plot.ITP2 | R Documentation |
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.
## S3 method for class 'ITP2'
plot(
x,
xrange = c(0, 1),
alpha1 = 0.05,
alpha2 = 0.01,
ylab = "Functional Data",
main = NULL,
lwd = 1,
col = c(1, 2),
pch = 16,
ylim = range(object$data.eval),
...
)
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 |
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 adjusted 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.
#' A. Pini and S. Vantini (2017). The Interval Testing Procedure: Inference for Functional Data Controlling the Family Wise Error Rate on Intervals. Biometrics 73(3): 835–845.
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 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.
# 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$paris,nknots=30,B=1000)
# Plotting the results of the ITP
plot(ITP.result.bspline,xlab='Day',xrange=c(1,365),main='NASA data')
# Selecting the significant components for the radius at 5% level
which(ITP.result.bspline$adjusted.pval < 0.05)
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