plot.IWTlm | R Documentation |
plot
method for class "IWTlm
".
Plotting function creating a graphical output of the IWT for the test on a functional on scalar linear model:
functional data, and IWT-adjusted p-values of the F-tests on the whole model and of t-tests on all covariates' effects.
## S3 method for class 'IWTlm'
plot(
x,
xrange = c(0, 1),
alpha1 = 0.05,
alpha2 = 0.01,
plot_adjpval = FALSE,
col = c(1, rainbow(dim(x$adjusted_pval_part)[1])),
ylim = NULL,
ylab = "Functional Data",
main = NULL,
lwd = 1,
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 |
col |
Colors for the plot of functional data. Default is |
ylim |
Range of the |
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 covariates 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 t-tests on each covariate's effect.
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 ITPlmbspline
for functional on scalar linear model based on B-spline basis representation
# Importing the NASA temperatures data set
data(NASAtemp)
temperature <- rbind(NASAtemp$milan,NASAtemp$paris)
groups <- c(rep(0,22),rep(1,22))
# Performing the IWT
IWT.result <- IWTlm(temperature ~ groups,B=1000)
# Summary of the IWT results
summary(IWT.result)
# Plot of the IWT results
layout(1)
plot(IWT.result)
# All graphics on the same device
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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