biglasso (version 1.0-1)

predict.biglasso: Model predictions based on a fitted "biglasso" object.

Description

Extract predictions (fitted reponse, coefficients, etc.) from a fitted biglasso object.

Usage

"predict"(object, X, row.idx = 1:nrow(X),  type = c("link", "response", "class", "coefficients", "vars", "nvars"), lambda, which = 1:length(object$lambda), ...)
"coef"(object, lambda, which = 1:length(object$lambda), drop = TRUE, ...)

Arguments

object

A fitted "biglasso" model object.

X

Matrix of values at which predictions are to be made. It must be a big.matrix object. Not used for type="coefficients".

row.idx

Similar to that in biglasso, it's a vector of the row indices of X that used for the prediction. 1:nrow(X) by default.

type

Type of prediction: "link" returns the linear predictors; "response" gives the fitted values; "class" returns the binomial outcome with the highest probability; "coefficients" returns the coefficients; "vars" returns a list containing the indices and names of the nonzero variables at each value of lambda; "nvars" returns the number of nonzero coefficients at each value of lambda.

lambda

Values of the regularization parameter lambda at which predictions are requested. Linear interpolation is used for values of lambda not in the sequence of lambda values in the fitted models.

which

Indices of the penalty parameter lambda at which predictions are required. By default, all indices are returned. If lambda is specified, this will override which.

drop

If coefficients for a single value of lambda are to be returned, reduce dimensions to a vector? Setting drop=FALSE returns a 1-column matrix.

...

Not used.

Value

type.

See Also

biglasso, cv.biglasso

Examples

## Logistic regression
data(heart)
X <- as.matrix(heart[,1:9])
y <- heart$chd
X <- as.big.matrix(X)
fit <- biglasso(X, y, penalty = 'lasso', family = "binomial")
coef(fit, lambda=0.05, drop = FALSE)
predict(fit, X, type="link", lambda=0.05)
predict(fit, X, type="response", lambda=0.05)
predict(fit, X, type="class", lambda=0.1)
predict(fit, type="vars", lambda=c(0.05, 0.1))
predict(fit, type="nvars", lambda=c(0.05, 0.1))