CODEWITHSUNDEEP
rdplyrbroom
RDavey
RDavey

Reputation: 1909

How to use augment with a model on new data

It is fairly straightforward to use the augment function from the Broom package in R to add predictions back into a tibble. Viz.

df <- iris %>%
  nest(data = everything()) %>%
  mutate(model = map(data, function(x) lm(Sepal.Length ~ Sepal.Width, data = x)),
         pred = map2(model, data, ~augment(.x, newdata = .y))) %>%
  unnest(pred)

However, when I take a linear model trained on one set of data and try and predict on new data I receive the following error.

mod <- lm(Sepal.Length ~ Sepal.Width, data = iris)

df2 <- iris %>%
  mutate(Sepal.Width = Sepal.Width + rnorm(1)) %>%
  nest(data = everything()) %>%
  mutate(pred = map2(mod, data, ~augment(.x, newdata = .y)))

# Error: Problem with `mutate()` input `pred`.
# x No augment method for objects of class numeric
# i Input `pred` is `map2(mod, data, ~augment(.x, newdata = .y))`.

How should I use augment to fit new data? Is using an external model object (in the example above this is mod) the best practice or is there a more elegant way?

Upvotes: 0

Views: 2425

Answers (2)

Ronak Shah
Ronak Shah

Reputation: 389065

Since there is only one model we can do this without using map.

library(dplyr)

df1 <- iris %>%
  mutate(Sepal.Width = Sepal.Width + rnorm(1)) %>%
  tidyr::nest(data = everything()) %>%
  summarise(pred = broom::augment(mod, newdata = data[[1]]), 
            mod = list(mod), 
            data = data)

Upvotes: 1

RDavey
RDavey

Reputation: 1909

Having just posted the question, I think I have an answer. I won't accept the answer for 48 hours just in case someone contradicts or provides a more comprehensive one.

In the example, map2 expects mod as a vector or list but it is a model object. Putting mod into the tibble as a list object suppresses the error and correctly calculates predictions.

mod <- lm(Sepal.Length ~ Sepal.Width, data = iris)

df2 <- iris %>%
  mutate(Sepal.Width = Sepal.Width + rnorm(1)) %>%
  nest(data = everything()) %>%
  mutate(mod = list(mod)) %>% #! this is the additional step
  mutate(pred = map2(mod, data, ~augment(.x, newdata = .y))) %>%
  unnest(pred)

Alternatively, coerce the external model object as list.

...
  mutate(pred = map2(list(mod), data, ~augment(.x, newdata = .y))) %>%
...

Upvotes: 0

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