error_modelSel: Compute error in model selection with Approximate Bayesian...

In poolABC: Approximate Bayesian Computation with Pooled Sequencing Data

Compute error in model selection with Approximate Bayesian Computation

Description

This function calculates the confusion matrix and the mean misclassification probabilities of models from the output of the sim_modelSel() function.

Usage

error_modelSel(object, threshold = NA, print = TRUE)

Arguments

object	a list created by the sim_modelSel() function, containing results of a simulation study to evaluate the quality of model selection with Approximate Bayesian Computation.
threshold	numeric value between 0 and 1 representing the minimum posterior probability of assignment.
print	logical, if TRUE (default), then this function prints the mean models probabilities.

Details

It is also possible to define a threshold for the posterior model probabilities. This threshold sets the minimum posterior probability of assignment. Thus, a simulation where the posterior probability of any model is below the threshold will not be assigned to a model and will instead be classified as "unclear".

Value

apart from directly displaying the results if print is TRUE, the output object of this function is a list with the following elements:

confusion.matrix	the confusion matrix.
probs	the mean model misclassification probabilities.
postmeans	the mean model misclassification probabilities when each model is correctly or incorrectly estimated.

Examples

# load the matrix with simulated parameter values
data(sumstats)

# select a random simulation to act as target just to test the function
target <- sumstats[10 ,]

# create a "fake" vector of model indices
# this assumes that half the simulations were from one model and the other half from other model
# this is not true but serves as an example of how to use this function
index <- c(rep("model1", nrow(sumstats)/2), rep("model2", nrow(sumstats)/2))

# perform a leave-one-out cross validation of model selection
mysim <- sim_modelSel(index = index, sumstats = sumstats, nval = 10, tol = 0.1)

# compute the confusion matrix and the mean misclassification probabilities
error_modelSel(object = mysim)

poolABC documentation built on Aug. 8, 2023, 5:14 p.m.