Description Usage Arguments Value Author(s) References Examples
Make predictions for new data
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object |
A model object obtained from |
newx |
A design matrix for new data.
See the description of |
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 |
The returned object depends on type
.
See predict.glmnet 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | #################### 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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