predict.AccurateGLM {aglm}

Make predictions for new data

Description

Make predictions for new data

Usage

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

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().

Value

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

Author(s)

• 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

#################### 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 <- [e-mail]

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)