cv.aglm: Fit an AGLM model with cross-validation for lambda
In aglm: Accurate Generalized Linear Model

Description Usage Arguments Value Author(s) References Examples

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

cv.aglm(
  x,
  y,
  qualitative_vars_UD_only = NULL,
  qualitative_vars_both = NULL,
  qualitative_vars_OD_only = NULL,
  quantitative_vars = NULL,
  use_LVar = FALSE,
  extrapolation = "default",
  add_linear_columns = TRUE,
  add_OD_columns_of_qualitatives = TRUE,
  add_interaction_columns = FALSE,
  OD_type_of_quantitatives = "C",
  nbin.max = NULL,
  bins_list = NULL,
  bins_names = NULL,
  family = c("gaussian", "binomial", "poisson"),
  keep = FALSE,
  ...
)

`x`	A design matrix. See aglm for more details.
`y`	A response variable.
`qualitative_vars_UD_only`	Same as in aglm.
`qualitative_vars_both`	Same as in aglm.
`qualitative_vars_OD_only`	Same as in aglm.
`quantitative_vars`	Same as in aglm.
`use_LVar`	Same as in aglm.
`extrapolation`	Same as in aglm.
`add_linear_columns`	Same as in aglm.
`add_OD_columns_of_qualitatives`	Same as in aglm.
`add_interaction_columns`	Same as in aglm.
`OD_type_of_quantitatives`	Same as in aglm.
`nbin.max`	Same as in aglm.
`bins_list`	Same as in aglm.
`bins_names`	Same as in aglm.
`family`	Same as in aglm.
`keep`	Set to `TRUE` if you need the `fit.preval` field in the returned value, as in `cv.glmnet()`.
`...`	Other arguments are passed directly when calling `cv.glmnet()`.

A model object fitted to the data with cross-validation results. Functions such as predict and plot can be applied to the returned object, same as the result of aglm(). See AccurateGLM-class for more details.

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

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

#################### Cross-validation for 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
model <- cv.aglm(x, y, family="binomial")

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

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