plot.fdatest2: Plot method for fdatest results on two-population test

In alessiapini/fdatest: Interval Wise Testing for Functional Data

Plot method for fdatest results on two-population test

Description

plot method for class "fdatest2". Plotting function creating a graphical output of the testing procedures for the mean comparison of two groups: functional data and adjusted p-values are plotted.

Usage

## S3 method for class 'fdatest2'
plot(
  x,
  xrange = c(0, 1),
  alpha1 = 0.05,
  alpha2 = 0.01,
  ylab = "Functional Data",
  main = NULL,
  lwd = 0.5,
  col = c(1, 2),
  ylim = NULL,
  type = "l",
  ...
)

Arguments

x	The object to be plotted. An object of class "fdatest2", usually, a result of a call to fdatest2, IWT2, TWT2, FDR2, or PCT2.
xrange	Range of the x axis.
alpha1	First level of significance used to select and display significant effects. Default is alpha1 = 0.05.
alpha2	Second level of significance used to select and display significant effects. Default is alpha1 = 0.01. alpha1 and alpha2 are s.t. alpha2 < alpha1. Otherwise the two values are switched.
ylab	Label of y axis of the plot of functional data. Default is "Functional Data".
main	Plot title.
lwd	Line width for the plot of the adjusted p-value function. Default is lwd=1.
col	Colors for the plot of functional data. Default is col = 1.
ylim	Range of the y axis. Default is NULL, giving a plot with automatic range for functional data.
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 plot, such as graphical parameters (see par).

Value

No value returned. The function produces a graphical output of the fdatest 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).

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

# Importing the NASA temperatures data set
data(NASAtemp)

# Performing the TWT for two populations
TWT.result <- fdatest2(
  NASAtemp$paris, NASAtemp$milan, 
  method = "TWT", B = 10L
)

# Plotting the results of the TWT
plot(
  TWT.result, 
  xrange = c(0, 12), 
  main = 'TWT results for testing mean differences'
)

# Selecting the significant components at 5% level
which(TWT.result$adjusted_pval < 0.05)

# Performing the IWT for two populations
IWT.result <- fdatest2(
  NASAtemp$paris, NASAtemp$milan, 
  method = "IWT", B = 10L
)

# Plotting the results of the IWT
plot(
  IWT.result, 
  xrange = c(0, 12), 
  main = 'IWT results for testing mean differences'
)

# Selecting the significant components at 5% level
which(IWT.result$adjusted_pval < 0.05)

alessiapini/fdatest documentation built on Jan. 4, 2025, 5:37 a.m.