interpretation_plot: interpretation_plot

In bliss: Bayesian Functional Linear Regression with Sparse Step Functions

interpretation_plot

Description

Provide a graphical representation of the functional data with a focus on the detected periods with the Bliss method.

Usage

interpretation_plot(data, Bliss_estimate, q = 1, centered = FALSE, cols = NULL)

Arguments

data	a list containing: y a numerical vector, the outcomes. x a list of matrices, the qth matrix contains the observations of the qth functional covariate at time points given by grids. grids a list of numerical vectors, the qth vector is the grid of time points for the qth functional covariate.
Bliss_estimate	a numerical vector, the Bliss estimate.
q	an integer (optional), the index of the functional covariate to plot.
centered	a logical value (optional), If TRUE, the functional data are centered.
cols	a numerical vector of colours (optional).

Examples

data(data1)
data(param1)
# result of res_bliss1 <- fit_Bliss(data=data1,param=param1,verbose=TRUE)
data(res_bliss1)
interpretation_plot(data=data1,Bliss_estimate=res_bliss1$Bliss_estimate,q=1)
interpretation_plot(data=data1,Bliss_estimate=res_bliss1$Bliss_estimate,q=1,centered=TRUE)

bliss documentation built on May 29, 2024, 8:35 a.m.