plotFANOVA {fdANOVA}

Plot Univariate Functional Data

Description

Univariate functional observations with or without indication of groups as well as mean functions of samples are plotted. We assume that n univariate functional observations are observed on a common grid of \mathcal{T} design time points equally spaced in I=[a,b] (see Section 3.1 of the vignette file, vignette("fdANOVA", package = "fdANOVA")).

Usage

plotFANOVA(x, group.label = NULL, int = NULL, separately = FALSE,
           means = FALSE, smooth = FALSE, ...)

Arguments

x

a \mathcal{T}\times n matrix of data, whose each column is a discretized version of a function and rows correspond to design time points.

group.label

a character vector containing group labels. Its default value means that all functional observations are drawn without division into groups.

int

a vector of two elements representing the interval I=[a,b]. When it is not specified, it is determined by a number of design time points.

separately

a logical indicating how groups are drawn. If separately = FALSE, groups are drawn on one plot by different colors. When separately = TRUE, they are depicted in different panels.

means

a logical indicating whether to plot only group mean functions.

smooth

a logical indicating whether to plot reconstructed data via smoothing splines instead of raw data.

...

additional arguments not used.

Author(s)

Tomasz Gorecki, Lukasz Smaga

See Also

fanova.tests, fmanova.ptbfr, fmanova.trp

Examples

# Some of the examples may run some time.

# gait data (both features)
library(fda)
gait.data.frame <- as.data.frame(gait)
x.gait <- vector("list", 2)
x.gait[[1]] <- as.matrix(gait.data.frame[, 1:39])
x.gait[[2]] <- as.matrix(gait.data.frame[, 40:78])

# vector of group labels
group.label.gait <- rep(1:3, each = 13)

plotFANOVA(x = x.gait[[1]], int = c(0.025, 0.975))
plotFANOVA(x = x.gait[[1]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975))
plotFANOVA(x = x.gait[[1]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), separately = TRUE)
plotFANOVA(x = x.gait[[1]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), means = TRUE)

plotFANOVA(x = x.gait[[1]], int = c(0.025, 0.975), smooth = TRUE)
plotFANOVA(x = x.gait[[1]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), smooth = TRUE)
plotFANOVA(x = x.gait[[1]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), separately = TRUE, smooth = TRUE)
plotFANOVA(x = x.gait[[1]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), means = TRUE, smooth = TRUE)

plotFANOVA(x = x.gait[[2]], int = c(0.025, 0.975))
plotFANOVA(x = x.gait[[2]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975))
plotFANOVA(x = x.gait[[2]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), separately = TRUE)
plotFANOVA(x = x.gait[[2]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), means = TRUE)

plotFANOVA(x = x.gait[[2]], int = c(0.025, 0.975), smooth = TRUE)
plotFANOVA(x = x.gait[[2]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), smooth = TRUE)
plotFANOVA(x = x.gait[[2]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), separately = TRUE, smooth = TRUE)
plotFANOVA(x = x.gait[[2]], group.label = as.character(group.label.gait),
           int = c(0.025, 0.975), means = TRUE, smooth = TRUE)

# Canadian Weather data (both features)
library(fda)
x.CW <- vector("list", 2)
x.CW[[1]] <- CanadianWeather$dailyAv[,,1]
x.CW[[2]] <- CanadianWeather$dailyAv[,,2]

# vector of group labels
group.label.CW <- rep(c("Eastern", "Western", "Northern"), c(15, 15, 5))

plotFANOVA(x = x.CW[[1]])
plotFANOVA(x = x.CW[[1]], group.label = as.character(group.label.CW))
plotFANOVA(x = x.CW[[1]], group.label = as.character(group.label.CW),
           separately = TRUE)
plotFANOVA(x = x.CW[[1]], group.label = as.character(group.label.CW),
           means = TRUE)

plotFANOVA(x = x.CW[[1]], smooth = TRUE)
plotFANOVA(x = x.CW[[1]], group.label = as.character(group.label.CW),
           smooth = TRUE)
plotFANOVA(x = x.CW[[1]], group.label = as.character(group.label.CW),
           separately = TRUE, smooth = TRUE)
plotFANOVA(x = x.CW[[1]], group.label = as.character(group.label.CW),
           means = TRUE, smooth = TRUE)

plotFANOVA(x = x.CW[[2]])
plotFANOVA(x = x.CW[[2]], group.label = as.character(group.label.CW))
plotFANOVA(x = x.CW[[2]], group.label = as.character(group.label.CW),
           separately = TRUE)
plotFANOVA(x = x.CW[[2]], group.label = as.character(group.label.CW),
           means = TRUE)

plotFANOVA(x = x.CW[[2]], smooth = TRUE)
plotFANOVA(x = x.CW[[2]], group.label = as.character(group.label.CW),
           smooth = TRUE)
plotFANOVA(x = x.CW[[2]], group.label = as.character(group.label.CW),
           separately = TRUE, smooth = TRUE)
plotFANOVA(x = x.CW[[2]], group.label = as.character(group.label.CW),
           means = TRUE, smooth = TRUE)