plot.IWT1 | R Documentation |
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.
## 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",
...
)
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 |
ylab |
Label of |
main |
Plot title. |
lwd |
Line width for the plot of the adjusted p-value function. Default
is |
col |
Colors for the plot of functional data. Default is |
ylim |
Range of the |
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
|
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).
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.
IWTimage
for the plot of p-values heatmaps. See also
IWT2
to perform the ITP to test differences between two
populations. See ITP1bspline
for one-population test based on
B-spline basis representation.
# Importing the NASA temperatures data set
data(NASAtemp)
# Performing the IWT for one population
IWT.result <- IWT1(NASAtemp$paris, mu = 4, B = 10L)
# Plotting the results of the IWT
plot(IWT.result, xrange = c(0, 12), main = 'Paris temperatures')
# Plotting the p-value heatmap
IWTimage(IWT.result, abscissa_range = c(0, 12))
# Selecting the significant components at 5% level
which(IWT.result$adjusted_pval < 0.05)
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