# Plotting ITP results for functional analysis of variance testing

### 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

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 effects. Default is |

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

`plot.adjpval` |
A logical indicating wether the plots of adjusted p-values have to be done. Default is |

`col` |
Colors for the plot of functional data. Default is |

`ylim` |
Range of the |

`ylab` |
Label of |

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

`lwd` |
Line width for the plot of functional data. Default is |

`pch` |
Point character for the plot of adjusted p-values. Default is |

`...` |
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, functional regression coefficients, and ITP-adjusted p-values of the F-tests on the whole model and on each factor.
The basis components selected as significant by the tests at level `alpha1`

and `alpha2`

are highlighted in the plot of the corrected p-values 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).

### 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.

K. Abramowicz, S. De Luna, C. Häger, A. Pini, L. Schelin, and S. Vantini (2015). Distribution-Free Interval-Wise Inference for Functional-on-Scalar Linear Models. *MOX-report 3/2015*, Politecnico di Milano.

### See Also

See also `ITPaovbspline`

to fit and test a functional analysis of variance applying the ITP, and `summary.ITPaov`

for summaries.
See `plot.ITPlm`

, `plot.ITP1`

, and `plot.ITP2`

for the plot method applied to the ITP results of functional-on-scalar linear models, one-population and two-population, respectively.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# 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))
``` |