predict.cv.biglasso: Model predictions based on a fitted 'cv.biglasso()' object

In biglasso: Extending Lasso Model Fitting to Big Data

Model predictions based on a fitted cv.biglasso() object

Description

Extract predictions from a fitted cv.biglasso() object.

Usage

## S3 method for class 'cv.biglasso'
predict(
  object,
  X,
  row.idx = 1:nrow(X),
  type = c("link", "response", "class", "coefficients", "vars", "nvars"),
  lambda = object$lambda.min,
  which = object$min,
  ...
)

## S3 method for class 'cv.biglasso'
coef(object, lambda = object$lambda.min, which = object$min, ...)

Arguments

object	A fitted "cv.biglasso" model object.
X	Matrix of values at which predictions are to be made. It must be a bigmemory::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. The default value is the one corresponding to the minimum cross-validation error. Accepted values are also the strings "lambda.min" (lambda of minimum cross-validation error) and "lambda.1se" (Largest value of lambda for which the cross-validation error was at most one standard error larger than the minimum.).
which	Indices of the penalty parameter lambda at which predictions are requested. The default value is the index of lambda corresponding to lambda.min. Note: this is overridden if lambda is specified.
...	Not used.

Value

The object returned depends on type.

Author(s)

Yaohui Zeng and Patrick Breheny

See Also

biglasso(), cv.biglasso()

Examples

## Not run: 
## predict.cv.biglasso
data(colon)
X <- colon$X
y <- colon$y
X.bm <- as.big.matrix(X, backingfile = "")
fit <- biglasso(X.bm, y, penalty = 'lasso', family = "binomial")
cvfit <- cv.biglasso(X.bm, y, penalty = 'lasso', family = "binomial", seed = 1234, ncores = 2)
coef <- coef(cvfit)
coef[which(coef != 0)]
predict(cvfit, X.bm, type = "response")
predict(cvfit, X.bm, type = "link")
predict(cvfit, X.bm, type = "class")
predict(cvfit, X.bm, lambda = "lambda.1se")

## End(Not run)

biglasso documentation built on May 29, 2024, 1:50 a.m.