tds.diff.plot: Plot TDS difference curves

In tempR: Temporal Sensory Data Analysis

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

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.foodqual.2009.04.005")}

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)")

tempR documentation built on Sept. 8, 2023, 5:19 p.m.