Description Usage Arguments Value References See Also Examples

`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.

1 2 3 4 |

`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 |

`ylim` |
Range of the |

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

`ylab` |
Label of |

`main` |
An overall title for the plots (it will be pasted to "Functional Data and F-test" for the first plot and to factor names for the other plots). |

`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 |

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 portions of the domain selected as significant by the test at level `alpha1`

and `alpha2`

are highlighted in the plot of the adjusted p-value function 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).

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 `ITP1bspline`

, `ITP2bspline`

to perform the ITP to test on the mean of one population and test of differences between two populations.
See `IWTaov`

for functional ANOVA not based on B-spline basis representation

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))
``` |

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