Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/IdMapConsumer.r

Create the ideal map and plot the ideal areas of the categories of qualitative variables. And perform 2 tests: a global test in order to highlight the significance of the difference between ideals of all the categories of the same variable; a pair comparison test to highlight the significance between 2 categories of the same variable.

1 2 3 4 5 | ```
IdMapConsumer(dataset.id, dataset.signa, col.p, col.j, col.lik,
num.col.var.signa, conf.level=0.95, id.recogn, nbchoix = NULL,
nbsimul = 500, alpha = 0.05, coord = c(1, 2), precision = 0.1,
levels.contour = NULL, color = FALSE, simusigni = 500)
``` |

`dataset.id` |
A matrix with at least two qualitative variables ( |

`dataset.signa` |
a data frame with n rows (individuals) and p columns (categorical variables) |

`col.p` |
The position of the |

`col.j` |
The position of the |

`col.lik` |
The position of the |

`id.recogn` |
The sequence in the variable names which distinguish the ideal
variables from the sensory variables. This sequence should be fixed and unique. |

`num.col.var.signa` |
The position of the categorical variables in the dataframe |

`conf.level` |
Threshold used for the tests |

`nbchoix` |
The number of consumers forming a virtual panel, by default the number of panelists in the original panel |

`nbsimul` |
The number of simulations (corresponding to the number of virtual panels) used to compute the ellipses |

`alpha` |
The confidence level of the ellipses |

`coord` |
A length 2 vector specifying the components to plot |

`precision` |
The value defining the step when gridding the space |

`levels.contour` |
The levels (between 0 and 1) to consider for the colors on the surface plot. By default, they are set automatically based on the results |

`color` |
Boolean, define whether the map is in color or in black and white |

`simusigni` |
The number of simulations used to perform the global and the pair comparison test |

The IdMapConsumer, step by step:
Step 1: the classical IdMap is plotted with the method "ellipses"

Step 2: for each modality of the categorical variable, the optimum of the ideal area is calculated with the method "density"

Step 3: for each categorical variable given in *num.col.var.signa*, simulations are performed giving the p-value
for the global ant the pair comparison test.

Step 4: if the global test is significant for a variable, the ideal areas of its modalities are plotted on the IdMap

This function needs the KernSmooth package.

A list containing the following components:

`PCA` |
the results from the PCA used to create the sensory space |

`idmap` |
a list containing the results of the IdMap ( |

`ideal` |
a list containing the estimated profile of the ideal of reference (not available for the wIdMap) as well as the percentage of consumers concerned |

`coordobs` |
The coordinates of all the ideals of all the categories on the sensory space |

`test.global` |
The results for the global test for each variables (observed inertia, critical inertia, P-value) |

`test.paires` |
The results for the pair comparison test for each variables, between its ideal's categories(observed distance between two categories, critical distance, P-value) |

The three last components are provided only if the user choose "color = FALSE", else no test and no ideal map with categories' ideal are performed.

Melodie Sanchez, Sarah Sanchez, [email protected]

Worch, T., Le, S., Punter, P., & Pages, J. (2012).
Construction of an Ideal Map (IdMap) based on the ideal profiles obtained directly from consumers.
*Food Quality and Preference*, 26, 93-104.

1 2 3 4 5 6 7 8 9 10 11 | ```
## Not run:
###Load the two datasets
data(cream_id)
data(cream_signa)
###Run the analysis and test the ideals of the variables from 1 to 12
## for example with a confidence level of 90
res.idmap <- IdMapConsumer(cream_id, cream_signa, col.p=2, col.j=1, col.lik=29,
num.col.var.signa=c(1:12),conf.level=0.90,id.recogn="id_")
## End(Not run)
``` |

SensoMineR documentation built on Dec. 13, 2017, 9:04 a.m.

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