IWTimage | R Documentation |
Plotting function creating a graphical output of the IWT: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.
IWTimage(
IWT_result,
alpha = 0.05,
abscissa_range = c(0, 1),
nlevel = 20,
plot_unadjusted = FALSE
)
IWT_result |
Results of the ITP, as created by |
alpha |
Threshold for the interval-wise error rate used for the hypothesis test. The default is |
abscissa_range |
Range of the plot abscissa. The default is |
nlevel |
Number of desired color levels for the p-value heatmap. The default is |
plot_unadjusted |
Flag indicating if the unadjusted p-value function has to be added to the plots. The default is |
No value returned.
Pini, A., & Vantini, S. (2018). 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.
See plot.IWT1
, plot.IWT2
, plot.IWTlm
, and plot.IWTaov
for the plot method applied to the IWT results of one- and two-population tests, linear models, and ANOVA, respectively.
# Importing the NASA temperatures data set
data(NASAtemp)
# Performing the IWT for two populations
IWT.result <- IWT2(NASAtemp$milan,NASAtemp$paris)
# Plotting the results of the IWT
IWTimage(IWT.result,abscissa_range=c(0,12))
# Selecting the significant components for the radius at 5\% level
which(IWT.result$corrected.pval < 0.05)
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