#' Plot method for IWT results on functional ANOVA
#'
#' \code{plot} method for class "\code{IWTaov}". Plotting function creating a
#' graphical output of the IWT for the test on a functional analysis of
#' variance: functional data, and IWT-adjusted p-values of the F-tests on the
#' whole model and on each factor are plotted.
#'
#' @param x The object to be plotted. An object of class "\code{IWTaov}",
#' usually, a result of a call to \code{\link{IWTaov}}.
#' @param xrange Range of the \code{x} axis.
#' @param alpha1 First level of significance used to select and display
#' significant effects. Default is \code{alpha1 = 0.05}.
#' @param alpha2 Second level of significance used to select and display
#' significant effects. Default is \code{alpha1 = 0.01}. \code{alpha1} and
#' \code{alpha2} are s.t. \code{alpha2 < alpha1}. Otherwise the two values are
#' switched.
#' @param plot_adjpval A logical indicating wether the plots of adjusted
#' p-values have to be done. Default is \code{plot_adjpval = FALSE}.
#' @param ylim Range of the \code{y} axis. Default is \code{NULL}, giving a plot
#' with authomatic range for functional data.
#' @param col Colors for the plot of functional data. Default is \code{col = 1}.
#' @param ylab Label of \code{y} axis of the plot of functional data. Default is
#' "\code{Functional Data}".
#' @param main An overall title for the plots (it will be pasted to "Functional
#' Data and F-test" for the first plot and to factor names for the other
#' plots).
#' @param lwd Line width for the plot of the adjusted p-value function. Default
#' is \code{lwd=1}.
#' @param type line type for the plot of the adjusted p-value function. Default
#' is type='l'.
#' @param ... Additional plotting arguments that can be used with function
#' \code{plot}, such as \code{\link{graphical parameters}} (see
#' \code{\link{par}}).
#'
#' @return 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 \code{alpha1} and \code{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 F-tests on each
#' factor (with colors corresponding to the levels of the factor).
#'
#' @seealso \code{\link{IWTimage}} for the plot of p-values heatmaps. See also
#' \code{\link{IWT1}}, \code{\link{IWT2}} to perform the ITP to test on the
#' mean of one population and test of differences between two populations. See
#' \code{\link{ITPaovbspline}} for functional ANOVA based on B-spline basis
#' representation
#'
#' @references
#' Pini, A., & Vantini, S. (2017). Interval-wise testing for functional data.
#' \emph{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. \emph{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. \emph{Scandinavian Journal of Statistics} 45(4),
#' 1036-1061.
#'
#' @export
#' @examples
#' temperature <- rbind(NASAtemp$milan, NASAtemp$paris)
#' groups <- c(rep(0, 22), rep(1, 22))
#'
#' # Performing the IWT
#' IWT.result <- IWTaov(temperature ~ groups, B = 5L)
#'
#' # Summary of the IWT results
#' summary(IWT.result)
#'
#' # Plot of the IWT results
#' graphics::layout(1)
#' plot(IWT.result)
#'
#' # All graphics on the same device
#' graphics::layout(matrix(1:4, nrow = 2, byrow = FALSE))
#' plot(
#' IWT.result,
#' main = 'NASA data',
#' plot_adjpval = TRUE,
#' xlab = 'Day',
#' xrange = c(1, 365)
#' )
plot.IWTaov <- function(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",
...) {
if (alpha1 < alpha2) {
temp <- alpha1
alpha1 <- alpha2
alpha2 <- temp
}
object <- x
nvar <- dim(object$adjusted_pval_factors)[1]
p <- length(object$unadjusted_pval_F)
n <- dim(t(object$data.eval))[1]
xmin <- xrange[1]
xmax <- xrange[2]
abscissa_pval <- seq(xmin, xmax, len = p)
grDevices::devAskNewPage(ask = TRUE)
main_f <- paste(main, ": Functional Data and F-test")
main_f <- sub("^ : +", "", main_f)
if (is.null(ylim))
ylim <- range(object$data.eval)
if (nvar > 1) {
fda::matplot(
abscissa_pval,
t(object$data.eval),
type = 'l',
col = col,
main = main_f,
ylab = ylab,
ylim = ylim,
lwd = lwd,
...
)
difference1 <- which(object$adjusted_pval_F < alpha1)
if (length(difference1) > 0) {
for (j in 1:length(difference1)) {
min_rect <- abscissa_pval[difference1[j]] -
(abscissa_pval[2] - abscissa_pval[1]) / 2
max_rect <- min_rect + (abscissa_pval[2] - abscissa_pval[1])
graphics::rect(
min_rect,
graphics::par("usr")[3],
max_rect,
graphics::par("usr")[4],
col = "gray90",
density = -2,
border = NA
)
}
graphics::rect(
graphics::par("usr")[1],
graphics::par("usr")[3],
graphics::par("usr")[2],
graphics::par("usr")[4],
col = NULL,
border = "black"
)
}
difference2 <- which(object$adjusted_pval_F < alpha2)
if (length(difference2) > 0) {
for (j in 1:length(difference2)) {
min_rect <- abscissa_pval[difference2[j]] -
(abscissa_pval[2] - abscissa_pval[1]) / 2
max_rect <- min_rect + (abscissa_pval[2] - abscissa_pval[1])
graphics::rect(
min_rect,
graphics::par("usr")[3],
max_rect,
graphics::par("usr")[4],
col = "gray80",
density = -2,
border = NA
)
}
graphics::rect(
graphics::par("usr")[1],
graphics::par("usr")[3],
graphics::par("usr")[2],
graphics::par("usr")[4],
col = NULL,
border = "black"
)
}
fda::matplot(
abscissa_pval,
t(object$data.eval),
type = 'l',
col = col,
add = TRUE,
lwd = lwd,
...
)
}
names_all <- colnames(object$design_matrix)
interaz <- grep(':', names_all)
for (var in 1:(dim(object$adjusted_pval_factors)[1])) {
var_name <- rownames(object$adjusted_pval_factors)[var]
main_t <- paste(main, ': factor', var_name, sep = ' ')
main_t <- sub("^ : +", "", main_t)
if (length(grep(':', var_name)) > 0) { # Plot interaction
var12 <- strsplit(var_name, ':')
var1 <- var12[[1]][1]
var2 <- var12[[1]][2]
dummy_test1 <- grep(var1, names_all)
dummy_test2 <- grep(var2, names_all)
dummy_test <- intersect(dummy_test1, dummy_test2)
colors <- object$design_matrix[, dummy_test]
if (length(dim(colors)) > 1) {
colors <- apply(colors, 1, paste, collapse = '')
}
colors <- as.factor(colors)
} else { # Plot of a factor
dummy_test <- grep(var_name, names_all)
dummy_test <- setdiff(dummy_test, interaz)
colors <- object$design_matrix[, dummy_test]
if (length(dim(colors)) > 1) {
colors <- apply(colors, 1, paste, collapse = '')
}
colors <- as.factor(colors)
}
fda::matplot(
abscissa_pval,
t(object$data.eval),
type = 'l',
col = colors,
ylim = ylim,
lwd = 1,
main = main_t,
ylab = ylab,
...
)
difference1 <- which(object$adjusted_pval_factors[var, ] < alpha1)
if (length(difference1) > 0) {
for (j in 1:length(difference1)) {
min_rect <- abscissa_pval[difference1[j]] -
(abscissa_pval[2] - abscissa_pval[1]) / 2
max_rect <- min_rect + (abscissa_pval[2] - abscissa_pval[1])
graphics::rect(
min_rect,
graphics::par("usr")[3],
max_rect,
graphics::par("usr")[4],
col = "gray90",
density = -2,
border = NA
)
}
graphics::rect(
graphics::par("usr")[1],
graphics::par("usr")[3],
graphics::par("usr")[2],
graphics::par("usr")[4],
col = NULL,
border = "black"
)
}
difference2 <- which(object$adjusted_pval_factors[var, ] < alpha2)
if (length(difference2) > 0) {
for (j in 1:length(difference2)) {
min_rect <- abscissa_pval[difference2[j]] -
(abscissa_pval[2] - abscissa_pval[1]) / 2
max_rect <- min_rect + (abscissa_pval[2] - abscissa_pval[1])
graphics::rect(
min_rect,
graphics::par("usr")[3],
max_rect,
graphics::par("usr")[4],
col = "gray80",
density = -2,
border = NA
)
}
graphics::rect(
graphics::par("usr")[1],
graphics::par("usr")[3],
graphics::par("usr")[2],
graphics::par("usr")[4],
col = NULL,
border = "black"
)
}
graphics::matlines(
abscissa_pval,
t(object$data.eval),
type = 'l',
col = colors,
...
)
graphics::abline(h = 0, lty = 2, col = 1)
}
# Plot adjusted p-values
if (plot_adjpval) {
main_p <- paste(main, ': Adjusted p-values - F-test')
main_p <- sub("^ : +", "", main_p)
plot(
abscissa_pval,
object$adjusted_pval_F,
ylim = c(0, 1),
main = main_p,
ylab = 'p-value',
type = type,
lwd = 2,
...
)
difference1 <- which(object$adjusted_pval_F < alpha1)
if (length(difference1) > 0) {
for (j in 1:length(difference1)) {
min_rect <- abscissa_pval[difference1[j]] -
(abscissa_pval[2] - abscissa_pval[1]) / 2
max_rect <- min_rect + (abscissa_pval[2] - abscissa_pval[1])
graphics::rect(
min_rect,
graphics::par("usr")[3],
max_rect,
graphics::par("usr")[4],
col = "gray90",
density = -2,
border = NA
)
}
graphics::rect(
graphics::par("usr")[1],
graphics::par("usr")[3],
graphics::par("usr")[2],
graphics::par("usr")[4],
col = NULL,
border = "black"
)
}
difference2 <- which(object$adjusted_pval_F < alpha2)
if (length(difference2) > 0) {
for (j in 1:length(difference2)) {
min_rect <- abscissa_pval[difference2[j]] -
(abscissa_pval[2] - abscissa_pval[1]) / 2
max_rect <- min_rect + (abscissa_pval[2] - abscissa_pval[1])
graphics::rect(
min_rect,
graphics::par("usr")[3],
max_rect,
graphics::par("usr")[4],
col = "gray80",
density = -2,
border = NA
)
}
graphics::rect(
graphics::par("usr")[1],
graphics::par("usr")[3],
graphics::par("usr")[2],
graphics::par("usr")[4],
col = NULL,
border = "black"
)
}
for (j in 0:10) {
graphics::abline(h = j / 10, col = 'lightgray', lty = "dotted")
}
graphics::lines(
abscissa_pval,
object$adjusted_pval_F,
lwd = 2,
type = type,
...
)
for (var in 1:(dim(object$adjusted_pval_factors)[1])) {
var_name <- rownames(object$adjusted_pval_factors)[var]
main_p <- paste(main, ': Adjusted p-values - factor', var_name)
main_p <- sub("^ : +", "", main_p)
plot(
abscissa_pval,
object$adjusted_pval_factors[var, ],
ylim = c(0, 1),
main = main_p,
ylab = 'p-value',
lwd = 2,
type = type,
...
)
difference1 <- which(object$adjusted_pval_factors[var, ] < alpha1)
if (length(difference1) > 0) {
for (j in 1:length(difference1)) {
min_rect <- abscissa_pval[difference1[j]] -
(abscissa_pval[2] - abscissa_pval[1]) / 2
max_rect <- min_rect + (abscissa_pval[2] - abscissa_pval[1])
graphics::rect(
min_rect,
graphics::par("usr")[3],
max_rect,
graphics::par("usr")[4],
col = "gray90",
density = -2,
border = NA
)
}
graphics::rect(
graphics::par("usr")[1],
graphics::par("usr")[3],
graphics::par("usr")[2],
graphics::par("usr")[4],
col = NULL,
border = "black"
)
}
difference2 <- which(object$adjusted_pval_factors[var, ] < alpha2)
if (length(difference2) > 0) {
for (j in 1:length(difference2)) {
min_rect <- abscissa_pval[difference2[j]] -
(abscissa_pval[2] - abscissa_pval[1]) / 2
max_rect <- min_rect + (abscissa_pval[2] - abscissa_pval[1])
graphics::rect(
min_rect,
graphics::par("usr")[3],
max_rect,
graphics::par("usr")[4],
col = "gray80",
density = -2,
border = NA
)
}
graphics::rect(
graphics::par("usr")[1],
graphics::par("usr")[3],
graphics::par("usr")[2],
graphics::par("usr")[4],
col = NULL,
border = "black"
)
}
for (j in 0:10) {
graphics::abline(h = j / 10, col = 'lightgray', lty = "dotted")
}
graphics::lines(
abscissa_pval,
object$adjusted_pval_factors[var, ],
type = type,
lwd = 2,
...
)
}
}
grDevices::devAskNewPage(ask = FALSE)
}
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