sensoTable.RdColors a flextable based on the result of a SensoMineR::decat according to 2 threshold levels.
sensoTable(
res.decat,
thres = 0.05,
thres2 = 0,
col.neg = "#ff7979",
col.neg2 = "#eb4d4b",
col.pos = "#7ed6df",
col.pos2 = "#22a6b3"
)| res.decat | result of a SensoMineR::decat |
|---|---|
| thres | the threshold under which cells are colored col.neg (or col.pos) if the tested coefficient is significantly lower (or higher) than the average |
| thres2 | the threshold under which cells are colored col.neg2 (or col.pos2); this threshold should be lower than thres |
| col.neg | the color used for thres when the tested coefficient is negative |
| col.neg2 | the color used for thres2 when the tested coefficient is negative |
| col.pos | the color used for thres when the tested coefficient is positive |
| col.pos2 | the color used for thres2 when the tested coefficient is positive |
Returns a formatted flextable
This function is useful to highlight elements which are significant, especially when there are many values to check
### Example 1
data("sensochoc")
# Use the decat function
resdecat <-SensoMineR::decat(sensochoc, formul="~Product+Panelist", firstvar = 5, graph = FALSE)
sensoTable(resdecat)
#> a flextable object.
#> col_keys: `row.names(adjmeantable)`, `CocoaF`, `Bitterness`, `CocoaA`, `Granular`, `Astringency`, `Acidity`, `Crunchy`, `Sticky`, `Melting`, `Sweetness`, `Vanilla`, `Caramel`, `MilkA`, `MilkF`
#> header has 1 row(s)
#> body has 6 row(s)
#> original dataset sample:
#> row.names(adjmeantable) CocoaF Bitterness CocoaA Granular Astringency
#> choc1 choc1 8.068966 7.068966 7.086207 3.448276 4.758621
#> choc2 choc2 6.913793 4.948276 6.551724 3.155172 3.155172
#> choc4 choc4 6.689655 5.189655 6.258621 3.551724 3.689655
#> choc5 choc5 6.793103 4.879310 6.793103 3.068966 3.103448
#> choc6 choc6 6.224138 4.189655 6.362069 3.172414 2.758621
#> Acidity Crunchy Sticky Melting Sweetness Vanilla Caramel MilkA
#> choc1 4.655172 5.965517 3.758621 4.741379 3.137931 1.103448 1.672414 3.586207
#> choc2 3.137931 7.706897 3.827586 4.327586 4.620690 1.810345 2.775862 4.000000
#> choc4 3.931034 6.103448 4.103448 4.379310 4.293103 2.120690 2.672414 4.103448
#> choc5 3.086207 6.637931 3.224138 4.741379 5.224138 1.793103 3.413793 4.172414
#> choc6 2.672414 7.327586 3.931034 4.206897 5.620690 1.913793 3.258621 4.568966
#> MilkF
#> choc1 1.568966
#> choc2 2.379310
#> choc4 2.586207
#> choc5 3.120690
#> choc6 3.362069
### Example 2
data("sensochoc")
resdecat2 <-SensoMineR::decat(sensochoc, formul="~Product+Panelist", firstvar = 5, graph = FALSE)
sensoTable(resdecat2,thres2=0.01) # Add a second level of significance
#> a flextable object.
#> col_keys: `row.names(adjmeantable)`, `CocoaF`, `Bitterness`, `CocoaA`, `Granular`, `Astringency`, `Acidity`, `Crunchy`, `Sticky`, `Melting`, `Sweetness`, `Vanilla`, `Caramel`, `MilkA`, `MilkF`
#> header has 1 row(s)
#> body has 6 row(s)
#> original dataset sample:
#> row.names(adjmeantable) CocoaF Bitterness CocoaA Granular Astringency
#> choc1 choc1 8.068966 7.068966 7.086207 3.448276 4.758621
#> choc2 choc2 6.913793 4.948276 6.551724 3.155172 3.155172
#> choc4 choc4 6.689655 5.189655 6.258621 3.551724 3.689655
#> choc5 choc5 6.793103 4.879310 6.793103 3.068966 3.103448
#> choc6 choc6 6.224138 4.189655 6.362069 3.172414 2.758621
#> Acidity Crunchy Sticky Melting Sweetness Vanilla Caramel MilkA
#> choc1 4.655172 5.965517 3.758621 4.741379 3.137931 1.103448 1.672414 3.586207
#> choc2 3.137931 7.706897 3.827586 4.327586 4.620690 1.810345 2.775862 4.000000
#> choc4 3.931034 6.103448 4.103448 4.379310 4.293103 2.120690 2.672414 4.103448
#> choc5 3.086207 6.637931 3.224138 4.741379 5.224138 1.793103 3.413793 4.172414
#> choc6 2.672414 7.327586 3.931034 4.206897 5.620690 1.913793 3.258621 4.568966
#> MilkF
#> choc1 1.568966
#> choc2 2.379310
#> choc4 2.586207
#> choc5 3.120690
#> choc6 3.362069