# plot.ITP2: Plotting ITP results for two-population tests In fdatest: Interval Testing Procedure for Functional Data

## Description

`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.

## Usage

 ```1 2 3 4``` ```## 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), ...) ```

## Arguments

 `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. `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`).

## 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 basis components selected as significant by the test at level `alpha1` and `alpha2` are highlighted in the plot of the corrected 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.

## Author(s)

Alessia Pini, Simone Vantini

## References

A. Pini and S. Vantini (2013). The Interval Testing Procedure: Inference for Functional Data Controlling the Family Wise Error Rate on Intervals. MOX-report 13/2013, Politecnico di Milano.

## See Also

`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.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```# 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\$corrected.pval < 0.05) ```

fdatest documentation built on May 29, 2017, 1:40 p.m.