prep_MRF_covariates

This function performs the cross-multiplication necessary
for prepping datasets to be used in <code>MRFcov</code> models. This
function is called by several of the functions within the package.

Approximate node interaction parameters of Markov Random Fields
graphical networks. Models can incorporate additional covariates, allowing users to estimate
how interactions between nodes in the graph are predicted to change across
covariate gradients. The general methods implemented in this package are described
in Clark et al. (2018) <doi:10.1002/ecy.2221>.

Nicholas J Clark

MRFcov

Markov Random Fields with Additional Covariates

Konstans Wells

Oscar Lindberg

prep_MRF_covariates function

<dl><dt>data</dt>
<dd>Dataframe. The input data where the <code>n_nodes</code>
left-most variables are outcome variables to be represented by nodes in the graph</dd>
<dt>n_nodes</dt>
<dd>Integer. The index of the last column in data
which is represented by a node in the final graph. Columns with index
greater than n_nodes are taken as covariates. Default is the number of
columns in data, corresponding to no additional covariates</dd></dl>

Arguments

Cross-multiply response and covariate variables — prep_MRF_covariates

<dl>

<dt>data</dt>
<dd>Dataframe. The input data where the <code>n_nodes</code>
left-most variables are outcome variables to be represented by nodes in the graph</dd>


<dt>n_nodes</dt>
<dd>Integer. The index of the last column in data
which is represented by a node in the final graph. Columns with index
greater than n_nodes are taken as covariates. Default is the number of
columns in data, corresponding to no additional covariates</dd>

</dl>

prep_MRF_covariates: Cross-multiply response and covariate variables

Description

Usage

Value

Arguments

Details