# 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

 1 2 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

fanova.tests, fmanova.ptbfr, fmanova.trp
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 # 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)