cva.aglm {aglm}R Documentation

Fit an AGLM model with cross-validation for both α and λ

Description

A fitting function with cross-validation for both α and λ. See aglm-package for more details on α and λ.

Usage

cva.aglm(
  x,
  y,
  alpha = seq(0, 1, len = 11)^3,
  nfolds = 10,
  foldid = NULL,
  parallel.alpha = FALSE,
  ...
)

Arguments

x

A design matrix. See aglm for more details.

y

A response variable.

alpha

A numeric vector representing α values to be examined in cross-validation.

nfolds

An integer value representing the number of folds.

foldid

An integer vector with the same length as observations. Each element should take a value from 1 to nfolds, identifying which fold it belongs.

parallel.alpha

(not used yet)

...

Other arguments are passed directly to cv.aglm().

Value

An object storing fitted models and information of cross-validation. See CVA_AccurateGLM-class for more details.

Author(s)

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


#################### Cross-validation for alpha and lambda ####################

library(aglm)
library(faraway)

## Read data
xy <- nes96

## Split data into train and test
n <- nrow(xy) # Sample size.
set.seed(2018) # For reproducibility.
test.id <- sample(n, round(n/5)) # 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[, c("popul", "TVnews", "selfLR", "ClinLR", "DoleLR", "PID", "age", "educ", "income")]
y <- train$vote
newx <- test[, c("popul", "TVnews", "selfLR", "ClinLR", "DoleLR", "PID", "age", "educ", "income")]

# NOTE: Codes bellow will take considerable time, so run it when you have time.


## Fit the model
cva_result <- cva.aglm(x, y, family="binomial")

alpha <- cva_result@alpha.min
lambda <- cva_result@lambda.min

mod_idx <- cva_result@alpha.min.index
model <- cva_result@models_list[[mod_idx]]

## Make the confusion matrix
y_true <- test$vote
y_pred <- levels(y_true)[as.integer(predict(model, newx, s=lambda, type="class"))]

cat(sprintf("Confusion matrix for alpha=%.5f and lambda=%.5f:\n", alpha, lambda))
print(table(y_true, y_pred))



[Package aglm version 0.4.0 Index]