get.significance.diff: Get least significant differences for pairwise comparisons

Description Usage Arguments Details Value References Examples

Description

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

Usage

1
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

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. http://dx.doi.org/10.1016/j.foodqual.2009.04.005

Examples

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# 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


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