Skip to contents

Extract Residuals

Source: R/residuals-methods.R
residuals.pense_fit.Rd

Extract residuals from a PENSE (or LS-EN) regularization path fitted by pense(), regmest() or elnet().

Usage

# S3 method for class 'pense_fit'
residuals(object, alpha = NULL, lambda, ...)

Arguments

object

PENSE regularization path to extract residuals from.

alpha

Either a single number or NULL (default). If given, only fits with the given alpha value are considered. If object was fit with multiple alpha values, and no value is provided, the first value in object$alpha is used with a warning.

lambda

a single number for the penalty level.

...

currently not used.

Value

a numeric vector of residuals for the given penalization level.

See also

Other functions for extracting components: coef.pense_cvfit(), coef.pense_fit(), predict.pense_cvfit(), predict.pense_fit(), residuals.pense_cvfit()

Examples

# Compute the LS-EN regularization path for Freeny's revenue data
# (see ?freeny)
data(freeny)
x <- as.matrix(freeny[ , 2:5])

regpath <- elnet(x, freeny$y, alpha = 0.75)

# Predict the response using a specific penalization level
predict(regpath, newdata = freeny[1:5, 2:5],
        lambda = regpath$lambda[[1]][[10]])
#>  1962.25   1962.5  1962.75     1963  1963.25 
#> 9.071638 9.075877 9.082341 9.091051 9.103643 

# Extract the residuals at a certain penalization level
residuals(regpath, lambda = regpath$lambda[[1]][[5]])
#>              Qtr1         Qtr2         Qtr3         Qtr4
#> 1962              -0.396169224 -0.398919454 -0.378220695
#> 1963 -0.384593892 -0.294338479 -0.278030430 -0.259140970
#> 1964 -0.265543684 -0.219857551 -0.206023748 -0.173545125
#> 1965 -0.192086388 -0.146253918 -0.133786817 -0.096671191
#> 1966 -0.090347926 -0.044601264 -0.027307275 -0.015531362
#> 1967  0.006739697  0.037235389  0.038869333  0.071407419
#> 1968  0.086275062  0.102036759  0.141643505  0.167310507
#> 1969  0.175437660  0.211500792  0.222942549  0.260029329
#> 1970  0.247870647  0.296181577  0.294470169  0.278217170
#> 1971  0.306686546  0.334684047  0.353729154  0.367702082

# Select penalization level via cross-validation
set.seed(123)
cv_results <- elnet_cv(x, freeny$y, alpha = 0.5,
                       cv_repl = 10, cv_k = 4)

# Predict the response using the "best" penalization level
predict(cv_results, newdata = freeny[1:5, 2:5])
#>  1962.25   1962.5  1962.75     1963  1963.25 
#> 8.795162 8.807070 8.824535 8.842175 8.882970 

# Extract the residuals at the "best" penalization level
residuals(cv_results)
#>              Qtr1         Qtr2         Qtr3         Qtr4
#> 1962              -0.002801588 -0.015699794 -0.009674689
#> 1963 -0.029165027  0.024539756  0.012794692  0.013400637
#> 1964 -0.014177641  0.006727561 -0.001786569  0.012847301
#> 1965 -0.026214283 -0.003738367 -0.010414370 -0.002497682
#> 1966 -0.014953173  0.014923561  0.008857587  0.002955549
#> 1967 -0.002438578  0.011336771 -0.004817002 -0.002203485
#> 1968 -0.014622332 -0.016038521  0.004783442  0.009509723
#> 1969  0.008935501  0.025135859  0.011008309  0.028171208
#> 1970 -0.021812388  0.015300404 -0.005936032 -0.018484409
#> 1971 -0.011338929  0.005850584  0.003783641  0.007952774
# Extract the residuals at a more parsimonious penalization level
residuals(cv_results, lambda = "1.5-se")
#>               Qtr1          Qtr2          Qtr3          Qtr4
#> 1962               -0.0133914763 -0.0253025812 -0.0180073467
#> 1963 -0.0375085950  0.0197904960  0.0063674275  0.0071084444
#> 1964 -0.0199445085  0.0028559476 -0.0059886764  0.0089958759
#> 1965 -0.0300906249 -0.0052788324 -0.0128420949 -0.0036364879
#> 1966 -0.0166186511  0.0137904162  0.0073750895  0.0016121587
#> 1967 -0.0021618673  0.0116385091 -0.0046227113  0.0002016235
#> 1968 -0.0117185912 -0.0127504673  0.0088610048  0.0131305182
#> 1969  0.0114094522  0.0285319822  0.0147126545  0.0325247699
#> 1970 -0.0158575338  0.0222103711  0.0004937030 -0.0127802705
#> 1971 -0.0035765461  0.0133307584  0.0117139290  0.0154227310