predict.ordinis: Prediction method for coord lasso fitted objects

In jaredhuling/ordinis: Fits penalized regression models with coordinate descent

Description

Prediction method for coord lasso fitted objects

Usage

## S3 method for class 'ordinis'
predict(object, newx, s = NULL, type = c("link",
  "response", "coefficients", "nonzero", "class"), ...)

Arguments

object	fitted "ordinis" model object
newx	Matrix of new values for x at which predictions are to be made. Must be a matrix; can be sparse as in the CsparseMatrix objects of the Matrix package. This argument is not used for type=c("coefficients","nonzero")
s	Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.
type	Type of prediction required. type = "link" gives the linear predictors for the "binomial" model; for "gaussian" models it gives the fitted values. type = "response" gives the fitted probabilities for "binomial". type = "coefficients" computes the coefficients at the requested values for s. type = "class" applies only to "binomial" and produces the class label corresponding to the maximum probability.
...	not used

Value

An object depending on the type argument

Examples

set.seed(123)
n.obs <- 1e4
n.vars <- 100
n.obs.test <- 1e3

true.beta <- c(runif(15, -0.5, 0.5), rep(0, n.vars - 15))

x <- matrix(rnorm(n.obs * n.vars), n.obs, n.vars)
y <- rnorm(n.obs, sd = 3) + x %*% true.beta
x.test <- matrix(rnorm(n.obs.test * n.vars), n.obs.test, n.vars)
y.test <- rnorm(n.obs.test, sd = 3) + x.test %*% true.beta

fit <- ordinis(x = x, y = y, nlambda = 10)

preds.lasso <- predict(fit, newx = x.test, type = "response")

apply(preds.lasso, 2, function(x) mean((y.test - x) ^ 2))

jaredhuling/ordinis documentation built on May 23, 2019, 4:03 a.m.