SmoothMap: Smoothed version of External Preference Mapping by LOESS

In SensMap: Sensory and Consumer Data Mapping

Smoothed version of External Preference Mapping by LOESS

Description

a function that provides a smoothed version of external preference mapping by performing a locally weighted regression smoothing (LOESS) on the curves of preference level.

Usage

SmoothMap(Y,X,S,axis=c(1,2),
formula,dimredumethod=1,
predmodel=1,pred.na =FALSE,span=.5,
degree=2, graphpred=FALSE, drawmap=TRUE, dmap.loess=FALSE,
nbpoints=50)

Arguments

Y	a data frame in which each row represents a product and each column represents hedonic scores of a given consumer for the products.
X	a data frame in which each row represents a product and each column represents evaluations of a given sensory or physico-chemical attribute for the products.
S	a data frame crossing products and panelists evaluations of a given sensory or physico-chemical attribute for the product. It's required when choosing Canonical Analysis as dimension reduction method.
axis	a length 2 vector specifying the components of dimension reduction method to plot.
formula	a formula expression to be introduced. If predmodel=1, it corresponds to four polynomial regression models performed using lm : Quadratic model, the corresponding formula = "~I(F1*F1)+I(F2*F2)+F1*F2". Vector model, the formula = "~ F1 + F2". Circular model, the formula="~ F1 + F2 + (F1*F1 + F2*F2)". Elliptic model, the formula = "~I(F1*F1)+I(F2*F2)". If predmodel= 2, Generalized Additive models (GAM) structure performed using gam, proposed formula="~s(F1,k=3)+s(F2,k=3)". If predmodel= 3, Generalized Linear Models(GLM) structure performed using glm, proposed formula = "~I(F1*F1)+ I(F2*F2)+ F1*F2". If predmodel= 4, Bayes structure performed using MCMCregress, proposed formula="~I(F1*F1)+I(F2*F2)+F1*F2".
dimredumethod	1 is assignedd to Principal Component Analysis(PCA). If 2, Multiple Factor Analysis(MFA). If 3, Canonical Analysis(CA).
predmodel	the type of prediction model : If predmodel = 1 : Polynomial regression models. If predmodel = 2 : Generalized Additive models (GAM). If predmodel = 3 : Generalized Linear Models (GLM). If predmodel = 4 : Bayesian models.
pred.na	boolean, if TRUE, predictions outside the set of liking scores are removed (generally [0:10]), if FALSE, we keep all predictions in and outside the set of liking scores.
span	the parameter which controls the degree of smoothing by LOESS.
degree	the degree of the polynomials to be used, normally between 1 and 2.
graphpred	boolean, if TRUE, plot of smoothed surface prediction of all consumers is posted.
drawmap	boolean, if TRUE, smoothed version of extrenal preference mapping in 2 dimensions is posted.
dmap.loess	boolean, if TRUE, smoothed version of extrenal preference mapping is 3 dimensions is posted.
nbpoints	an integer. The number of points in th grid-point of the discretized plan of the map provided with the two first components. By default 50.

Value

z.loess object of class loess obtained from fitting a polynomial surface determined by the components of dimension reduction method as predictors and consumers predictions as explained variable.

m.loess object of class loess obtained from fitting a polynomial surface determined by the components of dimension reduction method as predictors and consumers preferences as explained variable.

plots of the smoothed prediction surface, smoothed external preference mapping in 2D and in 3D

Author(s)

Ibtihel Rebhi, Dhafer Malouche

See Also

PrefMap, StabMap, SensMapUI

Examples

data(hedo_biscuit)
 data(sens_biscuit)
# Make data compatible to the form of X, Y and S
 rownames(hedo_biscuit)=hedo_biscuit[,1]
 Y=hedo_biscuit[,-1]
 library(doBy)
 juge_senso_bisc<- summaryBy(. ~ produit + juge,
 data=sens_biscuit, FUN=c(mean),keep.names = TRUE,na.rm=TRUE)
 S=juge_senso_bisc[,-3]
 prod_bisc=summaryBy(. ~ produit, data=sens_biscuit,
 FUN=c(mean),keep.names = TRUE,na.rm=TRUE)
 rownames(prod_bisc)= prod_bisc[,1]
 X= prod_bisc[,-c(1:3)]
# Example of smoothed version of external preference mapping for quadratic
#  model.
# Smap= SmoothMap(Y,X,S, axis=c(1,2),
# formula="~I(F1*F1)+I(F2*F2)+F1*F2",
# dimredumethod=1,predmodel=1,span=.5,degree=2,
 #graphpred=FALSE, drawmap=TRUE, dmap.loess=FALSE)
# results of predictions by LOESS smoothing
#  Smap$z.loess
#  Smap$m.loess

SensMap documentation built on July 5, 2022, 1:09 a.m.