fdatest (version 2.1.1)

plot.ITPlm: Plotting ITP results for functional-on-scalar linear model testing

Description

plot method for class "ITPlm". Plotting function creating a graphical output of the ITP for the test on a functional-on-scalar linear model: functional data, functional coefficients and ITP-adjusted p-values for the F-test and t-tests are plotted.

Usage

# S3 method for ITPlm
plot(x, xrange = c(0, 1), alpha1 = 0.05, alpha2 = 0.01, 
     plot.adjpval = FALSE, col = c(1, rainbow(dim(x$corrected.pval.t)[1])), 
     ylim = range(x$data.eval), ylab = "Functional Data", 
     main = NULL, lwd = 1, pch = 16, ...)

Arguments

x

The object to be plotted. An object of class "ITPlm", usually, a result of a call to ITPlmbspline.

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.

plot.adjpval

A logical indicating wether the plots of adjusted p-values have to be done. Default is plot.adjpval = FALSE.

col

Vector of colors for the plot of functional data (first element), and functional coefficients (following elements).

Default is col = c(1, rainbow(dim(x$corrected.pval.t)[1])).

ylim

Range of the y axis. Default is ylim = range(x$data.eval).

ylab

Label of y axis of the plot of functional data. Default is "Functional Data".

main

An overall title for the plots (it will be pasted to "Functional Data and F-test" for the first plot and "t-test" for the other plots).

lwd

Line width for the plot of functional data. Default is lwd=16.

pch

Point character for the plot of adjusted p-values. Default is pch=16.

…

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 ITP results: the plot of the functional data, functional regression coefficients, and ITP-adjusted p-values for the F-test and t-tests. The basis components selected as significant by the tests at level alpha1 and alpha2 are highlighted in the plot of the corrected p-values and in the one of functional data by gray areas (light and dark gray, respectively). The plot of functional data reports the gray areas corresponding to a significant F-test. The plots of functional regression coefficients report the gray areas corresponding to significant t-tests for the corresponding covariate.

References

A. Pini and S. Vantini (2013). The Interval Testing Procedure: Inference for Functional Data Controlling the Family Wise Error Rate on Intervals. MOX-report 13/2013, Politecnico di Milano.

K. Abramowicz, S. De Luna, C. H<U+00E4>ger, A. Pini, L. Schelin, and S. Vantini (2015). Distribution-Free Interval-Wise Inference for Functional-on-Scalar Linear Models. MOX-report 3/2015, Politecnico di Milano.

See Also

See also ITPlmbspline to fit and test a functional-on-scalar linear model applying the ITP, and summary.ITPlm for summaries. See plot.ITPaov, plot.ITP1, and plot.ITP2 for the plot method applied to the ITP results of functional analysis of variance, one-population and two-population, respectively.

Examples

# NOT RUN {
# Importing the NASA temperatures data set
data(NASAtemp)

data <- rbind(NASAtemp$milan,NASAtemp$paris)
lab <- c(rep(0,22),rep(1,22))

# Performing the ITP
ITP.result <- ITPlmbspline(data ~ lab,B=1000,nknots=20,order=3)
# Summary of the ITP results
summary(ITP.result)

# Plot of the ITP results
layout(1)
plot(ITP.result,main='NASA data',xlab='Day',xrange=c(1,365))

# Plots of the adjusted p-values
plot(ITP.result,main='NASA data', plot.adjpval = TRUE,xlab='Day',xrange=c(1,365))

# To have all plots in one device
layout(matrix(1:6,nrow=3,byrow=FALSE))
plot(ITP.result,main='NASA data', plot.adjpval = TRUE,xlab='Day',xrange=c(1,365))


# }