Predict from a fitted cv.rbridge object

Compute response predictions, extract coefficients, or identify nonzero predictors from a fitted cv.rbridge object.

Usage

# S3 method for class 'cv.rbridge'
predict(
  object,
  newx,
  s = c("lambda.min", "lambda.1se"),
  type = c("response", "nonzero", "coefficients"),
  ...
)

Arguments

object

Fitted object returned by cv.rbridge, with p predictors in its training design.

newx

Numeric matrix with one row per new observation and the same p columns, in the same order, as the training design. A numeric vector of length p represents one observation. Values are supplied on the original predictor scale. Required only for type = "response".

s

Penalty selection, using numeric fitted values or a character name. Numeric values must match object$lambda exactly; no interpolation is used. The character selectors are:

"lambda.min": Select the penalty with minimum CV error.
"lambda.1se": Select using the historical CV error bars.

The default is lambda.min.

type

Output to return: "response" for fitted response values, "coefficients" for intercept and slopes, or "nonzero" for predictor indices over the entire fitted path.

...

Additional arguments accepted for compatibility.

Value

For type = "response", a numeric vector for one selected penalty or a matrix with one row per new observation and one column per selected penalty. For type = "coefficients", a numeric matrix with p + 1 rows, including the intercept, even for one penalty. For type = "nonzero", a pathwise collection of 1-based predictor indices excluding the intercept. Its historical container can simplify depending on the support pattern and number of predictors.

Details

Prediction uses the fitted intercept and original-scale slopes. Columns of newx are matched by position, not reordered by their names. For type = "nonzero", all fitted penalties are inspected and s does not restrict the returned indices. The defaults differ between coef() and predict(): coefficient extraction uses lambda.1se, whereas response prediction uses lambda.min. Specify the same s in both calls when comparing coefficients and their predictions. Returned slopes are on the original predictor scale; the linear restrictions concern standardized slopes. See rbridge for converting scales before checking restriction residuals.

Author

Bahadir Yuzbasi
Maintainer: Bahadir Yuzbasi b.yzb@hotmail.com

Examples

# Reproducible synthetic demonstration: 100 observations, eight predictors.
set.seed(2019)
beta <- c(3, 1.5, 0, 0, 2, 0, 0, 0)
p <- length(beta)
beta <- matrix(beta, nrow = p, ncol = 1)
n <- 100
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
y <- as.numeric(X %*% beta + rnorm(n))
# A user-specified sum of standardized slopes: b[1] + b[2] + b[5] = 6.5.
# This target is illustrative; it is not asserted to equal the generating sum.
R1.mat <- matrix(c(1, 1, 0, 0, 1, 0, 0, 0), nrow = 1)
r1.vec <- matrix(6.5, nrow = 1, ncol = 1)

# Five penalties keep this help example fast; the default is 100.
model1 <- cv.rbridge(X, y, q = 1, R = R1.mat, r = r1.vec, nlambda = 5,
                     nfolds = 3, num_threads = 1)
# Predict five observations at one explicitly selected penalty.
predict(model1, newx = X[1:5, , drop = FALSE], s = "lambda.min")
#> [1]  -1.6233986   0.3470059  -1.5648146   2.6330664 -10.4462475
# Extract coefficients using the complete type name.
predict(model1, s = "lambda.min", type = "coefficients")
#>             802.149726533951
#> (Intercept)       0.01283347
#> V1                2.98320054
#> V2                1.57870923
#> V3                0.00000000
#> V4                0.00000000
#> V5                1.99412306
#> V6                0.00000000
#> V7                0.00000000
#> V8                0.00000000
# Predictor indices for every fitted penalty; the intercept is excluded.
predict(model1, type = "nonzero")
#> $`802.149726533951`
#> [1] 1 2 5
#> 
#> $`26.8035044402758`
#> [1] 1 2 3 4 5 6 7
#> 
#> $`0.895628118436414`
#> [1] 1 2 3 4 5 6 7 8
#> 
#> $`0.0299270466039737`
#> [1] 1 2 3 4 5 6 7 8
#> 
#> $`0.001`
#> [1] 1 2 3 4 5 6 7 8
#>