mgm (version 1.2-2)

resample: Resampling scheme for mgm objects

Description

Fits mgm model types (mgm, mvar, tvmgm, tvmvar) to a specified number of bootstrap samples

Usage

resample(object, data, nB, seeds, blocks, quantiles, pbar, ...)

Arguments

object

An mgm model object, the output of mgm(), tvmgm(), mvar(), tvmvar(). The model specifications for all fitted models are taken from this model object.

data

The n x p data matrix.

nB

The number of bootstrap samples.

seeds

A integer vector of length nB, providing random seeds for each bootstrap sample. Defaults to seeds = 1:nB.

blocks

The number of blocks for the block bootstrap used for time-varying models.

quantiles

A vector with values in [0, 1], specifying quantiles computed on the bootstrapped sampling distributions. Defaults to quantiles = c(.05, .95)

pbar

If TRUE, a progress bar is shown. Defaults to pbar = TRUE.

…

Additional arguments.

Value

The output consists of a list with the entries

call

Contains the function call.

models

A list with nB entries, containing the models fit to the bootstrapped sampels.

bootParameters

Contains all the bootstrapped sampling distribution of all parameters. The dimension of this object depends on the type of model. Specifically, this object has the same dimension as the main parameter output of the corresponding estimation function, with one dimension added for the bootstrap iterations. See "Details" above.

bootQuantiles

Contains the specified quantiles of the bootstrapped sampling distribution for each parameter. Has the same structure as bootParameters. See "Details" above.

Times

Returns the running time for each bootstrap model in seconds.

totalTime

Returns the running time for all bootstrap models together in seconds.

Details

resample() fits a model specified via the object argument to nB bootstrap samples obtained from the orginial dataset. For stationary models (mgm() and mvar()) objects, we use the standard bootstrap. For time-varying models (tvmgm() and tvmvar()) we use the block bootstrap.

For mvar models, bootParameters is a p x p x nlags x nB array, where p is the number of variables, nlags is the number of specified lags, and nB is the number of bootstrap samples. Thus bootParameters[7, 3, 2, ] returns the bootstrapped sampling distribution of the lagged effect from variable 3 on 7 for the 2nd specified lag. See also ?mvar.

For tvmar models, bootParameters is a p x p x nlags x nestpoints x nB array, analogously to mvar models. nestpoints is the number of specified estpoints. See also ?tvmvar.

Resampling is currently only supported for pairwise MGMs (k = 2). For mgms, bootParameters is a p x p x nB array. For tvmgms, bootParameters is a p x p x nestpoint x nB array.

References

Haslbeck, J., & Waldorp, L. J. (2017). mgm: Estimating time-varying Mixed Graphical Models in high-dimensional Data. arXiv preprint arXiv:1510.06871.

Examples

# NOT RUN {
# }
# NOT RUN {

# 1) Fit mgm to example dataset (true edges: 1-4, 2-3, 1-2)

mgm_obj <- mgm(data = mgm_data$data, 
               type = c('g', 'c', 'c', 'g'),
               level = c(1, 2, 4, 1),
               k = 2, 
               lambdaSel = 'CV', 
               threshold = 'none')


# 2) Take 50 bootstrap samples using resample()

res_obj <- resample(object = mgm_obj, 
                    data = mgm_data$data,
                    nB = 50)


# 3) Plot histogram of bootstrapped sampling distribution of edge 1-4

hist(res_obj$bootParameters[1, 4, ], 
     main = "", 
     xlab = "Parameter Estimate")


# }
# NOT RUN {

# }