summary.IWTaov: Summarizing Functional Analysis of Variance Fits

In alessiapini/fdatest: Interval Wise Testing for Functional Data

Summarizing Functional Analysis of Variance Fits

Description

summary method for class "IWTaov". Function returning a summary of the results of IWT for the test on a functional analysis of variance: minimum IWT-adjusted p-values of the F-tests on the whole model and on each factor are reported.

Usage

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

Arguments

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

Value

No value returned. The function summary.IWTaov 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:

• factors: A L x 1 matrix with columns for the factors of ANOVA, and corresponding (two-sided) IWT-adjusted minimum p-values of the corresponding tests of significance (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.IWTaov for the plot of analysis of variance 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 ITPaovbspline for functional ANOVA 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 <- IWTaov(temperature ~ groups, B = 10L)

# 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 Jan. 4, 2025, 5:37 a.m.