Help for package tempR

Type:

Package

Title:

Temporal Sensory Data Analysis

Version:

0.10.1.1

Date:

2024-5-7

Author:

J.C. Castura

[aut, cre, ctb]

Maintainer:

J.C. Castura <jcastura@compusense.com>

Description:

Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.

Depends:

R (≥ 4.3.0)

Imports:

grDevices, stats, graphics

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

LazyData:

TRUE

Encoding:

UTF-8

RoxygenNote:

7.3.1

NeedsCompilation:

Packaged:

2024-05-07 21:59:57 UTC; jcastura

Repository:

CRAN

Date/Publication:

2024-05-07 22:40:02 UTC

tempR

Description

Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS).

TCATA data set: Syrah wines

Description

TCATA citation proportions for three wine treatments evaluated using a two-sip evaluation protocol.

Format

A data frame with 1026 rows (3 treatments * 2 sips * 171 time slices) and 13 columns:

[, 1] WineSip (chr) Code for wine and sip
[, 2] Wine (chr) Code for wine (H=high, L=low, A=adjusted)
[, 3] Sip (int) Sip number
[, 4] Time (int) Time, in seconds
[, 5] Astringency (num) citation proportions
[, 6] Bitter (num) citation proportions
[, 7] Dark Fruit (num) citation proportions
[, 8] Earthy (num) citation proportions
[, 9] Green (num) citation proportions
[,10] Heat (num) citation proportions
[,11] Red Fruit (num) citation proportions
[,12] Spices (num) citation proportions
[,13] Sour (num) citation proportions

References

Baker, A.K., Castura, J.C., & Ross, C.F. (2016). Temporal check-all-that-apply characterization of Syrah wine finish. Journal of Food Science, 81, S1521-S1529. doi:10.1111/1750-3841.13328.

Examples

head(syrah, 3) # review first 3 rows of 'syrah' data set

TCATA data set: orange juice

Description

Raw results from 20-s TCATA evaluations of six orange juice samples by 50 consumers.

Format

A data frame with 1800 rows (50 consumers * 6 samples * 6 attributes) and 25 columns (4 headers + 21 time slices)

[, 1] cons (int) consumer id
[, 2] samp (chr) sample id
[, 3] samp_pos (int) position of sample in serving order
[, 4] attribute (chr) sensory attribute
[, 5:25] time_99s (int) value is 1 if attribute is selected at time slice; otherwise value is 0

References

Ares, G., Jaeger, S. R., Antúnez, L., Vidal, L, Giménez, A., Coste, B., Picallo, A., & Castura, J.C. (2016). Comparison of TCATA and TDS for dynamic sensory characterization of food products. Food Research International, 78, 148-158. doi:10.1016/j.foodres.2015.10.023

Examples

head(ojtcata) # review first 6 rows of 'ojtcata' data set

TDS data set: orange juice

Description

Raw results from 20-s TDS evaluations of six orange juice samples by 50 consumers.

Format

A data frame with 1800 rows (50 consumers * 6 samples * 6 attributes) and 25 columns (4 headers + 21 time slices)

[, 1] cons (int) consumer id
[, 2] samp (chr) sample id
[, 3] samp_pos (int) position of sample in serving order
[, 4] attribute (chr) sensory attribute
[, 5:25] time_99s (int) value is 1 if attribute is selected at time slice; otherwise value is 0

References

Examples

head(ojtds) # review first 6 rows of 'ojtds' data set

TDS data set: snack bars

Description

Raw TDS results from 24 assessors who evaluated four snack bars in triplicate.

Format

A data frame with 1440 rows (24 assessors * 3 sessions * 4 samples * 5 attributes) and 455 columns (4 header rows + 451 time slices)

[,1] assessor (chr) assessor id
[,2] session (chr) session id
[,3] sample (chr) sample id
[,4] attribute (chr) sensory attribute
[,5:455] time_99.9s (chr) value is 1 if attribute is dominant at time slice; otherwise value is 0

References

Findlay, C.J., Castura, J.C., & Valeriote, E. (2014). Temporal methods: A comparative study of four different techniques. In 17th IUFoST Congress. 17-21 August. Montréal, Québec, Canada.

Examples

head(bars, 2) # review first 2 rows of 'bars' data set

Adjust color brightness

Description

Select suitable colors for highlighting plots.

Usage

adjust.brightness(rgb.in, percent = 10)

Arguments

rgb.in

rgb of input color

percent

the degree to which input color will be modified/brightened

Value

hex hex code for new color

Examples

(rgb.in <- c(col2rgb("red")))
adjust.brightness(rgb.in, percent = 10)

Get bootstrap confidence bands for attribute selections

Description

Get bootstrap confidence bands for TCATA attribute citation rates or TDS attribute dominance rates.

Usage

bootstrap.band(X, boot = 999, alpha = 0.05, return.bias = FALSE)

Arguments

X

data frame of indicator data (with possible values 0 or 1)

boot

number of virtual panels

alpha

alpha level for bootstrap confidence bands

return.bias

indicates whether to return bias associated with bootstrap mean value

Details

Get bootstrap confidence bands for TCATA attribute citation rates or TDS attribute dominance rates.

Value

lcl lower 100(alpha/2)% bootstrap confidence limit

ccl upper 100(1 - alpha/2)% bootstrap confidence limit

bias provided if output.bias = TRUE

Examples

x <- ojtcata[ojtcata$samp == 1 & ojtcata$attribute == "Sweetness",  -c(1:4)]
x.boot.ci <- bootstrap.band(x, boot = 99) # 99 is only for illustrative purposes
x.boot.ci

Counts TCATA Citations and Observations for a Product and a Comparison Set

Description

Calculates how many times a specified product was checked and how many times a comparison set was checked. The number of evaluations for the product and comparison set are also calculated, along with a reference and decluttering matrix for plotting in tcata.line.plot.

Usage

citation.counts(x, product.name = "", product.col = 1,
attribute.col = 2, results.col = NULL, comparison = "average")

Arguments

x

matrix of TCATA 0/1 data with (Assessors x Products x Reps x Attributes) in rows with row headers and (Times) in columns

product.name

name of the product for which to calculate how many times a product was checked and not checked

product.col

index of column in x that contains the product identities

attribute.col

index of column in x that contains the attribute identities

results.col

indices of columns in x that contain the raw (0/1) data

comparison

specifies whether the comparison will be with the average of all products ("average" (default)) or with the average of the other products ("other", i.e. excludes the product specified by product.name)

Value

list object with three elements:

P1 matrix of counts for product specified by product.name (attributes are in rows; times are in columns).
Pn number of observations for product.name
C1 matrix of counts for comparison set specified by comparison (dimensions equal to P1.
Cn number of observations for the comparison set defined by comparison
ref a matrix of citation proportions for the comparison set specified by comparison (dimensions equal to P1; can be used to draw a reference line; see tcata.line.plot
declutter a matrix for decluttering in a line plot (dimensions equal to P1; see get.decluttered

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 'ojtcata' data set
data(ojtcata)

# comparison of Orange Juice 3 vs. all other OJs (1, 2, 4, 5, 6)
oj3.v.other <- citation.counts(ojtcata, product.name = "3", product.col = 2,
       attribute.col = 4, results.col = 5:25, comparison = "other")

# show results
oj3.v.other

times <- get.times(colnames(ojtcata)[-c(1:4)])
attributes <- unique(ojtcata$attribute)
palettes <- make.palettes(length(attributes))

# plot results
tcata.line.plot(oj3.v.other$P1, n = oj3.v.other$Pn,
   attributes = attributes, times = times,
   line.col = palettes$pal, reference = oj3.v.other$ref, ref.lty = 3,
   declutter = oj3.v.other$declutter, highlight = TRUE, highlight.lwd = 4,
   highlight.col = palettes$pal.light,
   height = 7, width = 11, legend.cex = 0.7, main = "Product 3 vs. Other Products")

Convert TCATA data

Description

Converts TCATA data from a set of onset-offset times to an indicator vector (0s and 1s). Also works for TDS data.

Usage

convert.tcata(X, times, decimal.places = 2)

Arguments

X

matrix with onset (start) times in first column and offset (stop) times in second column

times

time slices for output indicator vector

decimal.places

decimal places used in times; used for naming of the indices of out.vec

Value

out.vec indictor vector(0s and 1s)

Examples

X <- rbind(c(3.18, 6.83), c(8.46, 11.09), c(18.61, 21.80))
times <- seq(0, 25, by = 0.01)
Xnew <- convert.tcata(X, times)
Xnew

Convert Temporal Category data

Description

Converts Temporal Category data from a set of onset-offset times and ratings to an vector of ratings.

Usage

convert.tcategory(X, in.scores, times, decimal.places = 2)

Arguments

X

matrix with onset (start) times in first column and offset (stop) times in second column

in.scores

vector of category values corresponding to rows of X

times

time slices for output vector

decimal.places

decimal places used in times; used for naming of the indices of out.vec

Value

out.vec indictor vector(0s and 1s)

Examples

X <- rbind(c(3.18, 6.83), c(8.46, 11.09), c(18.61, 21.80))
in.scores <- c(7, 6, 5)
times <- seq(0, 25, by = 0.01)
Xnew <- convert.tcategory(X, in.scores, times)
Xnew

Count attribute selections

Description

Count the number of times that the attribute was selected (or optionally: deselected) in a single TCATA or TDS evaluation.

Usage

count.selections(x, deselections = FALSE)

Arguments

x

vector of binary data (with possible values 0 or 1)

deselections

set to TRUE if purpose is to count the number of deselections

Details

Count the number of times that the attribute was selected (or, optionally, deselected) in a single TCATA or TDS evaluation.

Value

count of selections (or deselections if deselections = TRUE)

Examples

data(bars)
paste0(bars[1, -c(1:4)], collapse = "")
# this attribute was checked 3 times and unchecked 2 times
count.selections(bars[1, -c(1:4)])
count.selections(bars[1, -c(1:4)], deselections = TRUE)

Calculate city block distance between two matrices

Description

Calculates the city block distance between two matrices.

Usage

dist.city.block(x, y)

Arguments

x

first matrix

y

second matrix

Value

cbdist city block distance between x and y

Examples

  x <- matrix(0, nrow = 5, ncol = 7)
  y <- matrix(1, nrow = 5, ncol = 7)
  dist.city.block(x, y)

  y <- matrix(c(rep(0, 15), rep(1, 20)), nrow = 5, ncol = 7)
  dist.city.block(x, y)

Draw h-cross, range box, and box to enclose h-box

Description

Draw h-cross, range box, and box to enclose h-cross, described by Castura, Rutledge, Ross & Næs (2022).

Usage

draw.hcross(rangebox = NULL, hcross = NULL,
rbox.col = "black", rbox.lty = "dotted", rbox.lwd = 4.5,
hbox.col = "lightgrey", hbox.lty = "solid", hbox.lwd = 4.5,
hcross.col = "black",hcross.lty = "solid",
hcross.signif.lwd = 7, hcross.nsd.lwd = 3.5)

Arguments

rangebox

matrix where columns 1 and 2 are x and y dimensions and rows 1 and 2 are the minimum and maximum values

hcross

matrix where columns 1 and 2 are x and y dimensions and rows 1 and 2 are the half-width of the confidence interval, which is often 95% thus approximately 2x the standard error

rbox.col

line color for the range box (default: "black")

rbox.lty

line type for the range box (default: "dotted")

rbox.lwd

line width for the range box (default: 4.5)

hbox.col

line color for the box enclosing the h-cross (default: "lightgrey")

hbox.lty

line type for the box enclosing the h-cross (default: "dotted")

hbox.lwd

line width for the box enclosing the h-cross (default: 4.5)

hcross.col

line color for the h-cross (default: "solid")

hcross.lty

line type for the h-cross (default: "solid")

hcross.signif.lwd

line width for the h-cross where there is a significant difference (default: 7)

hcross.nsd.lwd

line width for the h-cross where there is a significant difference (default: 3.5)

Details

Draw h-cross, range box, and box to enclose h-box.

References

Castura, J.C., Rutledge, D.N., Ross, C.F., & Næs, T. (2022). Discriminability and uncertainty in principal component analysis (PCA) of temporal check-all-that-apply (TCATA) data. Food Quality and Preference, 96, 104370. doi:10.1016/j.foodqual.2021.104370

Fills gaps

Description

Replace gaps in TDS and TCATA data with replacement responses.

Usage

fill.gaps(y, subst = 0, repl = 1)

Arguments

y

vector (or data frame) of Bernoulli data which may contain gaps

subst

value occurring in a gap (which represents real data outside a gap). Default is 0.

repl

value occurring for a response (used to replace gap values). Default is 1.

Value

out vector (or data frame) of Bernoulli data with filled gaps

Examples

# vector with gaps: x with NA gaps (e.g. due to attribute cuing)
(x <- rep(c(rep(NA, 4), rep(1, 4)), 2))
fill.gaps(x, subst = NA)

# array with gaps: y with an gap of 0s (e.g. due to attribute fading)
(y <- structure(c(0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,
                  1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0),
                .Dim = c(3L, 10L),
                .Dimnames = list(1:3, 1:10)))
fill.gaps(y)

TDS chance proportion

Description

Obtains the TDS chance proportion based on the number of attributes, as proposed by Pineau et al. (2009; Eq. 1).

Usage

get.chance(attributes = c(), include.stop = FALSE)

Arguments

attributes

number of attributes used in the TDS ballot.

include.stop

defaut is FALSE. Default should be kept if time standardization is applied. Optionally, set to TRUE if analyzing data on the raw timeline.

References

Pineau, N., Schlich, P., Cordelle, S., Mathonnière, C., Issanchou, S., Imbert, A., Rogeaux, M., Etiévant, P., & Köster, E. (2009). Temporal dominance of sensations: Construction of the TDS curves and comparison with time–intensity. Food Quality and Preference, 20, 450–455. doi:10.1016/j.foodqual.2009.04.005

Examples

# example using 'bars' data set
attributes <- unique(bars$attribute)
chance <- get.chance(attributes)
chance

Get decluttering matrix indicating where to show/hide reference lines

Description

Declutter TCATA curves by hiding reference lines from plots showing TCATA curves.

Usage

get.decluttered(x = x, n.x = n.x, y = y, n.y = n.y, alpha = 0.05)

Arguments

x

selections for sample of interest (can be a vector if several samples of interest)

n.x

evaluations of x (can be a vector if several samples of interest)

y

selections for comparison (can be a vector if several comparisons will be made)

n.y

evaluations of y (can be a vector if several comparisons of interest)

alpha

significance level

Value

declutter vector in which 1 indicates "show" and NA indicates "hide"

References

Examples


# functionality of get.decluttered() is conveniently provided in citation.counts()

# Data set: ojtcata
# Get declutter matrix for comparison of Product 2 vs. average of all products
data(ojtcata)
oj2.v.all <- citation.counts(ojtcata, product.name = "2", product.col = 2,
       attribute.col = 4, results.col = 5:25, comparison = "average")
oj2.v.all$declutter

# same as

p2.declutter <- get.decluttered(x = c(oj2.v.all$P1), n.x = oj2.v.all$Pn,
                                y = c(oj2.v.all$C1), n.y = oj2.v.all$Cn)
(p2.declutter <- matrix(p2.declutter, nrow = nrow(oj2.v.all$P1)))

Get vector of difference in dominance rates

Description

Get vector of difference in dominance rates

Usage

get.differences(x, y)

Arguments

x

matrix of dominance indicators for a single productxattribute (rows = evaluations, columns = times)

y

matrix of dominance indicators for a different product (same attribute)

Value

out vector of differences in dominance rates

References

Examples

# example using 'bars' data set
bars.m <- aggregate(bars[, -c(1:4)], list(sample = bars$sample, attribute = bars$attribute), mean)
bars.m <- bars.m[order(bars.m$sample, bars.m$attribute), ]
attributes <- bars.m$attribute[bars.m$sample == 1]
times <- get.times(colnames(bars.m)[-c(1:2)])
bar1 <- bars.m[bars.m$sample == 1 & bars.m$attribute == "Caramelized Flavour", -c(1:2)]
bar2 <- bars.m[bars.m$sample == 2 & bars.m$attribute == "Caramelized Flavour", -c(1:2)]
b.diff <- get.differences(bar1, bar2)
round(b.diff, 3)

# toy example
x <- data.frame(t10 = c( NA,  0,  0,  0,  1,  1,  0,  0,  1,  0, NA),
                t15 = c(  1,  0,  0,  1,  1,  1,  0,  1,  0,  1,  0),
                t20 = c(  1,  1,  1,  1,  1,  1,  1,  0,  1, NA,  0))
y <- data.frame(t10 = c( NA, NA,  0,  0,  1,  1,  0,  0,  0,  0, NA),
                t15 = c(  0,  0,  0,  0,  1,  0,  1,  1,  0,  1,  1),
                t20 = c(  1,  0,  1,  1,  0,  0,  1, NA,  1, NA,  0))
get.differences(x, y)

Get TDS dominance rates

Description

Get TDS dominance rates.

Usage

get.dominance.rates(citations, n)

Arguments

citations

matrix of dominance counts

n

number of observations (evaluations) per cell

References

Examples

x <- rbind(c( 6,  6,  8, 14, 16, 22, 22, 21, 13, 11, 14,  7,  7,  6,  5,  3),
           c(14, 24, 31, 36, 37, 39, 44, 48, 51, 55, 48, 40, 30, 20, 10,  5),
           c( 7,  8,  9, 15, 17, 21, 21, 20, 21, 22, 18, 17, 17, 20, 20, 20),
           c(44, 23, 23, 26,  1,  2,  2,  2,  2,  3,  4,  7, 15, 14, 18, 22),
           c(20, 30, 20,  0, 20,  7,  2,  0,  4,  0,  7, 20, 22, 31, 38, 41))
colnames(x) <- 0:15
get.dominance.rates(x, n = 91)

Pairwise comparisons

Description

p-value for pairwise comparisons.

Usage

get.mat.diff.sign(x = x, y = y, n.x = n.x, n.y = n.x, test.type = "f")

Arguments

x

citations for product x

y

citations for product y

n.x

total observations for x

n.y

total observations for y

test.type

So far only Fisher's exact test is implemented ("f")

References

Examples

# Toy TCATA citations data for two samples: s1, s2
s1 <- t(data.frame(sweet =  c(10, 23, 25, 26, 26, 43, 44),
                   bitter = c( 4, 18, 19, 27, 36, 43, 54),
                   sour = c(40, 53, 85, 70, 46, 33, 24)))
s2 <- t(data.frame(sweet = c(11, 33, 45, 46, 56, 43, 44),
                   bitter = c( 0, 11, 11, 14, 25, 35, 34),
                   sour = c(30, 33, 35, 20, 26, 23, 24)))
colnames(s1) <- colnames(s2) <- paste0("time_", seq(5, 35, by = 5), "s")
n <- 90
signif <- get.mat.diff.sign(s1, s2, n, n)
signif

TDS significance proportion

Description

Obtains the TDS significance proportion based on the number of observations and chance, as proposed by Pineau et al. (2009; Eq. 1).

Usage

get.significance(chance, n, alpha = 0.05)

Arguments

chance

chance proportion; see get.chance.

n

number of observations.

alpha

significance level for binomial test of 2 independent proportions (based on normal approximation; see: Pineau et al., 2009, Eq. 1)

Details

The TDS significance level proposed by Pineau et al. (2009, Eq. 1) provides a simple and widely used heuristic approach for contextualizing observed dominance rates, but should not be used for statistical inference.

References

Examples

# example using 'bars' data set
attributes <- unique(bars$attribute)
chance <- get.chance(attributes)
signif <- get.significance(chance, nrow(unique(bars[, 1:2])))
signif

Get least significant differences for pairwise comparisons

Description

Get least significant differences for pairwise comparisons (see Pineau et al., 2009, Eq. 2).

Usage

get.significance.diff(x, y, alpha = 0.05)

Arguments

x

matrix of dominance data (0/1) related to one entity

y

matrix of dominance data (0/1) related to another entity

alpha

significance for one-sided test (default 0.05)

Details

Calculation of least significant differences for TDS difference curves based on Pineau et al. (2009, Eq. 2). The absolute value of the observed dominance rate for a give attribute*time must exceed the corresponding least significant difference calculated here to be considered significant.

Value

out least significant difference (at level alpha) for dominance differences in matrix

References

Examples

# toy data example
x <- data.frame(t10 = c(rep(NA, 15), rep(0, 50), rep(1, 20)),
                t15 = c(rep(NA,  4), rep(0, 61), rep(1, 20)),
                t20 = c(rep(0, 55), rep(1, 30)))
y <- data.frame(t10 = c(rep(NA, 15), rep(0, 50), rep(1, 20)),
                t15 = c(rep(NA,  0), rep(0, 21), rep(1, 64)),
                t20 = c( rep(0, 35), rep(1, 50)))
signif.xy <- get.significance.diff(x, y)
#compare with observed differences
diff.xy <- get.differences(x, y)
abs(diff.xy) > signif.xy

# real data example - differences between Bar 1 and Bar 2 on the attribute "Grain Flavour"
attributes <- unique(bars$attribute)
times <- get.times(colnames(bars)[-c(1:4)])
bar1 <- bars[bars$sample == 1 & bars$attribute == "Grain Flavour", -c(1:4)]
bar2 <- bars[bars$sample == 2 & bars$attribute == "Grain Flavour", -c(1:4)]
signif.1vs2 <- get.significance.diff(bar1, bar2)
# review observed difference in dominance rates vs. least significant differences
diff.1vs2 <- get.differences(bar1, bar2)
abs(diff.1vs2) > signif.1vs2
# differences between samples start at 1.1s and occur throughout the 45.0 evaluation period

Convenience function for curve smoothing

Description

Smooth TCATA curves, constraining smooth within low.bound and up.bound.

Usage

get.smooth(y, w = NULL, spar = 0.5, low.bound = 0, up.bound = 1)

Arguments

y

the vector of proportions (or counts) to be smoothed. If a data frame is provided then smoothing is conducted on each row.

w

an optional vector of weights; see smooth.spline

spar

smoothing parameter; see smooth.spline

low.bound

lower bound for smoothed proportions

up.bound

upper bound for smoothed proportions

Value

out smoothed vector (or data frame with smoothed rows)

References

Examples

# example using 'syrah' data set
low1 <- t(syrah[seq(3, 1026, by = 6), -c(1:4)])
colnames(low1) <- 10:180
x <- get.smooth(low1)
round(x, 3)

Get times

Description

Convenience function to convert exported time labels, e.g. from character format c('time_0.1s', 'time_0.2s', ...) or related format to numeric format c(0.1, 0.2, ...).

Usage

get.times(time.char, trim.left = "time_", trim.right = "s")

Arguments

time.char

vector of characters containing the time

trim.left

string to be trimmed from left

trim.right

string to be trimmed from right

Details

Convenience function for getting times from column headers from common data export formats.

Value

times vector of times in numeric format

Examples

get.times(colnames(bars)[-c(1:4)])

(sample.colnames <- paste0("X", 0:30))
get.times(sample.colnames, trim.left = "X", trim.right = "")

Count observations with missing data

Description

Count observations with missing data.

Usage

lengthwhichis.na(x)

Arguments

x

vector data which may contain missings

Value

count of observations where data are missing

Examples

x <- c(rep(NA,18), rep(1,18), rep(0,10), rep(NA, 10))
lengthwhichis.na(x)

Convenience function for getting a pretty palette and highlight colours

Description

Make a vector of n pretty colours, and n matching highlight colours.

Usage

make.palettes(n)

Arguments

n

number of colours for each palette

Value

pal A character vector, cv, of colours that look pretty.

pal.light A character vector, cv, of matching highlight colours that look pretty.

Examples

make.palettes(8)

Plot trajectories based on Temporal Check-All-That-Apply (TCATA) data

Description

Plot trajectories following PCA on multiblock TCATA proportions, or same for Temporal Dominance of Sensations (TDS) proportions.

Usage

plot_pca.trajectories(in.pca = in.pca, products.times = matrix(NA),
attributes = c(), type = "smooth", span = 0.75, biplot = "distance",
flip = c(FALSE, FALSE), dims = c(1, 2),
att.offset.x = c(), att.offset.y = c(), att.cex = 1, inflate.factor = NA,
xlab = "_auto_", ylab = "_auto_", xlim = NULL, ylim = NULL,
attributes.col = "red", attributes.pch = 17,
lwd = 1, traj.lab.loc = 0, traj.col = c(grDevices::grey(1/2)), traj.points = NA,
traj.col.seg = NA, traj.cex = 1, traj.lab = c(), traj.lab.cex = 1,
arrow.loc = NA, arrow.length = 0.1, arrow.col = NA, arrow.lwd = NA,
contrails = list(), main = "", save.format = "eps", save.as = "")

Arguments

in.pca

Any list object with components sdev, rotation, and x. Most often it is a prcomp object obtained from PCA on a matrix of proportions (or, if there is no missing data, on counts) with Product*Times in rows and Attributes in columns.

products.times

a 2-column matrix, with an ascending sort order on products (column 1) and a secondary ascending sort on times (column 2), corresponding to the rows of the matrix submitted to prcomp to obtain "in.pca".

attributes

a vector of attribute labels, corresponding to the attributes of the matrix submitted to prcomp to obtain "in.pca".

type

Determines how trajectories are drawn. Possible values are "smooth" (default) or "raw".

span

A tuning parameter used if smoothing trajectories using the loess function.

biplot

Controls the type of biplot displayed. Possible values are "distance" (plots trajectories based on scores, and attributes based on eigenvectors multiplied by the "inflation.factor"), or "correlation" (plots trajectories based on scores divided by the sqrt of their respective eigenvalues, and attributes based on eigenvectors multiplied by the sqrt of their respective eigenvalues).

flip

a vector of two logical values. Value indicates whether to mirror the coordinates in the x and y dimensions respectively. Default is c(FALSE, FALSE).

dims

a vector of two integers, specifying the principal componts to display. Defaults is "c(1, 2)", i.e. PC1 vs. PC2.

att.offset.x

A vector of numeric values corresponding to the labels in "attributes". Used to adjust the horizontal position of attribute labels to make the plot more readable.

att.offset.y

A vector of numeric values corresponding to the labels in "attributes". Used to adjust the vertical position of attribute labels to make the plot more readable.

att.cex

Attribute text size.

inflate.factor

Scalar controlling the position of attribute labels. If "NA" (default), then this scalar is set to the largest absolute score divided by the largest absolute eigenvector based on the dimensions used. Use "1" for no inflation. Applies only when biplot = "distance".

xlab

Label for x axis.

ylab

Label for y axis.

xlim

Permits control of the x limit. Limits can be specified using a vector of 2 (ascending) numbers. If a single number is provided then values are selected such that the limits are 20% beyond the smallest and largest x coordinates, respectively. If unspecified then control over x axis limits is given to the plot function in R.

ylim

Permits control of the x limit using the same logic as is used for "xlim".

attributes.col

Color used to display attribute labels (see "attributes").

attributes.pch

Symbol for attribute coordinates.

lwd

Trajectory line width.

traj.lab.loc

Indicates where along the trajectory the trajectory label will be positioned. "1" indicates the start of the trajectory. The value "0" (default) is a special convention indicating the end of the trajectory.

traj.col

A vector of colors for trajectories. If not specified then all trajectories are shown in grey.

traj.points

Specifies the position of markers along smoothed trajectories, and used to indicate the progression of time.

traj.col.seg

A vector of colors for segments along trajectories. If NA (default) then no segments along the trajectories appear in a color other than those specified by "traj.col". This parameter applies to smoothed trajectories only.

traj.cex

Used with "traj.points" for smoothed trajectories. Controls the size of symbol displayed.

traj.lab

A vector of character labels that identify the trajectories. If unspecified, then products are identified by ascending natural numbers.

traj.lab.cex

Text size of traj.lab.

arrow.loc

Trajectory arrows locations for direction marker(s).

arrow.length

Trajectory arrows length. See length parameter in arrows.

arrow.col

Trajectory arrows color. See col parameter in arrows.

arrow.lwd

Trajectory arrows line width. See lwd parameter in arrows.

contrails

list of data.frame objects with columns x, y, count, col; x and y are coordinates, count is the number of values at the coordinate, and col is the rbg colour.

main

plot title; see plot.

save.format

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

save.as

The filename. Must be provided if the file will be saved.

References

Castura, J.C., Baker, A.K., & Ross, C.F. (2016). Using contrails and animated sequences to visualize uncertainty in dynamic sensory profiles obtained from temporal check-all-that-apply (TCATA) data. Food Quality and Preference, 54, 90-100. doi:10.1016/j.foodqual.2016.06.011

Examples

# example using 'syrah' data set
syrah.pca <- prcomp(syrah[1:248, -c(1:4)], scale. = FALSE)
plot_pca.trajectories(syrah.pca, products.times = syrah[1:124, c(1, 4)],
                      attributes = colnames(syrah)[-c(1:4)], type = "raw")

# now with smoothing; may need to play with the span parameter to get appropriate smoothing
plot_pca.trajectories(syrah.pca, products.times = syrah[1:124, c(1, 4)],
                      attributes = colnames(syrah)[-c(1:4)], type = "smooth", span = 0.3)

# plots at each time point (trajectories join 2 points so start at timepoint 2, i.e., 11 s)
x <- 11:14 # for brevity show only the first 4 timeslices
# x <- 11:41 # uncomment this line to to run a longer demo
pca.list <- list()
for(i in seq_along(x)){
  pca.list[[x[i]-10]] <- syrah.pca
  pca.list[[x[i]-10]]$x <- pca.list[[x[i]-10]]$x[1:((x[i]-9)*6), ]
  plot_pca.trajectories(pca.list[[x[i]-10]], products.times = syrah[1:((x[i]-9)*6), c(1, 4)],
                        attributes = colnames(syrah)[-c(1:4)], type = "raw", inflate.factor = 1.5)
  Sys.sleep(3/4)
  # save plot if saving stills for a video; see Castura, Baker, & Ross (2016, Video 1)
}

Get a pretty palette of colours

Description

Create a vector of n pretty colours.

Usage

pretty_palette(n)

Arguments

n

number of colours in the palette

Value

cv A character vector, cv, of colours that look pretty.

Examples

pretty_palette(8)

Quantify TCATA assessor repeatability

Description

Quantify TCATA assessor repeatability using city block distance

Usage

similarity.tcata.repeatability(X)

Arguments

X

list of matrices, where each matrix is a TCATA data (given as an indicator matrix) for assessor of interest for one rep

Details

Similarity between repeated evaluations given by a TCATA assessor is quantified. The repeatability index can take on values between 0 and 1, which indicate complete dissimilarity (non-repeatability) and complete similarity (repeatability), respectively.

Value

repeatability.index average city block distance between matrices from replicated evaluations

References

Examples

  # Toy data from one TCATA assessor on a product over three sessions: rep1, rep2, rep3
  rep1 <- rbind(rep(0, 7),
                rep(0, 7),
                c(0, 0, 0, 1, 1, 1, 1),
                c(0, 0, 0, 1, 1, 1, 1),
                c(0, 0, 0, 1, 1, 1, 0))
  rep2 <- rbind(c(0, 0, 0, 1, 1, 1, 0),
                rep(0, 7),
                c(0, 1, 1, 1, 1, 1, 0),
                rep(1, 7),
                c(0, 0, 0, 1, 1, 1, 1))
  rep3 <- rbind(rep(0, 7),
                rep(0, 7),
                rep(1, 7),
                rep(1, 7),
                rep(1, 7))
  rep.data <- list(rep1, rep2, rep3)

  # Quantify similarity of assessor a1 to the other assessors
  similarity.tcata.repeatability(rep.data)

Quantify TCATA assessor replication

Description

Quantify TCATA assessor replication using city block distance

Usage

similarity.tcata.replication(this.assessor, other.assessors)

Arguments

this.assessor

TCATA data (given as an indicator matrix) for assessor of interest

other.assessors

TCATA data (given as an indicator matrix) for other assessors

Details

Similarity between one TCATA assessor and other assessors on the panel is quantified. The replication index can take on values between 0 and 1, which indicate complete dissimilarity (disagreement) and complete similarity (agreement), respectively.

Value

replication.index city block distance between this assessor and other assessors

References

Examples

  # Toy TCATA data for three assessors: a1, a2, a3
  a1 <- rbind(rep(0, 7),
              rep(0, 7),
              c(0, 0, 0, 1, 1, 1, 1),
              c(0, 0, 0, 1, 1, 1, 1),
              c(0, 0, 0, 1, 1, 1, 0))
  a2 <- rbind(c(0, 0, 0, 1, 1, 1, 0),
              rep(0, 7),
              c(0, 1, 1, 1, 1, 1, 0),
              rep(1, 7),
              c(0, 0, 0, 1, 1, 1, 1))
  a3 <- rbind(rep(0, 7),
              rep(0, 7),
              rep(1, 7),
              rep(1, 7),
              rep(1, 7))

  # Quantify similarity of assessor a1 to the other assessors
  similarity.tcata.replication(a1, rbind(a2, a3))

Time standardize results

Description

Set results for a temporal evaluation to a timescale by trimming off time prior to the first onset and following the last offset time, and express the remaining times in terms of percentiles [0, 100].

Usage

std.time(X, trim.left = TRUE, trim.right = TRUE, scale = TRUE, missing = 0)

Arguments

X

vector (or data frame) of indicator data.

trim.left

Trim on the left? Default is TRUE.

trim.right

Trim on the right? Default is TRUE.

scale

Set to a [0, 1] scale? Default is TRUE.

missing

indicator for missing data; default is 0.

Value

out vector (or data frame) of trimmed and/or standardized indicator (0/1) data

References

Castura, J.C. (2019). Investigating temporal sensory data via a graph theoretic approach. Food Quality and Preference, 79, 103787. doi:10.1016/j.foodqual.2019.103787

Lenfant, F., Loret, C., Pineau, N., Hartmann, C., & Martin, N. (2009). Perception of oral food breakdown. The concept of sensory trajectory. Appetite, 52, 659-667.

Examples

# vector - toy data example
x <- rep(c(rep(0,18), rep(1,18)), 2)
names(x) <- 1:72
x           # raw time
std.time(x) # standardized time

# data frame - toy data example
y <- data.frame(rbind(c(c(rep(0,18),
                           rep(1,18)),
                           rep(0, 4)),
                           c(rep(c(rep(0,9),
                           rep(1,9)), 2),
                           1, rep(0, 3)),
                           rep(0, 40)))
colnames(y) <- 1:40
y           # raw time
std.time(y) # standardized time

# time standardization using 'bars' data set
# only sample 1 will be done (for illustrative purposes)
eval1 <- unique(bars[bars$sample == 1, (1:3)])
bar1.std <- data.frame(unique(bars[bars$sample == 1, (1:4)]), matrix(0, ncol = 101))

for (e in 1:nrow(eval1)){
  bar1.std[bar1.std$assessor == eval1$assessor[e] &
             bar1.std$session == eval1$session[e] &
             bar1.std$sample == eval1$sample[e],
             -c(1:4)] <- std.time(bars[bars$assessor == eval1$assessor[e] &
                                         bars$session == eval1$session[e] &
                                         bars$sample == eval1$sample[e],
                                           -c(1:4)])
}
colnames(bar1.std)[5:ncol(bar1.std)] <- 0:100
head(bar1.std)

TCATA difference plot

Description

Plots TCATA difference curves.

Usage

tcata.diff.plot(x1 = x1, x2 = NA, n1 = 1, n2 = NA,
attributes = c(), times = c(), lwd = 1,
declutter = NA, get.decluttered = FALSE, emphasis = NA, alpha = 0.05, emphasis.lwd = 3,
main = "", height = 8, width = 12,
xlab = "Time", ylab = "Difference in citation proportion",
axes.font = 1, axes.cex = 1, line.col = c(), x.increment = 5,
legend.cex = 1, legend.font = 1, save.as = "")

Arguments

x1

matrix of difference proportions, or of counts if n1 specified. If mat2 specified then proportions or counts apply to first sample. Attributes are in rows, times in columns.

x2

matrix of proportions for second sample, or of counts if n2 specified.

n1

number of observations for first sample

n2

number of observations for second sample

attributes

vector of attribute labels for row in x1 (and x2)

times

vector of times for columns in x1 (and x2)

lwd

Line width

declutter

indicator matrix with same dimensions of x1 to suppress output

get.decluttered

if TRUE then calculates the declutter matrix from get.mat.diff.sign

emphasis

set to 1 to emphasize significant differences

alpha

significance level for entrywise test of x1 and x2 (if counts)

emphasis.lwd

line weight for emphasizing significant differences

main

plot title; see plot

height

plot height

width

plot width

xlab

label for x axis

ylab

label for y axis

axes.font

Font for axes labels; see par.

axes.cex

Size for axes labels.

line.col

line color for attribute lines

x.increment

increment between time labels on x axis

legend.cex

symbol size for legend

legend.font

Font for the legend; see text.

save.as

Filename to use if file will be saved.

References

Examples

# difference between High and Low ethanol wines (sip 1)
x.diff.raw <- t(syrah[seq(1, 1026, by = 6), -c(1:4)]) -
                t(syrah[seq(3, 1026, by = 6), -c(1:4)])
x.diff.smooth <- get.smooth(x.diff.raw, low.bound = -1, up.bound = 1)
colnames(x.diff.smooth) <- colnames(x.diff.raw) <- times <- 10:180
tcata.diff.plot(x1 = x.diff.smooth, attributes = rownames(x.diff.smooth), times = times, lwd = 2,
                main = "Sip 1 differences: High-ethanol wine - Low-ethanol wine")

# an example based on the syrah data set (truncated for efficiency)
n <- 52
H1 <- t(syrah[seq(1, 126, by = 6), -c(1:4)] * n)
L1 <- t(syrah[seq(3, 126, by = 6), -c(1:4)] * n)
colnames(H1) <- colnames(L1) <- times <- 10:30
tcata.diff.plot(x1 = H1, x2 = L1, n1 = n, n2 = n,
                attributes = rownames(H1), get.decluttered = TRUE, lwd = 2)

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

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")

Plot TDS difference curves

Description

Plots TDS difference curves based on differences in dominance counts or dominace rates.

Usage

tds.diff.plot(
  X,
  times = NULL,
  attributes = NULL,
  xlab = "Time (seconds)",
  ylab = "Dominance rate",
  line.col = 1,
  lty = 1,
  lwd = 1,
  main = ""
)

Arguments

X

matrix of differences in dominance rates (Attributes in rows, Times in columns).

times

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

attributes

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

xlab, ylab

Labels for the x and y axes; see plot

line.col

A vector of colors for lines corresponding to attributes; see par

lty, lwd

line type and weight for attributes; see par

main

plot title; see plot

Details

Currently the differences in dominance rates are always displayed. Suppression of differences in dominances rates within a threshold range is not yet implemented.

References

Examples

# example using 'bars' data set
bars.m <- aggregate(bars[, -c(1:4)], list(samples = bars$sample, attribute = bars$attribute), mean)
bars.m <- bars.m[order(bars.m$sample, bars.m$attribute), ]
attributes <- unique(bars$attribute)
times <- get.times(colnames(bars.m)[-c(1:2)])
bar1 <- bars.m[bars.m$sample == 1, -c(1:2)]
bar2 <- bars.m[bars.m$sample == 2, -c(1:2)]
diff.1vs2 <- get.smooth(bar1 - bar2, low.bound = -1, up.bound = 1)
tds.diff.plot(diff.1vs2, times = times, attributes = attributes,
                lwd = 2, main = "TDS Differences (Bar 1 - Bar 2)")

# suppose we only want to show the curves where the difference in dominance rate
# is significantly different
# get samples sizes and dominance counts for each product
bars.s <- aggregate(bars[, -c(1:4)], list(samples = bars$sample, attribute = bars$attribute), sum)
bars.s <- bars.s[order(bars.s$sample, bars.s$attribute), ]
bar1.s <- bars.s[bars.s$sample == 1, -c(1:2)]
bar2.s <- bars.s[bars.s$sample == 2, -c(1:2)]
bar1.n <- nrow(unique(bars[bars$sample == 1, 1:2]))
bar2.n <- nrow(unique(bars[bars$sample == 2, 1:2]))

# prop.test2 is a wrapper for prop.test (with its default parameters)
# thus it will run chi-squared test with Yates continuity correction
prop.test2 <-  function(x1, x2, n1, n2, alpha = 0.05){
  return((suppressWarnings(prop.test(c(x1,x2), c(n1, n2),
          alternative = "two.sided"))$p.value < alpha) %in% TRUE)
}
# find significant pairwise comparison, treating data as if independent
diff_1v2.signif <- mapply(prop.test2, unlist(bar1.s), unlist(bar2.s), bar1.n, bar2.n)
# update smoothed difference matrix with NA where non-significant pairs
diff_1v2.signif[!diff_1v2.signif] <- NA
diff.1vs2 <- diff.1vs2 + diff_1v2.signif - 1
# line segments that are non-significant are missing/NA so not plotted
tds.diff.plot(diff.1vs2, times = times, attributes = attributes,
              lwd = 2, main = "TDS Differences (Bar 1 - Bar 2)")

Plot TDS curves

Description

Plots TDS curves based on dominance rates, showing chance and significance lines.

Usage

tds.plot(X, attributes = NULL, times = NULL, chance = NULL, signif = NULL,
 line.col = 1, lty = 1, lwd = 1, las = 0, xlab = "Time (seconds)",
 ylab = "Dominance rate", main = "", height = 8, width = 12, box = FALSE, save.as = "")

Arguments

X

matrix of dominance rates (Attributes in rows, Times in columns)

attributes

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

times

a vector of times, corresponding to the times in X

chance

proportion indicating the chance level, usually 1/length(attributes) or 1/(1+length(attributes))

signif

significance level associated with the number of observations and chance

line.col

A vector of colors for lines corresponding to attributes; see par

lty, lwd

line type and weight for attributes; see par

las

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

xlab, ylab

Labels for the x and y axes; see plot

main

plot title; see plot

height

Window height

width

Window width

box

draw box around plot area; see: box

save.as

Filename if the file will be saved

References

Examples

# example using 'bars' data set
bars.m <- aggregate(bars[, -c(1:4)], list(sample = bars$sample, attribute = bars$attribute), mean)
bars.m <- bars.m[order(bars.m$sample, bars.m$attribute), ]
attributes <- as.character(bars.m$attribute[bars.m$sample == 1])
times <- get.times(colnames(bars.m)[-c(1:2)])
chance <- get.chance(attributes)
signif <- get.significance(chance, nrow(unique(bars[, 1:2])))
tds.plot(get.smooth(bars.m[bars.m$sample == 1, -c(1:2)]), attributes = attributes,
         times = times, chance = chance, signif = signif,
         lwd = 2, main = "Bar 1")

# it is possible to hide the portion of the plot below the significance line:
rect(-2, -0.2, times[length(times)]+2, signif, col = "white", border = "transparent")
# re-add axes & significance line
axis(1, labels = seq(0, 45, by = 5), at = seq(0, 45, by = 5))
axis(2)
abline(h=signif, lty=3, col = "grey")

tempR

Description

TCATA data set: Syrah wines

Description

Format

References

Examples

TCATA data set: orange juice

Description

Format

References

Examples

TDS data set: orange juice

Description

Format

References

Examples

TDS data set: snack bars

Description

Format

References

Examples

Adjust color brightness

Description

Usage

Arguments

Value

Examples

Get bootstrap confidence bands for attribute selections

Description

Usage

Arguments

Details

Value

Examples

Counts TCATA Citations and Observations for a Product and a Comparison Set

Description

Usage

Arguments

Value

References

See Also

Examples

Convert TCATA data

Description

Usage

Arguments

Value

Examples

Convert Temporal Category data

Description

Usage

Arguments

Value

Examples

Count attribute selections

Description

Usage

Arguments

Details

Value

Examples

Calculate city block distance between two matrices

Description

Usage

Arguments

Value

Examples

Draw h-cross, range box, and box to enclose h-box

Description

Usage

Arguments

Details

References

Fills gaps

Description

Usage

Arguments

Value

Examples