Predict response values using a PENSE (or LS-EN) regularization path fitted by
pense(), regmest() or elnet().
# S3 method for class 'pense_fit'
predict(
object,
newdata,
alpha = NULL,
lambda,
exact = deprecated(),
correction = deprecated(),
...
)PENSE regularization path to extract residuals from.
an optional matrix of new predictor values. If missing, the fitted values are computed.
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$alpha is used with a warning.
a single number for the penalty level.
defunct Always gives a warning if lambda is not part of the fitted
sequence and coefficients need to be interpolated.
defunct.
currently not used.
a numeric vector of residuals for the given penalization level.
Other functions for extracting components:
coef.pense_cvfit(),
coef.pense_fit(),
predict.pense_cvfit(),
residuals.pense_cvfit(),
residuals.pense_fit()
# Compute the LS-EN regularization path for Freeny's revenue data
# (see ?freeny)
data(freeny)
x <- as.matrix(freeny[ , 2:5])
regpath <- elnet(x, freeny$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