boot.cgaim {cgaim}R Documentation

Boostrap CGAIM


Generates bootstrap replicates of a cgaim object.


boot.cgaim(object, boot.type = c("residuals", "wild", "pairs"),
  bsamples = NULL, B = 100, l = 1, nc = 1)



A cgaim object.


The type of bootstrap to perform. Currently available type are "residuals", "wild" and "pairs". See details


A numerical matrix of observation indices specifying bootstrap samples. Rows indicate observations and columns bootstrap samples. If NULL (the default), samples are generated internally.


Number of bootstrap samples to generate when bsamples = NULL.


Block length for block-bootstrap. Samples are generated by resampling block of observation of length l. The classical bootstrap corresponds to l = 1 (the default).


Positive integer. If nc > 1, the function is parallelized with nc indicating the number of cores to use.


This function fits the cgaim on bootstrap samples. It is called internally by the confint.cgaim function, but can also be called directly to generate various statistics.

Three types of bootstrap are currently implemented. "residuals" (the default) resamples the residuals in object to then be added to fitted values, creating alternative response vectors. The cgaim is then fitted on these newly generated y values with the original x. "wild" is similar except that residuals are multiplied by random draws from a standard normal distribution before being added to fitted values. "pairs" resamples directly pairs of y and x to create bootstrap samples.

Bootstrap samples can either be prespecified by the user through bsamples or generated internally. In the former case, the columns of bsamples indicate the number of replications B and the rows should match the original number of observations. Internally generated bootstrap samples are controlled by the number of replications B and block length l, implementing block bootstrap. The latter is particularly recommended for time series data.

As fitting a large number of cgaim models can be computationally intensive, the function can be run in parallel, using the doParallel package. This can be done by setting the argument nc to a value greater than 1, controlling the number of cores used in parallelization.


A boot.cgaim object with components


The bootstrap result. A list that includes all B replications of alpha, beta, gfit and indexfit organized in arrays.


The original object passed to the function.


The bootstrap samples. A matrix with indices corresponding to original observations.


The type of bootstrap performed.


The number of bootstrap replications.


The block length for block bootstrap.


# A simple CGAIM
n <- 200
x1 <- rnorm(n)
x2 <- x1 + rnorm(n)
z <- x1 + x2
y <- z + rnorm(n)
df1 <- data.frame(y, x1, x2) 
ans <- cgaim(y ~ g(x1, x2, acons = list(monotone = 1)), data = df1)

# Use function to compute confidence intervals (B should be increased)
boot1 <- boot.cgaim(ans, B = 10)
ci1 <- confint(boot1)

# Produces the same result as
ci2 <- confint(ans, type = "boot", B = 10)

# Create sampling beforehand
bsamp <- matrix(sample(1:n, n * 10, replace = TRUE), n)
boot2 <- boot.cgaim(ans, bsamples = bsamp)

# Parallel computing (two cores)

boot3 <- boot.cgaim(ans, nc = 2)

[Package cgaim version 1.0.0 Index]