The S3 methods autoplot.fanova() and plot.fanova() are methods
for plotting results of functional analysis of variance tests. They visualize the
functional data and the adjusted p-values obtained from the testing
procedures for mean comparison of multiple groups. The plots highlight significant
effects at two levels of significance, alpha1 and alpha2, using shaded
areas.
Usage
# S3 method for class 'fanova'
autoplot(
object,
xrange = c(0, 1),
alpha1 = 0.05,
alpha2 = 0.01,
plot_adjpval = FALSE,
ylim = NULL,
col = 1,
ylabel = "Functional Data",
title = NULL,
linewidth = 0.5,
type = "l",
...
)
# S3 method for class 'fanova'
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",
...
)Arguments
- object, x
An object of class
fanova, usually a result of a call tofunctional_anova_test(),iwt_aov(),twt_aov()orglobal_aov().- xrange
A length-2 numeric vector specifying the range of the x-axis for the plots. Defaults to
c(0, 1). This should match the domain of the functional data.- alpha1
A numeric value specifying the first level of significance used to select and display significant effects. Defaults to
alpha1 = 0.05.- alpha2
A numeric value specifying the second level of significance used to select and display significant effects. Defaults to
alpha2 = 0.01.- plot_adjpval
A boolean value specifying whether the plots of adjusted p-values should be displayed. Defaults to
FALSE.- ylim
A 2-length numeric vector specifying the range of the y-axis. Defaults to
NULL, which determines automatically the range from functional data.- col
An integer specifying the color for the plot of functional data. Defaults to
1L.- ylabel, ylab
A string specifying the label of the y-axis of the functional data plot. Defaults to
"Functional Data".- title, main
A string specifying the title of the functional data plot. Defaults to
NULLin which case no title is displayed.- linewidth, lwd
A numeric value specifying the width of the line for the functional data plot. Note that the line width for the adjusted p-value plot will be twice this value. Defaults to
0.5.- type
A string specifying the type of plot for the functional data. Defaults to
"l"for lines.- ...
Other arguments passed to specific methods. Not used in this function.
Value
The autoplot.fanova() function creates a ggplot object that
displays the functional data and the adjusted p-values. The significant
intervals at levels alpha1 and alpha2 are highlighted in the plots.
The plot.fanova() function is a wrapper around autoplot.fanova()
that prints the plot directly.
References
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.
See also
IWTimage() for the plot of p-values heatmaps (for IWT).
Examples
temperature <- rbind(NASAtemp$milan, NASAtemp$paris)
groups <- c(rep(0, 22), rep(1, 22))
# Performing the TWT
TWT_result <- functional_anova_test(
temperature ~ groups,
correction = "TWT",
B = 5L
)
#>
#> ── Point-wise tests ────────────────────────────────────────────────────────────
#>
#> ── Threshold-wise tests ────────────────────────────────────────────────────────
#>
#> ── Threshold-Wise Testing completed ────────────────────────────────────────────
# Plotting the results of the TWT
plot(
TWT_result,
xrange = c(0, 12),
main = 'TWT results for testing mean differences'
)
