plot.ITPaov: Plot method for ITP results on functional ANOVA

In alessiapini/fdatest: Interval Wise Testing for Functional Data

Plot method for ITP results on functional ANOVA

Description

plot method for class "ITPaov". Plotting function creating a graphical output of the ITP for the test on a functional analysis of variance: functional data, and ITP-adjusted p-values of the F-tests on the whole model and on each factor are plotted.

Usage

## S3 method for class 'ITPaov'
plot(
  x,
  xrange = c(0, 1),
  alpha1 = 0.05,
  alpha2 = 0.01,
  plot.adjpval = FALSE,
  ylim = range(x$data.eval),
  col = 1,
  ylab = "Functional Data",
  main = NULL,
  lwd = 1,
  pch = 16,
  ...
)

Arguments

x	The object to be plotted. An object of class "ITPaov", usually, a result of a call to ITPaovbspline.
xrange	Range of the x axis.
alpha1	First level of significance used to select and display significant effects. Default is alpha1 = 0.05.
alpha2	Second level of significance used to select and display significant effects. Default is alpha1 = 0.01. alpha1 and alpha2 are s.t. alpha2 < alpha1. Otherwise the two values are switched.
plot.adjpval	A logical indicating wether the plots of adjusted p-values have to be done. Default is plot_adjpval = FALSE.
ylim	Range of the y axis. Default is NULL, giving a plot with authomatic range for functional data.
col	Colors for the plot of functional data. Default is col = 1.
ylab	Label of y axis of the plot of functional data. Default is "Functional Data".
main	An overall title for the plots (it will be pasted to "Functional Data and F-test" for the first plot and to factor names for the other plots).
lwd	Line width for the plot of functional data. Default is lwd=1.
pch	Point character for the plot of adjusted p-values. Default is pch=16.
...	Additional plotting arguments that can be used with function plot, such as graphical parameters (see par).

Value

No value returned. The function produces a graphical output of the ITP results: the plot of the functional data and the one of the adjusted p-values. The portions of the domain selected as significant by the test at level alpha1 and alpha2 are highlighted in the plot of the adjusted p-value function and in the one of functional data by gray areas (light and dark gray, respectively). The first plot reports the gray areas corresponding to a significant F-test on the whole model. The remaining plots report the gray areas corresponding to significant F-tests on each factor (with colors corresponding to the levels of the factor).

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

ITPimage for the plot of p-values heatmaps. See also ITP1bspline, ITP2bspline to perform the ITP to test on the mean of one population and test of differences between two populations. See IWTaov for functional ANOVA not 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 ITP
ITP.result <- ITPaovbspline(temperature ~ groups,B=1000,nknots=20,order=3)

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

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

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

alessiapini/fdatest documentation built on April 23, 2024, 2:31 a.m.