biosensors.usc (version 1.0)

hypothesis_testing: hypothesis_testing

Description

Hypothesis testing between two random samples of distributional representations to detect differences in scale and localization (ANOVA test) or distributional differences (Energy distance).

Usage

hypothesis_testing(data1, data2, permutations=100)

Arguments

data1

A biosensor object. First population.

data2

A biosensor object. Second population.

permutations

Number of permutations used in the energy distance calibration test.

Value

An object of class biotest: p1_mean Quantile mean of the first population. p1_variance Quantile variance of the first population. p2_mean Quantile mean of the second population. p2_variance Quantile variance of the second population. energy_pvalue P-value of the energy distance test. anova_pvalue P-value of the ANOvA-Fr<U+00E9>chet test.

Examples

# NOT RUN {
# Data extracted from the paper: Hall, H., Perelman, D., Breschi, A., Limcaoco, P., Kellogg, R.,
# McLaughlin, T., Snyder, M., Glucotypes reveal new patterns of glucose dysregulation, PLoS
# biology 16(7), 2018.
file1 = system.file("extdata", "data_1.csv", package = "biosensors.usc")
file2 = system.file("extdata", "variables_1.csv", package = "biosensors.usc")
data1 = load_data(file1, file2)
file3 = system.file("extdata", "data_2.csv", package = "biosensors.usc")
file4 = system.file("extdata", "variables_2.csv", package = "biosensors.usc")
data2 = load_data(file3, file4)
htest = hypothesis_testing(data1, data2)
# }