Description Usage Arguments Value Author(s) References See Also Examples
Constructs mixed effects models for each of the selected by a user attributes. By default the largest possible models (that contain all possible interactions in fixed and random parts) are fitted. The complexity of the fitted models can be changed. Non-significant random effects are eliminated (by default). The likelihood ratio test (LRT) is used for testing the random terms, F-type hypothesis test is used for testing the terms. The type of the model and the type of the analysis can be changed with the control
argument (see sensmixedControl()
)
1 2 3 4 | sensmixed(attributes, prod_effects, assessor,
replication = NULL, data, product_structure = 3,
error_structure ="ASS-REP", MAM = TRUE,
control = sensmixedControl())
|
attributes |
a vector with names of sensory attributes |
prod_effects |
a vector with the names of the variables related to the product |
replication |
a character with the name of the replication column in the data, if present |
assessor |
a characthe with the name of the column in the data that represents assessors |
data |
a data frame (data from sensory studies) |
product_structure |
numeric, takes values in in c(1, 2, 3). Specifies the complexity of the fixed part (product effects) of the mixed effects models for all attributes.
|
error_structure |
character, takes values in c("ONLY-ASS", "ASS-REP"). Specifies the complexity of the random part of the mixed effects models for all attributes.
|
MAM |
logical. if TRUE then mixed assessor models (MAM) are fitted for the selected attributes (see Brockhoff, P. B., Schlich, P., & Skovgaard, I. (2015)) |
control |
a list (of class |
FCHi |
matrix with Chi square values from LRT test and F values form F-type test for the selected attributes |
pvalue |
matrix withp-values for all effects for the selected attributes |
Alexandra Kuznetsova, Per Bruun Brockhoff, Rune Haubo Bojesen Christensen
Brockhoff, P. B., Schlich, P., & Skovgaard, I. (2015). Taking individual scaling differences into account by analyzing profile data with the mixed assessor model. Food Quality and Preference, 39 , 156-166.
Kuznetsova, A., Christensen, R. H., Bavay, C., & Brockhoff, P. B. (2015). Automated mixed ANOVA modeling of sensory and consumer data. Food Quality and Preference, 40, Part A, 31 38. URL: http://www.sciencedirect.com/science/ article/pii/S0950329314001724. doi:http://dx.doi.org/10.1016/j. foodqual.2014.08.004.
sensmixedControl, conjoint, SensMixedUI
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ## import SensMixed package
library(SensMixed)
## convert some variables to factors in TVbo
TVbo <- convertToFactors(TVbo, c("Assessor", "Repeat", "Picture"))
## run automated selection process
res <- sensmixed(c("Coloursaturation", "Colourbalance"),
prod_effects = c("TVset", "Picture"),
assessor="Assessor", data=TVbo, MAM=TRUE)
res
## run MAManalysis function
res_MAM <- sensmixed(c("Coloursaturation", "Colourbalance"),
prod_effects=c("TVset"), replication="Repeat",
assessor="Assessor", data=TVbo, control = list(MAM_balanced=TRUE))
## print is not yet implemented
## get anova part
res_MAM[[3]][,,1]
## compare with the general implementation
res <- sensmixed(c("Coloursaturation", "Colourbalance"),
prod_effects=c("TVset"),
assessor="Assessor", data=TVbo, MAM=TRUE,
control = list(reduce.random=FALSE))
res$fixed
## Not run:
res <- sensmixed(names(TVbo)[5:(ncol(TVbo) - 1)],
prod_effects=c("TVset", "Picture"),
assessor="Assessor",
data=TVbo)
plot F and Chi square values
plot(res)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.