plotFANOVA: Plot Univariate Functional Data

In fdANOVA: Analysis of Variance for Univariate and Multivariate 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)

fdANOVA documentation built on May 2, 2019, 2:43 a.m.