extractPath

Extracts the coefficient path of the elastic net

Usage

extractPath(model, ...)

# S3 method for glmnet
extractPath(model, intercept = FALSE, ...)

# S3 method for cv.glmnet
extractPath(model, ...)

Arguments

model

A glmnet model

...

Further arguments

intercept

If FALSE (the default), no intercept will be provided

Value

A link[tibble]{tibble} holding the coefficients for various lambdas

Details

This is a replacement plot for visualizing the coefficient path resulting from the elastic net.

Author

Jared P. Lander

Examples

library(glmnet)
data(diamonds, package='ggplot2')
diaX <- useful::build.x(price ~ carat + cut + x - 1, data=diamonds, contrasts = TRUE)
diaY <- useful::build.y(price ~ carat + cut + x - 1, data=diamonds)
modG1 <- glmnet(x=diaX, y=diaY)
extractPath(modG1)
#> # A tibble: 79 × 8
#>    lambda  carat cutFair cutGood `cutVery Good` cutPremium cutIdeal     x
#>     <dbl>  <dbl>   <dbl>   <dbl>          <dbl>      <dbl>    <dbl> <dbl>
#>  1  0.953 10054.  -1511.   -354.          22.6       -4.64     319. -951.
#>  2  1.05  10034.  -1509.   -354.          21.5       -5.10     318. -942.
#>  3  1.14  10011.  -1508.   -354.          20.2       -5.60     317. -933.
#>  4  1.23   9985.  -1506.   -354.          18.9       -6.16     316. -922.
#>  5  1.33   9955.  -1504.   -354.          17.5       -6.77     314. -909.
#>  6  1.42   9924.  -1502.   -353.          16.0       -7.29     313. -896.
#>  7  1.51   9890.  -1499.   -353.          14.5       -7.78     312. -882.
#>  8  1.60   9852.  -1497.   -352.          12.9       -8.24     310. -866.
#>  9  1.70   9809.  -1493.   -351.          11.3       -8.67     309. -848.
#> 10  1.79   9765.  -1490.   -350.           9.69      -8.91     307. -830.
#> # ℹ 69 more rows

modG2 <- cv.glmnet(x=diaX, y=diaY, nfolds=5)
extractPath(modG2)
#> # A tibble: 79 × 8
#>    lambda  carat cutFair cutGood `cutVery Good` cutPremium cutIdeal     x
#>     <dbl>  <dbl>   <dbl>   <dbl>          <dbl>      <dbl>    <dbl> <dbl>
#>  1  0.953 10054.  -1511.   -354.          22.6       -4.64     319. -951.
#>  2  1.05  10034.  -1509.   -354.          21.5       -5.10     318. -942.
#>  3  1.14  10011.  -1508.   -354.          20.2       -5.60     317. -933.
#>  4  1.23   9985.  -1506.   -354.          18.9       -6.16     316. -922.
#>  5  1.33   9955.  -1504.   -354.          17.5       -6.77     314. -909.
#>  6  1.42   9924.  -1502.   -353.          16.0       -7.29     313. -896.
#>  7  1.51   9890.  -1499.   -353.          14.5       -7.78     312. -882.
#>  8  1.60   9852.  -1497.   -352.          12.9       -8.24     310. -866.
#>  9  1.70   9809.  -1493.   -351.          11.3       -8.67     309. -848.
#> 10  1.79   9765.  -1490.   -350.           9.69      -8.91     307. -830.
#> # ℹ 69 more rows