tcata.line.plot {tempR}

Temporal Check-All-That-Apply (TCATA) curve

Description

Plots TCATA curves based on count or proportion data. Can also be used for plotting Temporal Dominance of Sensations (TDS) curves based on dominance counts or proportions.

Usage

tcata.line.plot(X, n = 1, attributes = c(), times = c(),
lwd = 1, lty = 1, line.col = c(),
emphasis = NA, emphasis.col = c(), emphasis.lty = 1, emphasis.lwd = 3,
declutter = NA,
reference = NA, ref.col = c(), ref.lty = 2, ref.lwd = 1,
highlight = FALSE, highlight.col = c(), highlight.lty = 1, highlight.lwd = 5,
xlab = "Time", ylab = "Citation proportion", axes.font = 1,
axes.cex = 1, xlim = c(), las = 0,
x.increment = 5, box = FALSE,
legend.cex = 1, legend.font = 1, legend.pos = "topleft", legend.ncol = 2,
height = 8, width = 12, main = "",
save.format = "", save.as = "" )

Arguments

X

matrix of proportions (or, if there is no missing data, on counts), typically with Attributes in rows and times in columns.

n

The number of observations if X is a count matrix. Keep n = 1 if X is a matrix of proportions.

attributes

a vector of attribute labels, corresponding to the attributes in X.

times

a vector of time, corresponding to the times in X.

lwd

line width for attribute curves that matches either attributes or X.

lty

line types for attribute curves that matches either attributes or X.

line.col

attribute curves colours that matches attributes.

emphasis

matrix matching X in its dimensions, with a numeric value corresponding to points requiring emphasis, and NA for points without emphasis.

emphasis.col

vector colours for attributes corresponding to rows of X; taken from line.col if not specified.

emphasis.lty

either a line type (lty) for all emphasis lines .

emphasis.lwd

line weight associated with the emphasis line.

declutter

a matrix with the same dimensions as X; give the value 1 to show a proportion in X and reference (if given), otherwise give 0 or NA.

reference

a matrix with the same dimensions as X; give the value 1 if reference will be shown (allowing finer control than declutter), otherwise give 0 or NA

ref.col

reference line colour

ref.lty

reference line type

ref.lwd

reference line width

highlight

TRUE if differences will be highlighted; otherwise FALSE

highlight.col

a vector of colours for attributes corresponding to rows of X

highlight.lty

line type associated with the highlighting

highlight.lwd

line weight associated with the highlighting line

xlab

label for the x axis

ylab

label for the y axis

axes.font

font for axes labels; see par

axes.cex

size for axes labels.

xlim

x limits specified using a vector of 2 (ascending) numbers.

las

numeric in 0,1,2,3 indicating style of axis labels; see par

x.increment

interval between times when labelling the x axis

box

draw box around plot area; see: box

legend.cex

size of markers shown in the legend

legend.font

font for the legend; see text

legend.pos

location of plot legend; defaults to "topleft"

legend.ncol

number of columns in legend

height

window height

width

window width

main

plot title; see plot

save.format

If indicated, this will be the fle type for the save image. Defaults to "eps" (eps format). Other possible values are "" (not saved) or "png" (png format)

save.as

Filename if the file will be saved

References

Castura, J.C., Antúnez, L., Giménez, A., Ares, G. (2016). Temporal check-all-that-apply (TCATA): A novel temporal sensory method for characterizing products. Food Quality and Preference, 47, 79-90. doi:10.1016/j.foodqual.2015.06.017

Meyners, M., Castura, J.C. (2018). The analysis of temporal check-all-that-apply (TCATA) data. Food Quality and Preference, 67, 67-76. doi:10.1016/j.foodqual.2017.02.003

Examples

# example using 'syrah' data set
low1 <- t(syrah[seq(3, 1026, by = 6), -c(1:4)])
colnames(low1) <- 10:180
tcata.line.plot(get.smooth(low1), lwd = 2, main = "Low-ethanol wine (Sip 1)")

# example using 'ojtcata' data set
data(ojtcata)
# comparison of Orange Juice 1 vs. Other OJs (2 to 6)
oj1.v.other <- citation.counts(ojtcata, product.name = "1", product.col = 2,
       attribute.col = 4, results.col = 5:25, comparison = "other")
times <- get.times(colnames(ojtcata)[-c(1:4)])
attributes <- unique(ojtcata$attribute)
palettes <- make.palettes(length(attributes))

# plot results
tcata.line.plot(oj1.v.other$P1, n = oj1.v.other$Pn,
   attributes = attributes, times = times,
   line.col = palettes$pal, reference = oj1.v.other$ref, ref.lty = 3,
   declutter = oj1.v.other$declutter, highlight = TRUE, highlight.lwd = 4,
   highlight.col = palettes$pal.light,
   height = 7, width = 11, legend.cex = 0.7, main = "Product 1 vs. Other Products")

# example showing plots similar to those in Meyners & Castura (2018)
# comparison of Orange Juice 1 vs. All OJs (1 to 6)
oj1.v.all <- citation.counts(ojtcata, product.name = "1", product.col = 2,
       attribute.col = 4, results.col = 5:25, comparison = "average")
lty.mat <- matrix(1,nrow=6,ncol=21)
lty.mat[, 1:3] <- c(rep(NA,8),rep(c(1,NA),4), 1, 1)
lty.mat[2, 9:12] <- lty.mat[5, 8] <- 3
tcata.line.plot(oj1.v.all$P1, n = oj1.v.all$Pn, attributes = attributes,
                times = times, line.col = palettes$pal, lty = lty.mat, lwd = 2,
                height = 7, width = 11, legend.cex = 0.7, main = "Product 1 vs. All Products")