plot_msel {poolABC}

Plot model misclassification

Description

Displays a barplot of the confusion matrix obtained with a leave-one-out cross validation for model selection.

Usage

plot_msel(object, color = TRUE)

Arguments

object

a list created by the error_modelSel() function, containing the results of a leave-one-out cross validation for model selection.

color

logical, if TRUE (default) then a colour version of the barplot will be produced, if FALSE then a grey scale version will be produced.

Details

The barplot shows the proportion of validation simulations classified to each of the models. This function can produce either a colour or a grey scale barplot. If the classification of models is perfect, meaning that the model probability of each model is one for the correct model, then each bar will have a single colour representing its corresponding model.

Value

a barplot of the proportion of simulations classified to any of the models. In other words, a barplot of the confusion matrix.

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
myerror <- error_modelSel(object = mysim, print = FALSE)

# barplot of model misclassification
plot_msel(object = myerror)