plot.ITP1: Plotting ITP results for one-population tests
In alessiapini/fdatest: Interval Wise Testing for Functional Data

Description Usage Arguments Value Author(s) References See Also Examples

plot method for class "ITP1". Plotting function creating a graphical output of the ITP for the test of the mean of one population: functional data and ITP-adjusted p-values are plotted.

## S3 method for class 'ITP1'
plot(x, xrange = c(0, 1), alpha1 = 0.05, alpha2 = 0.01, 
     ylab = "Functional Data", main = NULL, lwd = 1, col = 1, 
     pch = 16, ylim = range(object$data.eval), ...)

`x`	The object to be plotted. An object of class "`ITP1`", that is, a result of an ITP for comparison between two populations. Usually a call to `ITP1bspline` or `ITP1fourier`.
`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`).

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).

Alessia Pini, Simone Vantini

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.

ITPimage for the plot of p-values heatmaps.

See also ITP1bspline and ITP1fourier to perform the ITP to test for the mean of a functional populations. See plot.ITP2 and plot.ITPlm for the plot method applied to the ITP results of two-population tests and linear models, respectively.

# Importing the NASA temperatures data set
data(NASAtemp)

# Performing the ITP for one population with the B-spline basis
ITP.result.bspline <- ITP1bspline(NASAtemp$paris,mu=4,nknots=50,B=1000)
# Plotting the results of the ITP
plot(ITP.result.bspline,xlab='Day',xrange=c(0,365),main='NASA data')
# Selecting the significant components for the radius at 5% level
which(ITP.result.bspline$corrected.pval < 0.05)