plot.ITP2: Plot method for ITP results on two-population tests
In alessiapini/fdatest: Interval Wise Testing for Functional Data

Description Usage Arguments Value References See Also Examples

plot method for class "ITP2". Plotting function creating a graphical output of the ITP for the test of comparison between two populations: functional data and ITP-adjusted p-values are plotted.

## S3 method for class 'ITP2'
plot(x, xrange = c(0, 1), alpha1 = 0.05, alpha2 = 0.01,
  ylab = "Functional Data", main = NULL, lwd = 1, col = c(1, 2),
  pch = 16, ylim = range(object$data.eval), ...)

`x`	The object to be plotted. An object of class "`ITP2`", that is, a result of an ITP for comparison between two populations. Usually a call to `ITP2bspline`, `ITP2fourier` or `ITP2pafourier`.
`xrange`	Range of the `x` axis. Default is `xrange=c(0,1)`.
`alpha1`	First level of significance used to select and display significant differences. Default is `alpha1 = 0.05`.
`alpha2`	Second level of significance used to select and display significant differences. Default is `alpha1 = 0.01`. `alpha1` and `alpha2` are s.t. `alpha2 < alpha1`. Otherwise the two values are switched.
`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`" for the first plot and "`adjusted p-values`" for the second plot).
`lwd`	Line width for the plot of functional data.
`col`	Color used to plot the functional data.
`pch`	Point character for the plot of adjusted p-values.
`ylim`	Range of the `y` axis.
`...`	Additional plotting arguments that can be used with function `plot`, such as `graphical parameters` (see `par`).

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 basis components selected as significant by the test at level alpha1 and alpha2 are highlighted in the plot of the adjusted p-values and in the one of functional data (in case the test is based on a local basis, such as B-splines) by gray areas (light and dark gray, respectively). In the case of a Fourier basis with amplitude and phase decomposition, two plots of adjusted p-values are done, one for phase and one for amplitude.

#' A. Pini and S. Vantini (2017). The Interval Testing Procedure: Inference for Functional Data Controlling the Family Wise Error Rate on Intervals. Biometrics 73(3): 835–845.

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.

ITPimage for the plot of p-values heatmaps. See also ITP2bspline, ITP2fourier, ITP2pafourier to perform the ITP to test for differences between two populations. See plot.ITP1 and plot.ITPlm for the plot method applied to the ITP results of one-population tests and a linear models, respectively.

# Importing the NASA temperatures data set
data(NASAtemp)

# Performing the ITP for two populations with the B-spline basis
ITP.result.bspline <- ITP2bspline(NASAtemp$milan,NASAtemp$paris,nknots=30,B=1000)
# Plotting the results of the ITP
plot(ITP.result.bspline,xlab='Day',xrange=c(1,365),main='NASA data')
# Selecting the significant components for the radius at 5% level
which(ITP.result.bspline$adjusted.pval < 0.05)