# Plot TDS difference curves Plots TDS difference curves based on differences in dominance counts or dominace rates. UTF-8 ```r 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. ## Examples ```r # 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)") ``` ## 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. tools:::Rd_expr_doi("10.1016/j.foodqual.2009.04.005")

tds.diff.plot function