Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
Usage
# S3 method for class 'glmnet'
tidy(x, return_zeros = FALSE, ...)Arguments
- x
A
glmnetobject returned fromglmnet::glmnet().- return_zeros
Logical indicating whether coefficients with value zero zero should be included in the results. Defaults to
FALSE.- ...
Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in
..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
Details
Note that while this representation of GLMs is much easier to plot and combine than the default structure, it is also much more memory-intensive. Do not use for large, sparse matrices.
No augment method is yet provided even though the model produces
predictions, because the input data is not tidy (it is a matrix that
may be very wide) and therefore combining predictions with it is not
logical. Furthermore, predictions make sense only with a specific
choice of lambda.
See also
Other glmnet tidiers:
glance.cv.glmnet(),
glance.glmnet(),
tidy.cv.glmnet()
Value
A tibble::tibble() with columns:
- dev.ratio
Fraction of null deviance explained at each value of lambda.
- estimate
The estimated value of the regression term.
- lambda
Value of penalty parameter lambda.
- step
Which step of lambda choices was used.
- term
The name of the regression term.
Examples
# load libraries for models and data
library(glmnet)
set.seed(2014)
x <- matrix(rnorm(100 * 20), 100, 20)
y <- rnorm(100)
fit1 <- glmnet(x, y)
# summarize model fit with tidiers + visualization
tidy(fit1)
#> # A tibble: 1,086 × 5
#> term step estimate lambda dev.ratio
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 1 -0.207 0.152 0
#> 2 (Intercept) 2 -0.208 0.139 0.00464
#> 3 (Intercept) 3 -0.209 0.127 0.0111
#> 4 (Intercept) 4 -0.210 0.115 0.0165
#> 5 (Intercept) 5 -0.210 0.105 0.0240
#> 6 (Intercept) 6 -0.210 0.0957 0.0321
#> 7 (Intercept) 7 -0.210 0.0872 0.0412
#> 8 (Intercept) 8 -0.210 0.0795 0.0497
#> 9 (Intercept) 9 -0.209 0.0724 0.0593
#> 10 (Intercept) 10 -0.208 0.0660 0.0682
#> # ℹ 1,076 more rows
glance(fit1)
#> # A tibble: 1 × 3
#> nulldev npasses nobs
#> <dbl> <int> <int>
#> 1 104. 255 100
library(dplyr)
library(ggplot2)
tidied <- tidy(fit1) %>% filter(term != "(Intercept)")
ggplot(tidied, aes(step, estimate, group = term)) +
geom_line()
ggplot(tidied, aes(lambda, estimate, group = term)) +
geom_line() +
scale_x_log10()
ggplot(tidied, aes(lambda, dev.ratio)) +
geom_line()
# works for other types of regressions as well, such as logistic
g2 <- sample(1:2, 100, replace = TRUE)
fit2 <- glmnet(x, g2, family = "binomial")
tidy(fit2)
#> # A tibble: 947 × 5
#> term step estimate lambda dev.ratio
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 1 0.282 0.0906 -1.62e-15
#> 2 (Intercept) 2 0.281 0.0826 6.28e- 3
#> 3 (Intercept) 3 0.279 0.0753 1.55e- 2
#> 4 (Intercept) 4 0.277 0.0686 2.48e- 2
#> 5 (Intercept) 5 0.284 0.0625 4.17e- 2
#> 6 (Intercept) 6 0.293 0.0569 5.79e- 2
#> 7 (Intercept) 7 0.303 0.0519 7.39e- 2
#> 8 (Intercept) 8 0.314 0.0473 8.94e- 2
#> 9 (Intercept) 9 0.325 0.0431 1.03e- 1
#> 10 (Intercept) 10 0.336 0.0392 1.14e- 1
#> # ℹ 937 more rows