <- function(data, formula, ...) {
map(list(data), formula, ...)[[1]]
}
by_continent <- by_continent %>%
mutate(model=map2(data, formula, est_model))
by_continent %>%
mutate(glance=map(model, glance)) %>%
unnest(glance, .drop=T)
## A tibble: 5 × 12
# continent r.squared adj.r.squared sigma statistic p.value df
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#1 Asia 0.4356350 0.4342026 8.9244419 304.1298 6.922751e-51 2
#2 Europe 0.4984677 0.4956580 3.8584819 177.4093 3.186760e-54 3
#3 Africa 0.4160797 0.4141991 7.0033542 221.2506 2.836552e-73 3
#4 Americas 0.9812082 0.9811453 8.9703814 15612.1901 4.227928e-260 1
#5 Oceania 0.9733268 0.9693258 0.6647653 243.2719 6.662577e-16 4
## ... with 5 more variables: logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>,
## df.residual <int>
I find it is easier to make a list of model formula. each model was only fit once for the corresponding continent. I add a new column formula to the nested data to make sure that the formula and the continent are in the same order in case they are not.
formulae <- c(
Asia= lifeExp ~ year,
Europe= lifeExp ~ year + pop,
Africa= lifeExp ~ year + gdpPercap,
Americas= lifeExp ~ year - 1,
Oceania= lifeExp ~ year + pop + gdpPercap
)
df <- gapminder %>%
group_by(continent) %>%
nest() %>%
mutate(formula = formulae[as.character(continent)]) %>%
mutate(model = map2(formula, data, ~ lm(.x, .y))) %>%
mutate(glance=map(model, glance)) %>%
unnest(glance, .drop=T)
# # A tibble: 5 × 12
# continent r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
# <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
# 1 Asia 0.4356350 0.4342026 8.9244419 304.1298 6.922751e-51 2 -1427.65947 2861.31893 2873.26317
# 2 Europe 0.4984677 0.4956580 3.8584819 177.4093 3.186760e-54 3 -995.41016 1998.82033 2014.36475
# 3 Africa 0.4160797 0.4141991 7.0033542 221.2506 2.836552e-73 3 -2098.46089 4204.92179 4222.66639
# 4 Americas 0.9812082 0.9811453 8.9703814 15612.1901 4.227928e-260 1 -1083.35918 2170.71836 2178.12593
# 5 Oceania 0.9733268 0.9693258 0.6647653 243.2719 6.662577e-16 4 -22.06696 54.13392 60.02419
# # ... with 2 more variables: deviance <dbl>, df.residual <int>
I found purrr::modify_depth() that does what I want to do with est_model() in the original question. This is the solution I am now happy with:
library(gapminder)
library(tidyverse)
library(purrr)
library(broom)
fmlas <- tibble::tribble(
~continent, ~formula,
"Asia", ~lm(lifeExp ~ year, data = .),
"Europe", ~lm(lifeExp ~ year + pop, data = .),
"Africa", ~lm(lifeExp ~ year + gdpPercap, data = .),
"Americas", ~lm(lifeExp ~ year - 1, data = .),
"Oceania", ~lm(lifeExp ~ year + pop + gdpPercap, data = .)
)
by_continent <- gapminder %>%
nest(-continent) %>%
left_join(fmlas) %>%
mutate(model=map2(data, formula, ~modify_depth(.x, 0, .y)))
by_continent %>%
mutate(glance=map(model, glance)) %>%
unnest(glance, .drop=T)
来源：https://stackoverflow.com/questions/41404198/fit-a-different-model-for-each-row-of-a-list-columns-data-frame