tna (version 0.4.0)

compare: Compare Two Matrices or TNA Models with Comprehensive Metrics

Description

Various distances, measures of dissimilarity and similarity, correlations and other metrics are computed to compare the models. Optionally, the weight matrices of the models can be scaled before comparison. The resulting object can be used to produce heatmap plots and scatterplots to further illustrate the differences.

Usage

compare(x, ...)
# S3 method for tna
compare(x, y, scaling = "none", ...)
# S3 method for matrix
compare(x, y, scaling = "none", ...)

Value

A tna_comparison object, which is a list containing the following elements:

• matrices: A list containing the scaled matrices of the input tna objects or the scaled inputs themselves in the case of matrices.

• difference_matrix: A matrix of differences x - y.

• edge_metrics: A data.frame of edge-level metrics about the differences.

• summary_metrics: A data.frame of summary metrics of the differences across all edges.

• network_metrics: A data.frame of network metrics for both x and y.

• centrality_differences: A data.frame of differences in centrality measures computes from x and y.

• centrality_correlations: A numeric vector of correlations of the centrality measures between x and y.

Arguments

x

A tna object or a matrix of weights.

...

Ignored.

y

A tna object or a matrix of weights.

scaling

A character string naming a scaling method to apply to the weights before comparing them. The supported options are:

• "none": No scaling is performed. The weights are used as is.

• "minmax": Performs min-max normalization, i.e., the minimum value is subtracted and the differences are scaled by the range.

• "rank": Applies min-max normalization to the ranks of the weights (computed with ties.method = "average").

• "zscore": Computes the standard score, i.e. the mean weight is subtracted and the differences are scaled by the standard deviation.

• "robust": Computes the robust z-score, i.e. the median weight is subtracted and the differences are scaled by the median absolute deviation (using stats::mad).

• "log": Simply the natural logarithm of the weights.

• "log1p": As above, but adds 1 to the values before taking the logarithm. Useful for scenarios with zero weights.

• "softmax": Performs softmax normalization.

• "quantile": Uses the empirical quantiles of the weights via stats::ecdf.

Examples

# Comparing TNA models
model_x <- tna(group_regulation[1:200, ])
model_y <- tna(group_regulation[1001:1200, ])
comp1 <- compare(model_x, model_y)

# Comparing matrices
mat_x <- model_x$weights
mat_y <- model_y$weights
comp2 <- compare(mat_x, mat_y)

# Comparing a matrix to a TNA model
comp3 <- compare(mat_x, model_y)