summary.IWTlm: Summarizing Functional Linear Model Fits

In alessiapini/fdatest: Interval Wise Testing for Functional Data

Summarizing Functional Linear Model Fits

Description

summary method for class "IWTlm". Function returning a summary of the results of IWT for the test on a functional linear model: minimum IWT-adjusted p-values of the F-tests on the whole model and of t-tests on all covariates' effects are reported.

Usage

## S3 method for class 'IWTlm'
summary(object, ...)

Arguments

object	An object of class "IWTlm", usually, a result of a call to IWTlm.
...	Further arguments passed to or from other methods..

Value

No value returned. The function summary.IWTlm computes and returns a list of summary statistics of the fitted functional analysis of variance given in object, using the component "call" from its arguments, plus:

ttest	A L+1 x 1 matrix with columns for the functional regression coefficients, and corresponding (two-sided) IWT-adjusted minimum p-values of t-tests (i.e., the minimum p-value over all p basis components used to describe functional data).
R2	Range of the functional R-squared.
ftest	IWT-adjusted minimum p-value of functional F-test.

References

Pini, A., & Vantini, S. (2017). Interval-wise testing for functional data. Journal of Nonparametric Statistics, 29(2), 407-424

Pini, A., Vantini, S., Colosimo, B. M., & Grasso, M. (2018). Domain‐selective functional analysis of variance for supervised statistical profile monitoring of signal data. Journal of the Royal Statistical Society: Series C (Applied Statistics) 67(1), 55-81.

Abramowicz, K., Hager, C. K., Pini, A., Schelin, L., Sjostedt de Luna, S., & Vantini, S. (2018). Nonparametric inference for functional‐on‐scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament. Scandinavian Journal of Statistics 45(4), 1036-1061.

See Also

IWTimage for the plot of p-values heatmaps. plot.IWTlm for the plot of regression results. See also IWT1, IWT2 to perform the ITP to test on the mean of one population and test of differences between two populations. See ITPlmbspline for functional linear model based on B-spline basis representation

Examples

# Importing the NASA temperatures data set
data(NASAtemp)

temperature <- rbind(NASAtemp$milan,NASAtemp$paris)
groups <- c(rep(0,22),rep(1,22))

# Performing the IWT
IWT.result <- IWTlm(temperature ~ groups,B=1000)

# Summary of the IWT results
summary(IWT.result)

# Plot of the IWT results
layout(1)
plot(IWT.result)

# All graphics on the same device
layout(matrix(1:4,nrow=2,byrow=FALSE))
plot(IWT.result,main='NASA data', plot_adjpval = TRUE,xlab='Day',xrange=c(1,365))

alessiapini/fdatest documentation built on April 28, 2024, 12:35 a.m.