nadayara_regression: nadayara_regression

In glucodensities/biosensors.usc: Distributional Data Analysis Techniques for Biosensor Data

nadayara_regression

Description

Functional non-parametric Nadaraya-Watson regression with 2-Wasserstein distance, using as predictor the distributional representation and as response a scalar outcome.

Usage

nadayara_regression(data, response)

Arguments

data	A biosensor object.
response	The name of the scalar response. The response must be a column name in data$variables.

Value

An object of class bnadaraya: prediction The Nadaraya-Watson prediction for each point of the training data at each h=seq(0.8, 15, length=200). r2 R2 estimation for the training data at each h=seq(0.8, 15, length=200). error Standard mean-squared error after applying leave-one-out cross-validation for the training data at each h=seq(0.8, 15, length=200). data A data frame with biosensor raw data. response The name of the scalar response.

Examples

# 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")
data = load_data(file1, file2)
nada = nadayara_regression(data, "BMI")

glucodensities/biosensors.usc documentation built on May 12, 2022, 1:57 a.m.