# Plotting ITP results for two-population tests

### 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 |

### Arguments

`x` |
The object to be plotted.
An object of class " |

`xrange` |
Range of the |

`alpha1` |
First level of significance used to select and display significant differences. Default is |

`alpha2` |
Second level of significance used to select and display significant differences. Default is |

`ylab` |
Label of |

`main` |
An overall title for the plots (it will be pasted to " |

`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 |

`...` |
Additional plotting arguments that can be used with function |

### 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)
``` |