plot.AccurateGLM: Plot contribution of each variable and residuals
In aglm: Accurate Generalized Linear Model

Description Usage Arguments Value Author(s) References Examples

Plot contribution of each variable and residuals

## S3 method for class 'AccurateGLM'
plot(
  x,
  vars = NULL,
  verbose = TRUE,
  s = NULL,
  resid = FALSE,
  smooth_resid = TRUE,
  smooth_resid_fun = NULL,
  ask = TRUE,
  layout = c(2, 2),
  only_plot = FALSE,
  main = "",
  add_rug = FALSE,
  ...
)

`x`	A model object obtained from `aglm()` or `cv.aglm()`.
`vars`	Used to specify variables to be plotted (`NULL` means all the variables). This parameter may have one of the following classes: `integer`: specifying variables by index. `character`: specifying variables by name.
`verbose`	Set to `FALSE` if textual outputs are not needed.
`s`	A numeric value specifying λ at which plotting is required. Note that plotting for multiple λ's are not allowed and `s` always should be a single value. When the model is trained with only a single λ value, just set it to `NULL` to plot for that value.
`resid`	Used to display residuals in plots. This parameter may have one of the following classes: `logical`(single value): If `TRUE`, working residuals are plotted. `character`(single value): type of residual to be plotted. See residuals.AccurateGLM for more details on types of residuals. `numerical`(vector): residual values to be plotted.
`smooth_resid`	Used to display smoothing lines of residuals for quantitative variables. This parameter may have one of the following classes: `logical`: If `TRUE`, smoothing lines are drawn. `character`: `smooth_resid="both"`: Balls and smoothing lines are drawn. `smooth_resid="smooth_only"`: Only smoothing lines are drawn.
`smooth_resid_fun`	Set if users need custom smoothing functions.
`ask`	By default, `plot()` stops and waits inputs each time plotting for each variable is completed. Users can set `ask=FALSE` to avoid this. It is useful, for example, when using devices as `bmp` to create image files.
`layout`	Plotting multiple variables for each page is allowed. To achieve this, set it to a pair of integer, which indicating number of rows and columns, respectively.
`only_plot`	Set to `TRUE` if no automatic graphical configurations are needed.
`main`	Used to specify the title of plotting.
`add_rug`	Set to `TRUE` for rug plots.
`...`	Other arguments are currently not used and just discarded.

No return value, called for side effects.

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

#################### 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)