marginal.cor: Calculate marginal correlations with response

In funreg: Functional Regression for Irregularly Timed Data

Description

Calculates marginal correlations between a functional covariate and a scalar response.

Usage

marginal.cor(object, id = NULL, response = NULL, alpha = 0.05)

Arguments

object	An object of type funeigen or funreg. One or the other of these is needed in order to provide a smoothed reconstructed curves for the functional covariate for each subject.
id	The vector of subject id's. These tell which responses in response correspond to which curves in object.
response	The vector of responses
alpha	The alpha level for confidence intervals (one minus the two-sided coverage)

Value

Returns a list with one component for each functional covariate. Each such component contains the between-subjects correlations between the fitted smoothed latent values of the functional covariate, and the response variable. We call this a marginal correlation because it simply ignores the other functional covariates (rather than trying to adjust or control for them). Both the functional regression coefficient and the marginal correlation can be useful, although they have different substantive interpretations.

funreg documentation built on Oct. 4, 2021, 5:07 p.m.