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Package ‘SensoMineR’ May 5, 2009 Version 1.09 Date 2009-30-04 Title Sensory data analysis with R Author Francois Husson, Sebastien Le Maintainer Francois Husson <husson@agrocampus-ouest.fr> Depends FactoMineR Description an R package for analysing sensory data License GPL (>= 2) URL http://sensominer.free.fr, http://www.agrocampus-rennes.fr/math/SensoMineR Encoding latin1 Repository CRAN Date/Publication 2009-05-05 08:00:48 R topics documented: ardi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 averagetable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 barrow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 boxprod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 carto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 chocolates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 cocktail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 coltable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 compo.cocktail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 construct.axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 cpa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 decat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 fast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1 2 ardi graphinter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 hedo.cocktail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 hedochoc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 histprod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 indscal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 interact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 magicsort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 nappeplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 napping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 napping.don . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 napping.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 optimaldesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 paneliperf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 panellipse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 panellipse.session . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 panelmatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 panelperf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 perfume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 plot.fast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 plotpanelist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 pmfa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 print.fast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 scalebypanelist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 search.desc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 senso.cocktail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 sensochoc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 sensopanels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 triangle.design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 triangle.pair.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 triangle.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Index 56 ardi Automatic Research of DIvergences between scores Description Spot the most singular or particular data with respect to all descriptors and to two qualitative vari- ables and all their possible categories combinations. Computes the highest differences between all the categories of the variables product, panelist and all their possible combinations, with respect to a set of quantitative variables (the sensory descriptors). Usage ardi(donnee, col.p, col.j, firstvar, lastvar = ncol(donnee), nbval = 10, center = TRUE, scale = FALSE) barrow 5 Value Return a matrix of dimension (p,q), where p is the number of categories of the qualitative variable of interest (in most cases, p is the number of products) and q is the number of (sensory) descriptors. If "coeff" is assigned to the method parameter then the function averagetable returns the matrix of the adjusted means; if "mean" is assigned to the method parameter then the function averagetable returns the matrix of the means per category. Author(s) François Husson 〈François.Husson@agrocampus-rennes.fr〉 References P. Lea, T. Naes, M. Rodbotten. Analysis of variance for sensory data. H. Sahai, M. I. Ageel. The analysis of variance. See Also aov Examples data(chocolates) resaverage<-averagetable(sensochoc, formul = "~Product+Panelist", firstvar = 5) coltable(magicsort(resaverage), level.upper = 6,level.lower = 4, main.title = "Average by chocolate") res.pca = PCA(resaverage, scale.unit = TRUE) barrow Barplot per row with respect to a set of quantitative variables Description Returns as many barplots as there are rows in a matrix. The barplots are automatically generated for all the quantitative variables. Usage barrow(donnee, numr = 2, numc = 2, numchar = 8, color = "lightblue", title = NULL) 6 boxprod Arguments donnee a data frame of dimension (p,q), where p is the number of products and q is the number of sensory descriptors for instance numr the number of barplots to be displayed per row (by default 2) numc the number of barplots to be displayed per column (by default 2) numchar the number of character used to write the boxplot labels (by default 8) color the color of the barplots (by default "lightblue") title the title used in the graphs Details Missing values are ignored when forming barplots. Author(s) Sébastien Lê 〈Sebastien.Le@agrocampus-rennes.fr〉 References Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983) Graphical Methods for Data Analysis. Wadsworth & Brooks/Cole. See Also plot Examples data(chocolates) resdecat<-decat(sensochoc, formul = "~Product+Panelist", firstvar = 5, graph = FALSE) ## Not run: barrow(resdecat$tabT) barrow(resdecat$coeff, color = "orange") ## End(Not run) boxprod Boxplot per category with respect to a categorical variable and a set of quantitative variables Description Returns as many boxplots as there are categories for a given categorical variable of interest (in most cases, the product variable). The boxplots are automatically generated for all the quantitative variables (in our type of applications, variables are often sensory descriptors). boxprod 7 Usage boxprod(donnee, col.p, firstvar, lastvar = ncol(donnee), numr = 2, numc = 2) Arguments donnee a data frame col.p the position of the categorical variable of interest firstvar the position of the first endogenous variable lastvar the position of the last endogenous variable (by default the last column of donnee) numr the number of boxplots per row (by default 2) numc the number of boxplots per column (by default 2) Details Missing values are ignored when forming boxplots. Author(s) François Husson 〈François.Husson@agrocampus-rennes.fr〉 Sébastien Lê 〈Sebastien.Le@agrocampus-rennes.fr〉 References Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983) Graphical Methods for Data Analysis. Wadsworth & Brooks/Cole. See Also boxplotwhich does the computation, bxp for the plotting and more examples; and stripchart for an alternative (with small data sets). Examples data(chocolates) boxprod(sensochoc, col.p = 4, firstvar = 5, numr = 2, numc = 2) 10 cocktail Description The data used here refer to six varieties of chocolates sold in France. - For the sensory description: each chocolate was evaluated twice by 29 panelists according to 14 sensory descriptors; - For the hedonic data: each chocolate was evaluated on a structured scale from 0 to 10, by 222 consumers, according to their liking (0) or disliking (10); - For the sensory panels description: each chocolate was evaluated by 7 panels according to 14 sensory descriptors. Usage data(chocolates) Format There are three data frames: - sensochoc: a data frame with 348 rows and 19 columns: 5 qualitative variables (Panelist, Session, Form, Rank, Product) and 14 sensory descriptors; - hedochoc: a data frame with 6 rows and 222 columns: each row corresponds to a chocolate and each column to the hedonic scores given by one of the 222 consumers participating in the study; - sensopanels: a data frame with 6 rows and 98 columns: each row corresponds to a chocolate and each column to the mean over the panelists of a given panel according to a sensory descriptor. Source Département de mathématiques appliquées, Agrocampus Rennes Examples data(chocolates) decat(sensochoc, formul = "~Product+Panelist", firstvar = 5, graph = FALSE) cocktail Cocktail data Description The data used here refer to 16 cocktails. There are 3 files corresponding to the composition of the cocktails; the sensory description of the cocktails; the hedonic scores. - For the composition of the cocktails: The mango, banana, orange and lemon concentration are known; - For the sensory description: each cocktail was evaluated by 12 panelists according to 13 sensory descriptors (only the average of each cocktail are given). - For the hedonic data: each cocktail was evaluated on a structured scale from 0 to 10, by 100 consumers, according to their liking (0) or disliking (10). coltable 11 Usage data(cocktail) Format There are three data frames: - compo.cocktail: a data frame with 16 rows and 4 columns: the composition of each cocktail is given for the 4 ingredients; - senso.cocktail: a data frame with 16 rows and 13 columns: each cocktail was evaluated by 12 panelists according to 13 sensory descriptors; hedo.cocktail: a data frame with 16 rows and 100 columns: each cocktail was evaluated on a structured scale from 0 to 10, by 100 consumers, according to their liking (0) or disliking (10). Source Département de mathématiques appliquées, Agrocampus Rennes Examples data(cocktail) coltable Color the cells of a data frame according to 4 threshold levels Description Return a colored display of a data frame according to 4 threshold levels. Usage coltable(matrice, col.mat = matrice, nbrow = nrow(matrice), nbcol = ncol(matrice), level.lower = 0.05, col.lower = "mistyrose", level.upper = 1.96, col.upper = "lightblue", cex = 0,nbdec = 4, main.title = NULL, level.lower2 = -1e10, col.lower2 = "red", level.upper2 = 1e10, col.upper2 = "blue", novalue = FALSE) Arguments matrice a data frame (or a matrix) with only quantitative variables col.mat a data frame (or a matrix) from which the cells of the matrice data frame are colored; by default, col.mat=matrice nbrow the number of rows to be displayed (by default, nrow(matrice)) nbcol the number of columns to be displayed (by default, ncol(matrice)) level.lower the threshold below which cells are colored in col.lower col.lower the color used for level.lower 12 coltable level.upper the threshold above which cells are colored in col.upper col.upper the color used for level.upper cex cf. function par in the graphics package nbdec the number of decimal places displayed main.title title of the graph(s) level.lower2 the threshold below which cells are colored in col.lower2; this level should be less than level.lower col.lower2 the color used for level.lower2 level.upper2 the threshold above which cells are colored in col.upper2; this level should be greater than level.upper col.upper2 the color used for level.upper2 novalue boolean, if TRUE the values are not written Details This function is very useful especially when there are a lot of values to check. Author(s) François Husson, Sébastien Lê Examples ## Example 1 data(chocolates) resdecat<-decat(sensochoc, formul = "~Product+Panelist", firstvar = 5, graph = FALSE) resaverage<-averagetable(sensochoc, formul = "~Product+Panelist", firstvar = 5) resaverage.sort = resaverage[rownames(magicsort(resdecat$tabT)), colnames(magicsort(resdecat$tabT))] coltable(resaverage.sort, magicsort(resdecat$tabT), level.lower = -1.96, level.upper = 1.96, main.title = "Average by chocolate") ## Example 3 ## Not run: data(chocolates) resperf<-paneliperf(sensochoc, formul = "~Product+Panelist+Product:Panelist", formul.j = "~Product", col.j = 1, firstvar = 5, lastvar = 12, synthesis = FALSE, graph = FALSE) resperfprob<-magicsort(resperf$prob.ind, method = "median") coltable(resperfprob, level.lower = 0.05, level.upper = 1, main.title = "P-value of the F-test (by panelist)") resperfr2<-magicsort(resperf$r2.ind, method = "median", ascending = FALSE) coltable(resperfr2, level.lower = 0.00, level.upper = 0.85, cpa 15 References Escofier, B. and Pagès, J. (1990) Analyses factorielles simples et multiples: objectifs, méthodes et interprétation Dunod, Paris. 1–267. Escofier, B. and Pagès, J. (1994) Multiple factor analysis (AFMULT package). Computational Statistics and Data Analysis, 18, 121–140. See Also MFA Examples ## Example1: PCA data(chocolates) donnee <- cbind.data.frame(sensochoc[,c(1,4,5:18)]) axe <- construct.axes(donnee, scale.unit = TRUE) ## Example2: MFA (two groups of variables) data(chocolates) donnee <- cbind.data.frame(sensochoc[,c(1,4,5:18)]) axe <- construct.axes(donnee, group = c(6,8), name.group = c("A-F","T-S"),scale.unit = TRUE) cpa Consumers’ Preferences Analysis Description Performs preference mapping techniques based on multidimensional exploratory data analysis. This methodology is oriented towards consumers’ preferences; here consumers are pictured according only to their preferences. In this manner, the distance between two consumers is very natural and easy to interpret, and a clustering of the consumers is also very easy to obtain. Usage cpa(senso, hedo, coord=c(1,2), center = TRUE, scale = TRUE, nb.clusters = 0, scale.unit = FALSE, name.panelist = TRUE, col = terrain.colors(45)[1:41]) Arguments senso a data frame of dimension (p,k), where p is the number of products and k the number of sensory descriptors hedo a data frame of dimension (p,j), where p is the number of products and j the number of consumers or panelists coord a length 2 vector specifying the components to plot 16 cpa center boolean, if TRUE then data are mean centered scale boolean, if TRUE then data are scaled to unit variance nb.clusters number of clusters to use (by default, 0 and the optimal numer of clusters is calculated scale.unit boolean, if TRUE then PCA is made on scaled data name.panelist boolean, if TRUE then the name of the panelist is written col color palette Details This methodology is oriented towards consumers’ preferences; here, consumers are pictured accord- ing only to their preferences. In this manner, the distance between two consumers is very natural and easy to interpret, and a clustering of the consumers is also very easy to obtain using a classic hierarchical clustering procedure performed on Euclidian distances with the Ward’s minimum vari- ance criterion. The originality of the representation is that the characteristics of the products are also superimposed to the former picture. Value Return the following results: clusters the cluster number allocated to each consumer result the coordinates of the panelists, of the clusters, of the archetypes prod.clusters a list with as many elements as there are clusters; each element of the list gathers the specific products for its corresponding cluster des.clusters the correlation coefficients between the average hedonic scores per cluster and the sensory descriptors A dendogram which highlight the clustering, a correlation circle that displays the hedonic scores, a graph of the consumers such as two consumers are all the more close that they do like the same products, as many graphs as there are variables: for a given variable, each consumer is colored according to the coefficient of correlation based on his hedonic scores and the variable. Author(s) François Husson 〈François.Husson@agrocampus-rennes.fr〉 Sébastien Lê 〈Sebastien.Le@agrocampus-rennes.fr〉 References S. Lê, F. Husson, J. Pagès (2005). Another look at sensory data: how to "have your salmon and eat it, too!". 6th Pangborn sensory science symposium, August 7-11, 2005, Harrogate, UK. decat 17 Examples ## Not run: data(cocktail) res.cpa = cpa(cbind(compo.cocktail, senso.cocktail), hedo.cocktail) ## If you prefer a graph in black and white and with 3 clusters res.cpa = cpa(cbind(compo.cocktail, senso.cocktail), hedo.cocktail, name.panelist = TRUE, col = gray((50:1)/50), nb.clusters = 3) ## End(Not run) decat DEscription of CATegories Description This function is designed to point out the variables that are the most characteristic according to the set of products in its whole, and to each of the products in particular. This function is designed to test the main effect of a categorical variable (F-test) and the significance of its coefficients (T-test) for a set of endogenous variables and a given analysis of variance model. In most cases, the main effect is the product effect and the endogenous variables are the sensory descriptors. Usage decat(donnee, formul, firstvar, lastvar = length(colnames(donnee)), proba = 0.05, graph = TRUE, col.lower = "mistyrose", col.upper = "lightblue", nbrow = NULL, nbcol = NULL, random = TRUE) Arguments donnee a data frame made up of at least two qualitative variables (product, panelist) and a set of quantitative variables (sensory descriptors) formul the model that is to be tested firstvar the position of the first endogenous variable lastvar the position of the last endogenous variable (by default the last column of donnee) proba the significance threshold considered for the analyses of variance (by default 0.05) graph a boolean, if TRUE a barplot of the P-values associated with the F-test of the product effet is displayed col.lower the color used for ’level.lower’. Only useful if graph is TRUE col.upper the color used for ’upper.lower’. Only useful if graph is TRUE nbrow the number of rows to be displayed (by default, all the values are displayed). Only useful if graph is TRUE nbcol the number of columns to be displayed (by default, all the values are displayed). Only useful if graph is TRUE random boolean, effect should be possible as fixed or random (default as random) 20 graphinter Author(s) Marine Cadoret, Sébastien L\textasciicircume 〈sebastien.le@agrocampus-ouest.fr〉 References Cadoret, M., L\textasciicircume, S., Pagès, J. (2008) A novel Factorial Approach for analysing Sorting Task data. 9th Sensometrics meeting. St Catharines, Canada Examples ## Not run: data(perfume) ## Example of fast results res.fast<-fast(perfume) ## End(Not run) graphinter Graphical display of the interaction between two qualitative variables Description This function is designed to display the interaction between two qualitative variables, in most cases the product and the session variables. Usage graphinter(donnee, col.p, col.j, firstvar, lastvar=ncol(donnee), numr = 2,numc = 2) Arguments donnee a data frame made up of at least two qualitative variables (product, panelist) and a set of quantitative variables (sensory descriptors) col.p the position of one categorical variables of interest (the product variable) col.j the position of one categorical variables of interest (the session variable) firstvar the position of the first endogenous variable lastvar the position of the last endogenous variable (by default the last column of donnee) numr the number of graphs per row (by default 2) numc the number of graphs per column (by default 2) Details The data set must be balanced (or not unbalanced too much). hedo.cocktail 21 Value If the variables of interest are the product and the session variables, a list containing the following components: prod a data frame of dimension (p,q), the means over the panelists and the sessions for the p products and the q sensory descriptors seance as many matrices of dimension (p,q) as there are sessions, the means over the panelists for the p products, the q sensory descriptors and for each session The graphical display of the interaction for each sensory descriptor. Author(s) François Husson, Sébastien Lê References P. Lea, T. Naes, M. Rodbotten. Analysis of variance for sensory data. H. Sahai, M. I. Ageel. The analysis of variance. See Also aov Examples ## Not run: data(chocolates) graphinter(sensochoc, col.p = 4, col.j = 2, firstvar = 5, lastvar = 12, numr = 1, numc = 1) ## End(Not run) hedo.cocktail Cocktails hedonic scores Description The data used here refer to 16 cocktails. Each cocktail was evaluated on a structured scale from 0 to 10, by 100 consumers, according to their liking (0) or disliking (10). Usage data(cocktail) Format A data frame with 16 rows and 100 columns: each row corresponds to a cocktail and each column to the hedonic scores given by one of the 100 consumers participating in the study. 22 histprod Source Département de mathématiques appliquées, Agrocampus Rennes Examples data(cocktail) hedochoc Chocolates hedonic scores Description The data used here refer to six varieties of chocolates sold in France. Each chocolate was evaluated on a structured scale from 0 to 10, by 222 consumers, according to their liking (0) or disliking (10). Usage data(chocolates) Format A data frame with 6 rows and 222 columns: each row corresponds to a chocolate and each column to the hedonic scores given by one of the 222 consumers participating in the study. Source Département de mathématiques appliquées, Agrocampus Rennes Examples data(chocolates) histprod Histogram for each descriptor Description Computes automatically histograms for a set of quantitative variables. Usage histprod(donnee, firstvar, lastvar = ncol(donnee), numr = 2, numc = 2, adjust = 1) interact 25 interact Estimation of interaction coefficients Description Computes automatically the interaction coefficients between two quantitative variables col.p and col.j for the following model: "~col.p+col.j+col.p:col.j". Usage interact(donnee, col.p, col.j, firstvar, lastvar = ncol(donnee)) Arguments donnee a data frame made up of at least two qualitative variables (product, panelist) and a set of quantitative variables (sensory descriptors) col.p the position of the product effect for instance col.j the position of the panelist effect for instance firstvar the position of the first endogenous variable lastvar the position of the last endogenous variable (by default the last column of donnee) Details In most cases col.p represents the product effect, col.j represents the panelist effect, and the variables of interest are the sensory descriptors. The model considered is the following one: "~Product+Panelist+Product:Panelist". Data must be complete (but not necessarily balanced). Value Returns an array of dimension (p,j,k), where p is the number of products, j the number of panelists and k the number of sensory descriptors. The entries of this array are the interaction coefficients between a panelist and a product for a given descriptor. For each sensory descriptor, returns a graph where each (panelist,product) interaction coefficient is displayed, a graph where the contribution to the (panelist,product) interaction coefficient by prod- uct is displayed, a graph where the contribution to the (panelist,product) interaction coefficient by panelist is displayed. Author(s) François Husson See Also aov 26 magicsort Examples ## Not run: data(chocolates) resinteract=interact(sensochoc, col.p = 4, col.j = 1, firstvar = 5) ## End(Not run) magicsort Returns a sorted data matrix Description Sort the rows and columns of a matrix in a "magic" order or by ascending (or descending) mean or median or geometrical mean. Usage magicsort(matrice, sort.mat = matrice, method = "magic", byrow = TRUE, bycol = TRUE, ascending = TRUE) Arguments matrice a data matrix to sort sort.mat sort the rows and columns according to the result of the PCA made on this matrix (by default the matrice) method four types of calculations, magic ("magic"), ("median"), arithmetical ("mean") or geometrical ("geo") mean (by default magic) byrow boolean, if TRUE then data are sorted over the rows bycol boolean, if TRUE then data are sorted over the columns ascending boolean, if TRUE then data are sorted in ascending order Details Very useful function to compare results. Author(s) François Husson, Sébastien Lê Examples ## Example 1 data(chocolates) resdecat<-decat(sensochoc, formul = "~Product", firstvar = 5, graph = FALSE) coltable(magicsort(resdecat$tabT), level.lower = -1.96, level.upper = 1.96, main.title = "Products' description") nappeplot 27 ## Example 2 data(chocolates) resperf<-paneliperf(sensochoc, formul = "~Product+Panelist+Product:Panelist", formul.j = "~Product", col.j = 1, firstvar = 5, lastvar = 12, synthesis = FALSE, graph = FALSE) res.sort=magicsort(resperf$prob.ind, method = "median") coltable(res.sort, main.title = "P-values of the F-test by panelist") nappeplot Plot panelists’ tableclothe Description Plot panelists’ tableclothe. Usage nappeplot(donnee, numr = 2, numc = 2, color = "blue", lim = c(60,40)) Arguments donnee a data frame of dimension (p,2j), where p represents the number of products and j the number of panelists numr the number of tableclothe per row (by default 2) numc the number of tableclothe per column (by default 2) color the color used to display the products lim the size of the tableclothe Details The data used here refer to a specific experiment, where panelists are asked to position products on a tableclothe of dimension lim, by default (60,40). Value Returns as many graphs as there are panelists, each graph represents products positioned by a given panelist on a tablecloth Author(s) François Husson References Pagès J. (2005). Collection and analysis of perceived product inter-distances using multiple factor analysis; application to the study of ten white wines from the Loire Valley. Food Quality and Preference. 16 (7) pp. 642-649. 30 optimaldesign Source Département de mathématiques appliquées, Agrocampus Rennes Examples ## Not run: data(napping) nappeplot(napping.don) x11() pmfa(napping.don, napping.words) ## End(Not run) optimaldesign Construction of an optimal design Description Construction of an optimal design balanced for first order of carry-over effect. Usage optimaldesign(nbPanelist, nbProd, nbProdByPanelist, nbPanelistMin = nbPanelist, ordre = TRUE, weight = 0.5, graine = Sys.time(), nbDesignProd = 10, nbDesignOrdre = 50, matEssImp = NA ) Arguments nbPanelist Maximum number of panelists nbProd Number of products nbProdByPanelist Number of products that each panelist will evaluate nbPanelistMin Minimum number of panelists who will evaluate the products ordre Boolean, if TRUE the order of presentation of the product to the panelist is given weight Importance of the rank and of the carry-over effect. From 0 to 1, if 0 the design will only take into account the carry-over effect (and not the rank effect). graine initialization of the algorithm nbDesignProd Number of iteration of the algorithm to affect the products to the panelists nbDesignOrdre Number of iteration of the algorithm for the rank of presentation matEssImp Matrix of the imposed experiments Value List with design Design with the products evaluated by each panelist rank Design with the products evaluated by each panelist and with the rank paneliperf 31 Author(s) E. Périnel, O. Tran, J. Mazet References Périnel E. & Pagès J. (2003). Optimal nested cross-over designs in sensory analysis. Food Quality and Preference. 15 (5). pp. 439-446. Examples ## Not run: optimaldesign(nbPanelist=10,nbPanelistMin=8,nbProd=5,nbProdByPanelist=3) ## End(Not run) paneliperf Panelists’ performance according to their capabilities to dicriminate between products Description Computes automatically P-values, Vtests, residuals, r-square for each category of a given qualitative variable (e.g. the panelist variable); Computes he agreement between each panelist and the panel results; Gives the panel results (optional). Usage paneliperf(donnee, formul, formul.j = "~Product", col.j, firstvar, lastvar = ncol(donnee), synthesis = FALSE, random = TRUE, graph = FALSE) Arguments donnee a data frame made up of at least two qualitative variables (product, panelist) and a set of quantitative variables (sensory descriptors) formul the aov model used for the panel formul.j the aov model used for each panelist (no panelist effect allowed) col.j the position of the panelist variable firstvar the position of the first endogenous variable lastvar the position of the last endogenous variable (by default the last column of donnee synthesis boolean, the possibility to have the anova results for the panel model random boolean, the status of the Panelist variable in the anova model for the panel graph boolean, draws the PCA and MFA graphs 32 paneliperf Details The formul parameter must be filled in by an analysis of variance model and must begin with the categorical variable of interest (e.g. the product effect) followed by the different other factors of interest (and their combinations). E.g.:formul = "~Product+Session". Value A list containing the following components: prob.ind a matrix which rows are the panelist, which columns are the endogenous vari- ables (in most cases the sensory descriptors) and which entries are the P-values associated to the AOV model vtest.ind a matrix which rows are the panelist, which columns are the endogenous vari- ables (in most cases the sensory descriptors) and which entries are the Vtests associated to the AOV model res.ind a matrix which rows are the panelist, which columns are the endogenous vari- ables (in most cases the sensory descriptors) and which entries are the residuals associated to the AOV model r2.ind a matrix which rows are the panelist, which columns are the endogenous vari- ables (in most cases the sensory descriptors) and which entries are the R-square associated to the AOV model signif.ind a vector with the number of significant descriptors per panelist agree.ind a matrix with as many rows as there are panelists and as many columns as there are descriptors and the entries of this matrix are the correlation coefficients be- tween the product coefficients for the panel and for the panelists complete a matrix with the v-test corresponding to the p.value (see p.values below), the median of the agreement (see agree upper), the standard deviation of the panel anova model (see res below) p.value a matrix of dimension (k,m) of P-values associated with the F-test for the k descriptors and the m factors and their combinations considered in the analysis of variance model of interest variability a matrix of dimension (k,m) where the entries correspond to the percentages of variability due to the effects introduced in the analysis of variance model of interest res a vector of dimension k of residual terms for the analysis of variance model of interest r2 a vector of dimension k of r-squared for the analysis of variance model of interest The usual graphs when MFA is performed on the data.frame resulting from vtest.ind and agree.ind. The PCA graphs for the complete output. Author(s) François Husson, Sébastien Lê panellipse 35 Details Panellipse, step by step: Step 1 Performs a selection of discriminating descriptors with respect to a threshold set by users Step 2 Virtual panels are generated using Boostrap techniques; the number of panels as well as their size are set by users with the nbsimul and nbchoix parameters Step 3 Coordinates of the products with respect to each virtual panels are computed Step 4 Each product is then circled by its confidence ellipse generated by virtual panels and com- prising (1-alpha)*100 percent of the virtual products Step 5 Variability of the variables is drawn and confidence interval of the correlation coefficient between descriptors are calculated by bootstrap Value A list containing the following elements: eig a matrix with the component of the factor analysis (in row) and the eigenvalues, the inertia and the cumulative inertia for each component coordinates a list with: the coordinates of the products with respect to the panel and to each panelists and the coordinates of the partial products with respect to the panel and to each panelists hotelling Returns a matrix with the P-values of the Hotelling’s T2 tests for each pair of products: this matrix allows to find the product which are significatnly different for the 2-components sensory description; if an MFA is done, hotelling returns as many matrices as there are group, these matrices allows to find the product which are significantly different for the 2-components sensory description of the group, and it returns also a global matrix corresponding to the P-values for the tests corresponding to the mean product. Returns a graph of the products as well as a correlation circle of the descriptors. Returns a graph where each product is displayed with respect to a panel and to each panelist com- posing the panel; products described by the panel are displayed as square, they are displayed as circle when they are described by each panelist. Returns a graph where each product is circled by its confidence ellipse generated by virtual panels. When a Multiple Factor Analysis is performed, returns a graph where each partial product is circled by its confidence ellipse generated by virtual panels. Returns a graph where the variability of each variable is drawn on the correlation circle graph. Author(s) François Husson 36 panellipse.session References Husson F., Le Dien S. & Pagès J. (2005). Confidence ellipse for the sensory profiles obtained by Principal Components Analysis. Food Quality and Preference. 16 (3), 245-250. Pagès J. & Husson F. (2005). Multiple Factor Analysis with confidence ellipses: a methodology to study the relationships between sensory and instrumental data. To be published in Journal of Chemometrics. Husson F., Lê S. & Pagès J. Variability of the representation of the variables resulting from PCA in the case of a conventional sensory profile. Food Quality and Preference. 16 (3), 245-250. See Also panellipse.session, panelmatch Examples ## Not run: ## Example 1: PCA data(chocolates) res <- panellipse(sensochoc, col.p = 4, col.j = 1, firstvar = 5) coltable(res$hotelling, main.title = "P-values for the Hotelling's T2 tests") ## If we consider only 12 panelists in a virtual panel, ## what would be the size of the ellipses res2 <- panellipse(sensochoc, col.p = 4, col.j = 1, nbchoix = 12, firstvar = 5) coltable(res2$hotelling, main.title = "P-values for the Hotelling's T2 tests") ## If we want the confidence ellipses around the individual descriptions panellipse(sensochoc, col.p = 4, col.j = 1, nbchoix = 1, firstvar = 5) ## Example 2: MFA data(chocolates) res <- panellipse(sensochoc, col.p = 4, col.j = 1, firstvar = 5, group = c(6,8), name.group = c("G1","G2")) for (i in 1:dim(res$hotelling$bygroup)[3]) coltable(res$hotelling$bygroup[,,i], main.title = paste("P-values for the Hotelling's T2 tests (", dimnames(res$hotelling$bygroup)[3][[1]][i],")",sep="")) ## End(Not run) panellipse.session Repetability of panelists descriptions studied by confidence ellipses around products per session Description Virtual panels are generated using Boostrap techniques in order to display confidence ellipses around products. panellipse.session 37 Usage panellipse.session(donnee, col.p, col.j, col.s, firstvar, lastvar = ncol(donnee), alpha = 0.05, coord = c(1,2), scale.unit = TRUE, nbsimul = 500, nbchoix = NULL, level.search.desc = 0.2, centerbypanelist = TRUE, scalebypanelist = FALSE, name.panelist = FALSE, variability.variable = FALSE, cex = 1, color= NULL) Arguments donnee a data frame made up of at least two qualitative variables (product, panelist) and a set of quantitative variables (sensory descriptors) col.p the position of the product variable col.j the position of the panelist variable col.s the position of the session variable firstvar the position of the first sensory descriptor lastvar the position of the last sensory descriptor (by default the last column of donnee) alpha the confidence level of the ellipses coord a length 2 vector specifying the components to plot scale.unit boolean, if T the descriptors are scaled to unit variance nbsimul the number of simulations (corresponding to the number of virtual panels) used to compute the ellipses nbchoix the number of panelists forming a virtual panel, by default the number of pan- elists in the original panel level.search.desc the threshold above which a descriptor is not considered as discriminant accord- ing to AOV model "descriptor=Product+Panelist" centerbypanelist boolean, if T center the data by panelist before the construction of the axes scalebypanelist boolean, if T scale the data by panelist before the construction of the axes (by default, FALSE is assigned to that parameter) name.panelist boolean, if T then the name of each panelist is displayed on the plotpanelist graph (by default, FALSE is assigned to that parameter) variability.variable boolean, if T a plot with the variability of the variable is drawn and a confidence intervals of the correlations between descriptors are calculated cex cf. function par in the graphics package color a vector with the colors used; by default there are 35 colors defined 40 panelmatch coord a length 2 vector specifying the components to plot scale.unit boolean, if T the descriptors are scaled to unit variance nbsimul the number of simulations (corresponding to the number of virtual panels) used to compute the ellipses nbchoix the number of panelists forming a virtual panel, by default the number of pan- elists in the original panel centerbypanelist boolean, if T center the data by panelist before the construction of the axes scalebypanelist boolean, if T scale the data by panelist before the construction of the axes (by default, FALSE is assigned to that parameter) name.panelist boolean, if T then the name of each panelist is displayed on the plotpanelist graph (by default, FALSE is assigned to that parameter) cex cf. function par in the graphics package color a vector with the colors used; by default there are 35 colors defined hierar hierarchy in the variable (see hmfa) Value A list containing the following elements: eig a matrix with the component of the factor analysis (in row) and the eigenvalues, the inertia and the cumulative inertia for each component coordinates a list with: the coordinates of the products with respect to the panel and to each panelists and the coordinates of the partial products with respect to the panel and to each panelists hotelling Returns a matrix with the P-values of the Hotelling’s T2 tests for each pair of products: this matrix allows to find the product which are significatnly different for the 2-components sensory description Returns a graph of the products as well as a correlation circle of the descriptors. Returns a graph where each product is displayed with respect to a panel and to each panelist com- posing the panel; products described by the panel are displayed as square, they are displayed as circle when they are described by each panelist. Returns a graph where each product is circled by its confidence ellipse generated by virtual panels. When a Multiple Factor Analysis is performed, returns a graph where each partial product is circled by its confidence ellipse generated by virtual panels. Author(s) François Husson panelperf 41 References Husson F., Le Dien S. & Pagès J. (2005). Confidence ellipse for the sensory profiles obtained by Principal Components Analysis. Food Quality and Preference. 16 (3), 245-250. Pagès J. & Husson F. (2005). Multiple Factor Analysis with confidence ellipses: a methodology to study the relationships between sensory and instrumental data. To be published in Journal of Chemometrics. See Also panellipse, panellipse.session Examples ## Not run: data(chocolates) Panel1= sensochoc[sensochoc[,1]<11,] Panel2= sensochoc[(sensochoc[,1]<21)&(sensochoc[,1]>10),] Panel3= sensochoc[sensochoc[,1]>20,] res <- panelmatch(list(P1=Panel1,P2=Panel2,P3=Panel3), col.p = 4, col.j = 1, firstvar = 5) ## End(Not run) panelperf Panel’s performance according to its capabilities to dicriminate be- tween products Description Computes automatically P-values associated with the F-test as well as the residual term for a given analysis of variance model. Usage panelperf(donnee, formul, subset = NULL, firstvar, lastvar = ncol(donnee), random = TRUE) Arguments donnee a data frame formul the model that is to be tested subset cf. function lm in the stats package firstvar the position of the first endogenous variable lastvar the position of the last endogenous variable (by default the last column of donnee random boolean, effect should be possible as fixed or random (default as random) 42 panelperf Details The formul parameter must be filled in by an analysis of variance model and must begin with the categorical variable of interest (e.g. the product effect) followed by the different other factors of interest (and their combinations). E.g.:formul = "~Product+Session". Value A list containing the following components: p.value a matrix of dimension (k,m) of P-values associated with the F-test for the k descriptors and the m factors and their combinations considered in the analysis of variance model of interest variability a matrix of dimension (k,m) where the entries correspond to the percentages of variability due to the effects introduced in the analysis of variance model of interest res a vector of dimension k of residual terms for the analysis of variance model of interest r2 a vector of dimension k of r-squared for the analysis of variance model of interest Author(s) François Husson, Sébastien Lê References P. Lea, T. Naes, M. Rodbotten. Analysis of variance for sensory data. H. Sahai, M. I. Ageel. The analysis of variance. See Also paneliperf, aov Examples data(chocolates) res=panelperf(sensochoc, firstvar = 5, formul = "~Product+Panelist+ Session+Product:Panelist+Session:Product+Panelist:Session") ## Sort results by product p.values. coltable(magicsort(res$p.value, sort.mat = res$p.value[,1], bycol = FALSE, method = "median"), main.title = "Panel performance (sorted by product P-value)") plotpanelist 45 Examples ## Not run: data(perfume) res.fast <- fast(perfume,graph=FALSE) plot.fast(res.fast,choix="ind",invisible="var",habillage=5) plot.fast(res.fast,choix="group") ## End(Not run) plotpanelist Plotpanelist Description Displays panelists’ sensory profiles onto the products’ space Usage plotpanelist(mat, coord = c(1,2), name = FALSE, eig, cex = 1, color = NULL) Arguments mat a data frame structured as the first element of the list resulting from the function construct.axes, i.e. the coordinates of the products with respect to the panel and to each panelists coord a length 2 vector specifying the components to plot name boolean, if T then the name of each panelist is displayed on the graph (by default, FALSE is assigned to that parameter) eig a matrix with the component of the factor analysis (in row) and the eigenvalues, the inertia and the cumulative inertia for each component. Typically, the eig output of the construct.axes function cex cf. function par in the graphics package color a vector with the colors used; by default there are 35 colors defined Value Returns a graph where each product is displayed with respect to a panel and to each panelist com- posing the panel. Products described by the panel are displayed as square, they are displayed as circle when they are described by each panelist. Author(s) François Husson 46 pmfa Examples data(chocolates) donnee <- cbind.data.frame(sensochoc[,c(1,4,5:18)]) axe <- construct.axes(donnee, scale.unit = TRUE) plotpanelist(axe$moyen, eig = signif(axe$eig,4)) pmfa Procrustean Multiple Factor Analysis (PMFA) Description Performs Multiple Factor Analysis combined with Procrustean Analysis. Usage pmfa(matrice, matrice.illu = NULL, mean.conf = NULL, dilat = TRUE, graph.ind = TRUE, graph.mfa = TRUE, lim = c(60,40), coord = c(1,2), cex = 0.8) Arguments matrice a data frame of dimension (p,2j), where p represents the number of products and j the number of panelists matrice.illu a data frame with illustrative variables (with the same row.names in common as in matrice) mean.conf coordinates of the average configuration (by default NULL, the average config- uration is generated by MFA) dilat boolean, if TRUE (which is the default value) the Morand’s dilatation is used graph.ind boolean, if TRUE (which is the default value) superimposes each panelist’s con- figuration on the average configuration graph.mfa boolean, if TRUE (which is the default value) and if mean.conf = NULL the graphs of the MFA are drawn lim size of the tablecothe coord a length 2 vector specifying the components to plot cex cf. function par in the graphics package Details Performs first Multiple Factor Analysis on the tableclothes, then GPA in order to superimpose as well as possible panelist’s configuration on the average configuration obtained by MFA (in the case where mean.conf is NULL). If mean.conf is not NULL the configuration used is the one input by the user. print.fast 47 Value Returns the RV coefficient between each individual configuration and the consensus. If mean.conf is NULL (and graph.mfa is TRUE), returns the usual graphs resulting from the MFA function: the graph of the individuals and their partial representations, the graph of the variables (i.e. the coordinates of the products given by each panelist). If mean.conf is not NULL returns the configuration input by the user. When matrice.illu is not NULL, returns a graph of illustrative variables. Returns as many superimposed representations of individual configurations as there are panelists. Author(s) François Husson, Sébastien Lê References Morand, E., Pagès, J. Procrustes multiple factor analysis to analyze the overall perception of food products. Food Quality and Preference 14, 182-188. See Also MFA, nappeplot, indscal Examples ## Not run: data(napping) nappeplot(napping.don) x11() pmfa(napping.don, napping.words) ## End(Not run) print.fast Print Factorial Approach for Sorting Task data (FAST) results Description Print Factorial Approach for Sorting Task data (FAST) results. Usage print.fast(x, file = NULL, sep = ";", ...) 50 senso.cocktail Value Returns a data frame with all the qualitative variables and only discriminating variables Author(s) François Husson Examples data(chocolates) ## In this example, all the descriptos are discriminated interesting.desc <- search.desc(sensochoc, col.j = 1, col.p = 4, firstvar = 5, level = 0.5) senso.cocktail Sensory data for 16 cocktails Description The data used here refer to the sensory description of 16 cocktails. Each cocktail was evaluated by 12 panelists according to 13 sensory descriptors (only the average of each cocktail are given). Usage data(cocktail) Format A data frame with 16 rows and 13 columns: each cocktail was evaluated by 12 panelists according to 13 sensory descriptors. Source Département de mathématiques appliquées, Agrocampus Rennes Examples data(cocktail) sensochoc 51 sensochoc Sensory data for 6 chocolates Description The data used here refer to the sensory description of six varieties of chocolates sold in France: each chocolate was evaluated twice by 29 panelists according to 14 sensory descriptors. Usage data(chocolates) Format A data frame with 348 rows and 19 columns: 5 qualitative variables (Panelist, Session, Form, Rank, Product) and 14 sensory descriptors. Source Département de mathématiques appliquées, Agrocampus Rennes Examples data(chocolates) decat(sensochoc, formul = "~Product+Panelist", firstvar = 5, graph = FALSE) sensopanels Sensory profiles given by 7 panels Description The data used here refer to six varieties of chocolates sold in France. Each chocolate was evaluated by 7 panels according to 14 sensory descriptors. Usage data(chocolates) Format A data frame with 6 rows and 98 columns: each row corresponds to a chocolate and each column to the mean over the panelists of a given panel according to a sensory descriptor. Source Département de mathématiques appliquées, Agrocampus Rennes 52 triangle.design Examples data(chocolates) triangle.design Construct a design for triangle tests Description Construct a design to make triangle tests. Usage triangle.design (nbprod , nbpanelist, bypanelist = nbprod*(nbprod-1)/2, labprod=1:nbprod, labpanelist=1:nbpanelist) Arguments nbprod number of products to compare nbpanelist number of panelists who make the triangle test bypanelist number of expermient that each panelist can done (by default each panelist make all the comparisons between the products labprod name of the products (by default, the product are coded from 1 to the number of products labpanelist name of the panelists (by default, the panelists are coded from 1 to the number of panelists Details 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. Value Returns an data.frame of dimension (t,3), where t is the number of experiments. In column 1, 2 and 3 the product to test are given. The product in column 1 is by coded "X", in column 2 is coded by "Y" and in column 3 is coded by "Z". Panelist should start by product "X", then "Y" and then by "Z". Author(s) François Husson See Also triangle.test, triangle.pair.test triangle.test 55 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. Author(s) François Husson See Also triangle.pair.test, triangle.design Examples 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) Index ∗Topic color coltable, 10 ∗Topic datasets chocolates, 8 cocktail, 9 compo.cocktail, 12 fast, 18 hedo.cocktail, 20 hedochoc, 21 napping, 27 napping.don, 28 napping.words, 28 perfume, 42 senso.cocktail, 49 sensochoc, 50 sensopanels, 50 ∗Topic dplot plot.fast, 42 ∗Topic manip magicsort, 25 scalebypanelist, 47 ∗Topic math optimaldesign, 29 ∗Topic models averagetable, 3 carto, 7 decat, 16 graphinter, 19 interact, 24 paneliperf, 30 panelperf, 40 search.desc, 48 triangle.design, 51 triangle.pair.test, 52 triangle.test, 53 ∗Topic multivariate carto, 7 construct.axes, 12 cpa, 14 indscal, 22 nappeplot, 26 panellipse, 32 panellipse.session, 35 panelmatch, 38 plotpanelist, 44 pmfa, 45 ∗Topic print print.fast, 46 ∗Topic univar ardi, 1 barrow, 5 boxprod, 6 histprod, 21 aov, 4, 17, 20, 24, 32, 41 ardi, 1 averagetable, 3 barrow, 5 boxplot, 7 boxprod, 6 bxp, 7 carto, 7 chocolates, 8 cocktail, 9 coltable, 10 compo.cocktail, 12 construct.axes, 12 cpa, 14 decat, 2, 16 density, 22 fast, 18, 43, 47 GPA, 8 graphinter, 19 hedo.cocktail, 20 56 INDEX 57 hedochoc, 21 hist, 22 histprod, 21 indscal, 22, 27, 46 interact, 24 lm, 40 magicsort, 25 MFA, 8, 14, 46 nappeplot, 23, 26, 46 napping, 27, 27 napping.don, 28 napping.words, 28 optimaldesign, 29 paneliperf, 30, 41 panellipse, 32, 38, 40 panellipse.session, 35, 35, 40 panelmatch, 35, 38 panelperf, 32, 40 par, 7, 11, 33, 36, 39, 43–45 perfume, 42 plot, 5 plot.fast, 42 plotpanelist, 44 pmfa, 23, 27, 45 print.fast, 46 scalebypanelist, 47 search.desc, 48 senso.cocktail, 49 sensochoc, 50 sensopanels, 50 stripchart, 7 triangle.design, 51, 53, 54 triangle.pair.test, 51, 52, 54 triangle.test, 51, 53, 53