plot.IWT1: Plot method for IWT results on one-population test
In alessiapini/fdatest: Interval Wise Testing for Functional Data

plot.IWT1

R Documentation

Plot method for IWT results on one-population test

Description

plot method for class "IWT1". Plotting function creating a graphical output of the IWT for the test of the mean of one population: functional data and IWT-adjusted p-values are plotted.

Usage

## S3 method for class 'IWT1'
plot(
  x,
  xrange = c(0, 1),
  alpha1 = 0.05,
  alpha2 = 0.01,
  ylab = "Functional Data",
  main = NULL,
  lwd = 1,
  col = 1,
  ylim = NULL,
  type = "l",
  ...
)

Arguments

`x`	The object to be plotted. An object of class "`IWT1`", usually, a result of a call to `IWT1`.
`xrange`	Range of the `x` axis.
`alpha1`	First level of significance used to select and display significant effects. Default is `alpha1 = 0.05`.
`alpha2`	Second level of significance used to select and display significant effects. 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`	Plot title.
`lwd`	Line width for the plot of the adjusted p-value function. Default is `lwd=1`.
`col`	Colors for the plot of functional data. Default is `col = 1`.
`ylim`	Range of the `y` axis. Default is `NULL`, giving a plot with automatic range for functional data.
`type`	line type for the plot of the adjusted p-value function. Default is type='l'.
`...`	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 IWT 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).

References

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.

Examples

# Importing the NASA temperatures data set
data(NASAtemp)

# Performing the IWT for one population
IWT.result <- IWT1(NASAtemp$paris,mu=4)

# Plotting the results of the IWT
plot(IWT.result,xrange=c(0,12),main='Paris temperatures')

# Plotting the p-value heatmap
IWTimage(IWT.result,abscissa_range=c(0,12))

# Selecting the significant components at 5% level
which(IWT.result$adjusted_pval < 0.05)

alessiapini/fdatest documentation built on April 17, 2024, 8:39 p.m.