plot.IWTaov | R Documentation |
plot
method for class "IWTaov
". Plotting function creating a
graphical output of the IWT for the test on a functional analysis of
variance: functional data, and IWT-adjusted p-values of the F-tests on the
whole model and on each factor are plotted.
## S3 method for class 'IWTaov'
plot(
x,
xrange = c(0, 1),
alpha1 = 0.05,
alpha2 = 0.01,
plot_adjpval = FALSE,
ylim = NULL,
col = 1,
ylab = "Functional Data",
main = NULL,
lwd = 0.5,
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 |
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 the adjusted p-value function. Default
is |
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). 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.
IWTimage
for the plot of p-values heatmaps. See also
IWT1
, IWT2
to perform the ITP to test on the
mean of one population and test of differences between two populations. See
ITPaovbspline
for functional ANOVA based on B-spline basis
representation
temperature <- rbind(NASAtemp$milan, NASAtemp$paris)
groups <- c(rep(0, 22), rep(1, 22))
# Performing the IWT
IWT.result <- IWTaov(temperature ~ groups, B = 5L)
# Summary of the IWT results
summary(IWT.result)
# Plot of the IWT results
graphics::layout(1)
plot(IWT.result)
# All graphics on the same device
graphics::layout(matrix(1:4, nrow = 2, byrow = FALSE))
plot(
IWT.result,
main = 'NASA data',
plot_adjpval = TRUE,
xlab = 'Day',
xrange = c(1, 365)
)
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