globalMetric: Global metric score defined by a prediction.

In evaluomeR: Evaluation of Bioinformatics Metrics

Description

This analysis calculates a global metric score based upon a prediction model computed with flexmix package.

Usage

globalMetric(data, k.range = c(2, 15), nrep = 10,
  criterion = c("BIC", "AIC"), PCA = FALSE, seed = NULL)

Arguments

data	A SummarizedExperiment. The SummarizedExperiment must contain an assay with the following structure: A valid header with names. The first column of the header is the ID or name of the instance of the dataset (e.g., ontology, pathway, etc.) on which the metrics are measured. The other columns of the header contains the names of the metrics. The rows contains the measurements of the metrics for each instance in the dataset.
k.range	Concatenation of two positive integers. The first value k.range[1] is considered as the lower bound of the range, whilst the second one, k.range[2], as the higher. Both values must be contained in [2,15] range.
nrep	Positive integer. Number of random initializations used in adjusting the model.
criterion	String. Critirion applied in order to select the best model. Possible values: "BIC" or "AIC".
PCA	Boolean. If true, a PCA is performed on the input dataframe before computing the predictions.
seed	Positive integer. A seed for internal bootstrap.

Value

A dataframe containing the global metric score for each metric.

Examples

# Using example data from our package
data("rnaMetrics")
globalMetric(rnaMetrics, k.range = c(2,3), nrep=10, criterion="AIC", PCA=TRUE)

evaluomeR documentation built on March 15, 2021, 6 p.m.