predict.AccurateGLM: Make predictions for new data

Description

Make predictions for new data

Usage

# S3 method for AccurateGLM
predict(
  object,
  newx = NULL,
  s = NULL,
  type = c("link", "response", "coefficients", "nonzero", "class"),
  exact = FALSE,
  newoffset,
  ...
)

Value

The returned object depends on type. See predict.glmnet for more details.

Arguments

object: A model object obtained from aglm() or cv.aglm().
newx: A design matrix for new data. See the description of x in aglm for more details.
s: Same as in predict.glmnet.
type: Same as in predict.glmnet.
exact: Same as in predict.glmnet.
newoffset: Same as in predict.glmnet.
...: Other arguments are passed directly when calling predict.glmnet().

Author

Kenji Kondo,
Kazuhisa Takahashi and Hikari Banno (worked on L-Variable related features)

References

Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa. (2020) AGLM: A Hybrid Modeling Method of GLM and Data Science Techniques,
https://www.institutdesactuaires.com/global/gene/link.php?doc_id=16273&fg=1
Actuarial Colloquium Paris 2020

Examples

Run this code


#################### using plot() and predict() ####################

library(MASS) # For Boston
library(aglm)

## Read data
xy <- Boston # xy is a data.frame to be processed.
colnames(xy)[ncol(xy)] <- "y" # Let medv be the objective variable, y.

## Split data into train and test
n <- nrow(xy) # Sample size.
set.seed(2018) # For reproducibility.
test.id <- sample(n, round(n/4)) # ID numbders for test data.
test <- xy[test.id,] # test is the data.frame for testing.
train <- xy[-test.id,] # train is the data.frame for training.
x <- train[-ncol(xy)]
y <- train$y
newx <- test[-ncol(xy)]
y_true <- test$y

## With the result of aglm()
model <- aglm(x, y)
lambda <- 0.1

plot(model, s=lambda, resid=TRUE, add_rug=TRUE,
     verbose=FALSE, layout=c(3, 3))

y_pred <- predict(model, newx=newx, s=lambda)
plot(y_true, y_pred)

## With the result of cv.aglm()
model <- cv.aglm(x, y)
lambda <- model@lambda.min

plot(model, s=lambda, resid=TRUE, add_rug=TRUE,
     verbose=FALSE, layout=c(3, 3))

y_pred <- predict(model, newx=newx, s=lambda)
plot(y_true, y_pred)

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