create_sim_samples: Create random samples for simulations

In guillermozbta/precrec: Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves

Description

The create_sim_samples function generates random samples with different performance levels.

Usage

create_sim_samples(n_repeat, np, nn, score_names = "random")

Arguments

n_repeat	The number of iterations to make samples.
np	The number of positives in a sample.
nn	The number of negatives in a sample.
score_names	A character vector for the names of the following performance levels. "random" Random "poor_er" Poor early retrieval "good_er" Good early retrieval "excel" Excellent "perf" Perfect "all" All of the above

Value

The create_sim_samples function returns a list with the following items.

• scores: a list of numeric vectors

• labels: an integer vector

• modnames: a character vector of the model names

• dsids: a character vector of the dataset IDs

See Also

mmdata for formatting input data. evalmod for calculation evaluation measures.

Examples

##################################################
### Create a set of samples with 10 positives and 10 negatives
### for the random performance level
###
samps1 <- create_sim_samples(1, 10, 10, "random")

## Show the list structure
str(samps1)


##################################################
### Create two sets of samples with 10 positives and 20 negatives
### for the random and the poor early retrieval performance levels
###
samps2 <- create_sim_samples(2, 10, 20, c("random", "poor_er"))

## Show the list structure
str(samps2)


##################################################
### Create 3 sets of samples with 5 positives and 5 negatives
### for all 5 levels
###
samps3 <- create_sim_samples(3, 5, 5, "all")

## Show the list structure
str(samps3)

guillermozbta/precrec documentation built on May 11, 2019, 7:22 p.m.