Description Usage Arguments Details Value Author(s) See Also Examples
Make a Triangle test for a set of products.
1 | triangle.test (design, answer, preference = NULL)
|
design |
a data.frame corresponding to the design use to make the Triangle test (typically the ouput of the function |
answer |
a vector of the answers of all the panelists; all the answer should be "X", "Y" or "Z" |
preference |
a vector of the preference of the panelists; all the answer should be "X", "Y" or "Z" (by default, there preference are not taken into account) |
Triangle test: panelists receive three coded samples. They are told that two of the sample are the same and one is different. Panelists are asked to identify the odd sample.
Returns a list of matrices. Each matrix give the reult for all the pair of products:
nb.comp |
a matrix with the number of comparisons done for each pair of products; |
nb.ident |
a matrix with the number of panelists who indicate the odd product for each pair of products; |
p.value |
a matrix with the p-value of the Triangle tests for each pair of products; |
nb.recognition |
estimation of the panelists who really perceived the difference between two product, for each pair of product; |
maxML |
Maximum Likelihood of the estimation of the number of panelists who really perceive the difference between the products; |
confusion |
estimation of the percentage of panelists who do not perceived the difference between two product, for each pair of product; |
minimum |
minimum of panelists who should detect the odd product to can say that panelists perceive the difference between the products, for each pair of products; |
preference |
number of times that product of row i is prefered that product in column j for the panelists who find the odd product. |
Fran<e7>ois Husson
triangle.pair.test
, triangle.design
1 2 3 4 | design = triangle.design(nbprod = 4, nbpanelist = 6, bypanelist = 3)
answer = c("X","Y","Y","X","Z","X","Y","X","Z",
"X","X","Z","X","Y","X","Z","X","Y")
triangle.test (design, answer)
|
Loading required package: FactoMineR
$nb.comp
1 2 3 4
1 0 3 3 3
2 3 0 3 3
3 3 3 0 3
4 3 3 3 0
$nb.ident
1 2 3 4
1 0 0 1 3
2 0 0 0 2
3 1 0 0 1
4 3 2 1 0
$p.value
1 2 3 4
1 1.00000000 1.0000000 0.7037037 0.03703704
2 1.00000000 1.0000000 1.0000000 0.25925926
3 0.70370370 1.0000000 1.0000000 0.70370370
4 0.03703704 0.2592593 0.7037037 1.00000000
$nb.recognition
1 2 3 4
1 0 0 0 2
2 0 0 0 1
3 0 0 0 0
4 2 1 0 0
$maxML
1 2 3 4
1 1.0000000 0.0000000 0.4444444 1.0000000
2 0.0000000 1.0000000 0.0000000 0.6666667
3 0.4444444 0.0000000 1.0000000 0.4444444
4 1.0000000 0.6666667 0.4444444 1.0000000
$confusion
1 2 3 4
1 1.0000000 1.0000000 1 0.3333333
2 1.0000000 1.0000000 1 0.6666667
3 1.0000000 1.0000000 1 1.0000000
4 0.3333333 0.6666667 1 1.0000000
$minimum
1 2 3 4
1 NA 3 3 3
2 3 NA 3 3
3 3 3 NA 3
4 3 3 3 NA
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