predict.lqa {lqa} | R Documentation |
This function computes predictions based on an lqa
object.
## S3 method for class 'lqa' predict(object, new.x = NULL, new.y = NULL, weights = rep(1, n.newobs), ...) ## S3 method for class 'pred.lqa' print(x, ...)
object |
a fitted object of class |
new.x |
Optionally, a new data frame from which to make the predictions. If omitted, the fitted linear predictors are used.
Note, if given |
new.y |
Optionally, a vector of new responses. If given, the deviance can be computed. |
weights |
an optional vector including weights of the new observations. |
x |
an object of class |
... |
additional arguments. |
predict.lqa
returns an object of class pred.lqa
, i.e. this is a list with the following elements
deviance |
the deviance based on the new observations. This element is NULL if new.y = NULL, i.e. no new responses are used in |
tr.H |
the trace of the hat matrix of the design matrix used to fit the model. This is just an extraction from the |
n.newobs |
the number of new observations. |
eta.new |
the estimated new predictors. |
mu.new |
the estimated new responses. |
lqa.obj |
the |
new.y |
the |
Jan Ulbricht
set.seed (1111) n <- 200 p <- 5 X <- matrix (rnorm (n * p), ncol = p) X[,2] <- X[,1] + rnorm (n, sd = 0.1) X[,3] <- X[,1] + rnorm (n, sd = 0.1) true.beta <- c (1, 2, 0, 0, -1) y <- drop (X %*% true.beta) + rnorm (n) cv.obj1 <- cv.lqa (y, X, intercept = TRUE, lambda.candidates = list (c (0.001, 0.05, 1, 5, 10), c (0.1, 0.5, 1)), family = gaussian (), penalty.family = fused.lasso, loss.func = "gcv.loss") cv.obj1 beta0.hat <- coef (cv.obj1$best.obj)[1] # extracts the estimated intercept pred.obj <- predict.lqa (cv.obj1$best.obj, new.x = c (beta0.hat, 1, 2, 3, 4, 5)) pred.obj cv.obj2 <- cv.lqa (y, X, intercept = TRUE, lambda.candidates = list (c (0.001, 0.05, 1, 5, 10), c (0.1, 0.5, 1)), family = gaussian (), penalty.family = fused.lasso, n.fold = 5, loss.func = "squared.loss") cv.obj2 beta0.hat <- coef (cv.obj2$best.obj)[1] # extracts the estimated intercept predict.lqa (cv.obj2$best.obj, new.x = cbind (beta0.hat, matrix (1 : 10, nrow = 2)))