LGDtoolkit (version 0.2.0)

staged.blocks: Staged blocks regression

Description

staged.blocks performs blockwise regression where the predictions of each blocks' model is used as an offset for the model of the following block.

Usage

staged.blocks(method, target, db, blocks, reg.type = "ols", p.value = 0.05)

Value

The command staged.blocks returns a list of three objects.

The first object (model) is the list of the models of each block (an object of class inheriting from "lm").

The second object (steps), is the data frame with risk factors selected from the each block.

The third object (dev.db), returns the list of block's model development databases.

Arguments

method

Regression method applied on each block. Available methods: "stepFWD" or "stepRPC".

target

Name of target variable within db argument.

db

Modeling data with risk factors and target variable.

blocks

Data frame with defined risk factor groups. It has to contain the following columns: rf and block.

reg.type

Regression type. Available options are: "ols" for OLS regression and "frac.logit" for fractional logistic regression. Default is "ols". For "frac.logit" option, target has to have all values between 0 and 1.

p.value

Significance level of p-value for the estimated coefficient. For numerical risk factors this value is is directly compared to p-value of the estimated coefficient, while for categorical multiple Wald test is employed and its p-value is used for comparison with selected threshold (p.value).

See Also

embedded.blocks, ensemble.blocks, stepFWD and stepRPC.

Examples

library(LGDtoolkit)
data(lgd.ds.c)
#stepwise with continuous risk factors
set.seed(123)
blocks <- data.frame(rf = names(lgd.ds.c)[!names(lgd.ds.c)%in%"lgd"], 
		   block = sample(1:3, ncol(lgd.ds.c) - 1, rep = TRUE))
blocks <- blocks[order(blocks$block, blocks$rf), ]
res <- LGDtoolkit::staged.blocks(method = "stepFWD", 
		   target = "lgd",
		   db = lgd.ds.c,
		   reg.type = "ols", 
		   blocks = blocks,
		   p.value = 0.05)
names(res)
res$models
summary(res$models[[3]])
identical(unname(predict(res$models[[1]], newdata = res$dev.db[[1]])),
    res$dev.db[[2]]$offset.vals)