Model Predictions

Model Predictions from ridge regression

Usage

# S4 method for class 'lmRidge'
predict(object, newdata, lambda, ...)

Arguments

object

fit from lmRidge()

newdata

list storing design and X matrices

lambda

vector or scalar of regularization parameter values

...

other parameters, not used

Value

predict response for each lambda value

See also

lmRidge()

Examples

y = longley$GNP.deflator
design = model.matrix(~ 1, longley)
X = longley[,-1]

lambda = seq(0, .05, length.out=40)

fit = lmRidge(y, design, X, lambda = lambda)

# get coefs with best GCV
i = which.min(fit$GCV)

# predict
predict(fit, 
  newdata = list(design = design, X = X), 
  lambda = fit$lambda[i])
#>      0.005128205
#> 1947    83.70544
#> 1948    86.87336
#> 1949    88.14397
#> 1950    90.87599
#> 1951    95.90758
#> 1952    97.69792
#> 1953    98.49161
#> 1954   100.11293
#> 1955   103.26701
#> 1956   105.16366
#> 1957   107.47808
#> 1958   109.44567
#> 1959   112.71412
#> 1960   113.94012
#> 1961   115.38703
#> 1962   117.69551