Description Usage Arguments Details Value Author(s) See Also Examples
Similar to usual predict methods and predict.grprep
in grpreg
package.
1 2 3 |
object |
A fitted " |
newdata |
Optionally, a matrix or data frame where to predict. If omits, the fitted predictors are used. |
lambda |
Value of the regularization parameter |
type |
The type of prediction: " |
... |
Not used. |
This function gives the predictions at newdata
or all predictors if the
argument newdata
is not supplied. The default lambda
for "grpreg
"
object is the one at which we obtain the minimum loss value, i.e., negative log-likelihood
value. Typically, type = "response"
is
used for linear or poisson regression, and type = "class"
or
type = "probability"
is used for logistic regression.
The predicted values depending on the type.
Debin Qiu, Jeongyoun Ahn
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | library(MASS)
set.seed(23)
n <- 30 # sample size
p <- 3 # number of predictors in each group
J <- 50 # group size
group <- rep(1:J,each = 3) # group indices
X <- mvrnorm(n,seq(0,5,length.out = p*J),diag(p*J))
beta <- runif(12,-2,5)
mu <- X%*%matrix(c(beta,rep(0,p*J-12)),ncol = 1)
# linear regression with family = "gaussian"
y <- mu + rnorm(n)
## without cross-validation
gss12 <- grpss(X,y,ncut = 10,group,select = TRUE)
predict(gss12) # fitted values
predict(gss12,lambda = 0.2) # fitted values at lambda = 0.2
# logistic regression with family = "binomial"
set.seed(23)
y1 <- rbinom(n,1,1/(1 + exp(-mu)))
gss21 <- grpss(X,y1,group, criterion = "gDC",select = TRUE,
family = "binomial")
predict(gss21)
|
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