Glance accepts a model object and returns a tibble::tibble()
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modelling function. This includes the name of the modelling function or any arguments passed to the modelling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as NA
.
# S3 method for glmnet glance(x, ...)
x | A |
---|---|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Other glmnet tidiers: glance.cv.glmnet
,
tidy.cv.glmnet
, tidy.glmnet
A tibble::tibble()
with exactly one row and columns:
total passes over the data across all lambda values
null deviance
library(glmnet) set.seed(2014) x <- matrix(rnorm(100*20),100,20) y <- rnorm(100) fit1 <- glmnet(x,y) tidy(fit1)#> # A tibble: 1,086 x 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 V16 2 -0.00292 0.139 0.00464 #> 4 V17 2 -0.0148 0.139 0.00464 #> 5 (Intercept) 3 -0.209 0.127 0.0111 #> 6 V16 3 -0.0150 0.127 0.0111 #> 7 V17 3 -0.0286 0.127 0.0111 #> 8 (Intercept) 4 -0.210 0.115 0.0165 #> 9 V16 4 -0.0260 0.115 0.0165 #> 10 V17 4 -0.0412 0.115 0.0165 #> # ... with 1,076 more rowsglance(fit1)#> # A tibble: 1 x 2 #> nulldev npasses #> <dbl> <int> #> 1 104. 255library(dplyr) library(ggplot2) tidied <- tidy(fit1) %>% filter(term != "(Intercept)") ggplot(tidied, aes(step, estimate, group = term)) + 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: 976 x 5 #> term step estimate lambda dev.ratio #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 1 -0.241 0.177 1.78e-15 #> 2 (Intercept) 2 -0.241 0.161 1.57e- 2 #> 3 V13 2 0.0620 0.161 1.57e- 2 #> 4 (Intercept) 3 -0.241 0.147 2.88e- 2 #> 5 V13 3 0.119 0.147 2.88e- 2 #> 6 (Intercept) 4 -0.241 0.134 3.98e- 2 #> 7 V13 4 0.171 0.134 3.98e- 2 #> 8 (Intercept) 5 -0.242 0.122 4.91e- 2 #> 9 V13 5 0.220 0.122 4.91e- 2 #> 10 (Intercept) 6 -0.243 0.111 5.69e- 2 #> # ... with 966 more rows