R/ms.prediction.pw.R
In jlopezsi/MDSConjoint: An implementation of metric and nonmetric conjoint models for marketing decisions
fac2char <- function(mpi){
#' @title This function transform factor variable into character type
#' @description
#' This functions takes as data frame with variables of factor type and transform them into
#' a character type. It is usefull for computing product profile utility from a matrix of part worths
#' @param mpi a data frame
#' @return mpi a data frame transformed
#' @export
#'
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' fac2char(osc$market.profiles)
mpi<-data.frame(lapply(mpi, as.character), stringsAsFactors = FALSE)
mpi<-as.character(mpi)
return(mpi)
}
cupi <- function(pw, mp, n.attributes){
#' @title This function computes for client i the utility of each product profile from a data frame of part worths
#' @description
#'
#' This function computes for client i the utility of each market profile (given rivals' profiles and new profiles
#' ) for consumer i. The data frame mp has the data of existing profiles
#' for which we are going to compute the market shares.
#' It is a table of market product profiles (rows) by
#' attributes (columns), wp is a data frame of client i (rows) by part worths
#' (colums), design.l is a list with conjoint desing.
#' @param mp a data frame with the description of competitors' existing product profiles
#' @param pw a data frame with all clients' partworths
#' @param n.attributes the number attributs of conjoint design
#' @return upi a matrix with the prediction of utilities for client i
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' #this function is called by uop().
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' #osc.cupi <-cupi(osc$market.profiles, osc.conjoint$part.worths[1,], osc$design)
#' #head(osc.cupi)
pwa<-0
pw.names<-names(pw)
brand.names<-row.names(mp)
n.mp<-nrow(mp)
pw<-as.matrix(pw)
mp<-as.matrix(mp)
mpi<-fac2char(mp[1,])
# compute utilities for market profile number 1
for (j in 1:n.attributes) {
index<-which(pw.names==mpi[j])
pwa<-pwa+as.numeric(pw[index])
}
upi <- as.data.frame(pwa) #build the data frame with product profile utilities
#### Second and n market profiles
for (i in 2:n.mp) {
mpi<-fac2char(mp[i,])
#mpi<-data.frame(lapply(mpi, as.character), stringsAsFactors = FALSE)
#mpi<-as.character(mpi)
pwa<-0
for (j in 1:n.attributes) {
index<-which(pw.names==mpi[j])
pwa<-pwa+as.numeric(pw[index])
}
upi<-cbind(upi, pwa)
}
upi <- as.data.frame(upi)
names(upi) <- brand.names
return(upi)
}
uop <- function(mp, pw, design.l) {
# Copyright 2016 Jordi L. Sintas
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#' @title This function computes the utility of each product profile from a data frame of part worths
#' @description
#'
#' This function computes the utility of each profile (given rivals' profiles and new profiles
#' ) for each consumer. The data frame mp has the data of existing profiles
#' for which we are going to compute the market shares.
#' It is a table of market product profiles (rows) by
#' attributes (columns), wp is a data frame of clients (rows) by part worths
#' (colums), design.l is a list with conjoint desing.
#' @param mp a data frame with the description of competitors' existing product profiles
#' @param pw a data frame with all clients' partworths
#' @param design.l a list with conjoint design
#' @return up a matrix with the prediction of utilities for all clients
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' #this function is called by market share function.
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' osc.uop <-uop(osc$market.profiles, osc.conjoint$part.worths, osc$design)
#' head(osc.uop)
#'
################### other variables in the function ##################
# n.attributes number of attributs in conjoint design
######################
n.attributes=length(design.l)
up <- apply(pw, 1, cupi, mp, n.attributes)
up<-do.call(rbind.data.frame, up)
return(up)
}
########
ms.fe.pw <- function(mp, pw, design.l) {
# Copyright 2016 Jordi L. Sintas
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
#' @title This function computes the market share of product profiles from a data frame of part worths
#' @description
#'
#' This function computes the market share of market product profiles
#' according to the first choice rule. Uses a data frame with part woths to predict utilities
#' market profiles, mp, is a matrix of market product profiles (rows)
#' by attributes (columns); pw is a data frame of clients (rows) by
#' attribut levels part worths (colums); design.l is a list with the definition of the conjoint design
#'
#' @param mp a data frame with the description of competitors' existing product profiles
#' @param pw a data frame frame with all clients' part worths
#' @param design.l a list with conjoint design
#' @return ms
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' names(osc)
#' sapply(osc, class)
#' sapply(osc$market.proiles, class)
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' ms.fe.pw(osc$market.profiles, osc.conjoint$part.worths, osc$design)
#' class(ms.fe.pw(osc$market.profiles, osc$ratings, osc$bundles))
###################### other variables in the function ###########
# up the results of the utilities.of.profiles() function
# individual.elections cols. identiy respondents for whech the product profiles reports the maximum utility
# ms for each brand compute the mean across respondents
# brand.name collects brands' names
############################
up <- uop(mp, pw, design.l) #gets the matrix of utilities using the function utilitiesProfiles()
individual.elections <- apply(up, 1, which.max) # cols. identiy respondents for whech the product profiles reports the maximum utility
# individual.elections #print the findings
ms <- vector() #initialize the vextor of market shares
brands <- ncol(up)
for (brand in 1:brands) {
ms[brand] <- length(individual.elections[individual.elections ==
brand])/length(individual.elections) # compute market share for brand j
}
ms<-round(ms*100, digits=4)
brand.names <- row.names(mp)
names(ms) <- brand.names
return(as.data.frame(ms))
}
################
ms.us.pw <- function(mp, pw, design.l) {
# Copyright 2016 Jordi L. Sintas
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
#' @title This function computes the market share of product profiles
#' @description
#'
#' This function computes the market share of product profiles
#' according to the utility share rule. Uses a data frame with part woths to predict utilities
#' market profiles, mp, is a matrix of market product profiles (rows)
#' by attributes (columns); pw is a data frame of clients (rows) by
#' attribut levels part worths (colums); design.l is a list with the definition of the conjoint design.
#' @param mp a matrix with the description of competitors' product profiles
#' @param pw a data frame with all clients' part worths
#' @param design.l a a list with conjoint design
#' @return ms
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' ms.us.pw(osc$market.profiles, osc.conjoint$part.worths, osc$design)
#' class(ms.us.pw(osc$market.profiles, osc$ratings, osc$bundles))
############################ other variables in the function #########
# up the results of the utilities.of.profiles() function
# row.profiles.clients compute clients' probability of buying each brand
# ms for each brand compute the mean across respondents
# brand.name collects brands' names
#############################
up <- uop(mp, pw, design.l) #gets the matrix of utilities using the function utilitiesProfiles()
row.profiles.clients <- prop.table(as.matrix(up), 1) #compute de probability of buying each brand
#sum.util.client <- rowSums(up) #Sum rows across respondents
#profiles.prob.client <- up/sum.util.client #compute de probability of buying each brand
# head(profiles.prob.client) sum(profiles.prob.client[1,]) check
# whether sum = 1
ms <- colMeans(row.profiles.clients)*100 #for each brand compute the mean across respondents
ms<-round(ms, digits=4)
brand.names <- row.names(mp)
names(ms) <- brand.names
options(digits=3)
return(as.data.frame(ms))
}
###############
ms.logit.pw <- function(mp, pw, design.l) {
# Copyright 2016 Jordi L. Sintas
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
#' @title Computes the market share of the product profiles
#' @description
#'
#' This function
#' computes the market share of the product profiles according to the
#' logit rule. Uses a data frame with part woths to predict utilities
#' market profiles, mp, is a matrix of market product profiles (rows)
#' by attributes (columns); pw is a data frame of clients (rows) by
#' attribut levels part worths (colums); design.l is a list with the definition of the conjoint design.
#'
#' @param mp a matrix with the description of competitors' product profiles
#' @param pw a data frame with all clients' part worths
#' @param design.l a list with conjoint design
#' @return ms
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<- MDSConjointData$osc
#' names(osc)
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' ms.logit.pw(osc$market.profiles, osc.conjoint$part.worths, osc$design)
#' class(ms.logit.pw(osc$market.profiles, osc$ratings, osc$bundles))
##################### other variables in the function #############
# up the results of the utilities.of.profiles() function
# exp.row.profiles.clients compute clients' probability of buying each brand
# ms for each brand compute the mean across respondents
# brand.name collects brands' names
up <- uop(mp, pw, design.l)
exp.up <- exp(up)
exp.row.profiles.clients <- prop.table(as.matrix(exp.up), 1) #compute de probability of buying each brand
#sum.exp.util.client <- rowSums(exp.up) #Sum rows across respondents
#profiles.prob.client <- exp.up/sum.exp.util.client #compute de probability of buying each brand
# head(profiles.prob.client) sum(profiles.prob.client[1,]) check
# whether sum = 1
options(scipen=999)
ms <- colMeans(exp.row.profiles.clients)*100 #for each brand compute the mean across respondents
# for each brand compute the mean across respondents
ms<-round(ms, digits=4)
brand.names <- row.names(mp)
names(ms) <- brand.names
return(as.data.frame(ms))
}
jlopezsi/MDSConjoint documentation built on May 19, 2019, 12:48 p.m.
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fac2char <- function(mpi){
#' @title This function transform factor variable into character type
#' @description
#' This functions takes as data frame with variables of factor type and transform them into
#' a character type. It is usefull for computing product profile utility from a matrix of part worths
#' @param mpi a data frame
#' @return mpi a data frame transformed
#' @export
#'
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' fac2char(osc$market.profiles)
mpi<-data.frame(lapply(mpi, as.character), stringsAsFactors = FALSE)
mpi<-as.character(mpi)
return(mpi)
}
cupi <- function(pw, mp, n.attributes){
#' @title This function computes for client i the utility of each product profile from a data frame of part worths
#' @description
#'
#' This function computes for client i the utility of each market profile (given rivals' profiles and new profiles
#' ) for consumer i. The data frame mp has the data of existing profiles
#' for which we are going to compute the market shares.
#' It is a table of market product profiles (rows) by
#' attributes (columns), wp is a data frame of client i (rows) by part worths
#' (colums), design.l is a list with conjoint desing.
#' @param mp a data frame with the description of competitors' existing product profiles
#' @param pw a data frame with all clients' partworths
#' @param n.attributes the number attributs of conjoint design
#' @return upi a matrix with the prediction of utilities for client i
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' #this function is called by uop().
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' #osc.cupi <-cupi(osc$market.profiles, osc.conjoint$part.worths[1,], osc$design)
#' #head(osc.cupi)
pwa<-0
pw.names<-names(pw)
brand.names<-row.names(mp)
n.mp<-nrow(mp)
pw<-as.matrix(pw)
mp<-as.matrix(mp)
mpi<-fac2char(mp[1,])
# compute utilities for market profile number 1
for (j in 1:n.attributes) {
index<-which(pw.names==mpi[j])
pwa<-pwa+as.numeric(pw[index])
}
upi <- as.data.frame(pwa) #build the data frame with product profile utilities
#### Second and n market profiles
for (i in 2:n.mp) {
mpi<-fac2char(mp[i,])
#mpi<-data.frame(lapply(mpi, as.character), stringsAsFactors = FALSE)
#mpi<-as.character(mpi)
pwa<-0
for (j in 1:n.attributes) {
index<-which(pw.names==mpi[j])
pwa<-pwa+as.numeric(pw[index])
}
upi<-cbind(upi, pwa)
}
upi <- as.data.frame(upi)
names(upi) <- brand.names
return(upi)
}
uop <- function(mp, pw, design.l) {
# Copyright 2016 Jordi L. Sintas
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#' @title This function computes the utility of each product profile from a data frame of part worths
#' @description
#'
#' This function computes the utility of each profile (given rivals' profiles and new profiles
#' ) for each consumer. The data frame mp has the data of existing profiles
#' for which we are going to compute the market shares.
#' It is a table of market product profiles (rows) by
#' attributes (columns), wp is a data frame of clients (rows) by part worths
#' (colums), design.l is a list with conjoint desing.
#' @param mp a data frame with the description of competitors' existing product profiles
#' @param pw a data frame with all clients' partworths
#' @param design.l a list with conjoint design
#' @return up a matrix with the prediction of utilities for all clients
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' #this function is called by market share function.
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' osc.uop <-uop(osc$market.profiles, osc.conjoint$part.worths, osc$design)
#' head(osc.uop)
#'
################### other variables in the function ##################
# n.attributes number of attributs in conjoint design
######################
n.attributes=length(design.l)
up <- apply(pw, 1, cupi, mp, n.attributes)
up<-do.call(rbind.data.frame, up)
return(up)
}
########
ms.fe.pw <- function(mp, pw, design.l) {
# Copyright 2016 Jordi L. Sintas
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
#' @title This function computes the market share of product profiles from a data frame of part worths
#' @description
#'
#' This function computes the market share of market product profiles
#' according to the first choice rule. Uses a data frame with part woths to predict utilities
#' market profiles, mp, is a matrix of market product profiles (rows)
#' by attributes (columns); pw is a data frame of clients (rows) by
#' attribut levels part worths (colums); design.l is a list with the definition of the conjoint design
#'
#' @param mp a data frame with the description of competitors' existing product profiles
#' @param pw a data frame frame with all clients' part worths
#' @param design.l a list with conjoint design
#' @return ms
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' names(osc)
#' sapply(osc, class)
#' sapply(osc$market.proiles, class)
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' ms.fe.pw(osc$market.profiles, osc.conjoint$part.worths, osc$design)
#' class(ms.fe.pw(osc$market.profiles, osc$ratings, osc$bundles))
###################### other variables in the function ###########
# up the results of the utilities.of.profiles() function
# individual.elections cols. identiy respondents for whech the product profiles reports the maximum utility
# ms for each brand compute the mean across respondents
# brand.name collects brands' names
############################
up <- uop(mp, pw, design.l) #gets the matrix of utilities using the function utilitiesProfiles()
individual.elections <- apply(up, 1, which.max) # cols. identiy respondents for whech the product profiles reports the maximum utility
# individual.elections #print the findings
ms <- vector() #initialize the vextor of market shares
brands <- ncol(up)
for (brand in 1:brands) {
ms[brand] <- length(individual.elections[individual.elections ==
brand])/length(individual.elections) # compute market share for brand j
}
ms<-round(ms*100, digits=4)
brand.names <- row.names(mp)
names(ms) <- brand.names
return(as.data.frame(ms))
}
################
ms.us.pw <- function(mp, pw, design.l) {
# Copyright 2016 Jordi L. Sintas
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
#' @title This function computes the market share of product profiles
#' @description
#'
#' This function computes the market share of product profiles
#' according to the utility share rule. Uses a data frame with part woths to predict utilities
#' market profiles, mp, is a matrix of market product profiles (rows)
#' by attributes (columns); pw is a data frame of clients (rows) by
#' attribut levels part worths (colums); design.l is a list with the definition of the conjoint design.
#' @param mp a matrix with the description of competitors' product profiles
#' @param pw a data frame with all clients' part worths
#' @param design.l a a list with conjoint design
#' @return ms
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<-MDSConjointData$osc
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' ms.us.pw(osc$market.profiles, osc.conjoint$part.worths, osc$design)
#' class(ms.us.pw(osc$market.profiles, osc$ratings, osc$bundles))
############################ other variables in the function #########
# up the results of the utilities.of.profiles() function
# row.profiles.clients compute clients' probability of buying each brand
# ms for each brand compute the mean across respondents
# brand.name collects brands' names
#############################
up <- uop(mp, pw, design.l) #gets the matrix of utilities using the function utilitiesProfiles()
row.profiles.clients <- prop.table(as.matrix(up), 1) #compute de probability of buying each brand
#sum.util.client <- rowSums(up) #Sum rows across respondents
#profiles.prob.client <- up/sum.util.client #compute de probability of buying each brand
# head(profiles.prob.client) sum(profiles.prob.client[1,]) check
# whether sum = 1
ms <- colMeans(row.profiles.clients)*100 #for each brand compute the mean across respondents
ms<-round(ms, digits=4)
brand.names <- row.names(mp)
names(ms) <- brand.names
options(digits=3)
return(as.data.frame(ms))
}
###############
ms.logit.pw <- function(mp, pw, design.l) {
# Copyright 2016 Jordi L. Sintas
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
#' @title Computes the market share of the product profiles
#' @description
#'
#' This function
#' computes the market share of the product profiles according to the
#' logit rule. Uses a data frame with part woths to predict utilities
#' market profiles, mp, is a matrix of market product profiles (rows)
#' by attributes (columns); pw is a data frame of clients (rows) by
#' attribut levels part worths (colums); design.l is a list with the definition of the conjoint design.
#'
#' @param mp a matrix with the description of competitors' product profiles
#' @param pw a data frame with all clients' part worths
#' @param design.l a list with conjoint design
#' @return ms
#' @export
#' @examples
#' data(MDSConjointData)
#' names(MDSConjointData)
#' osc<- MDSConjointData$osc
#' names(osc)
#' osc.conjoint <- conjoint.estimation(osc$ratings, osc$bundles, osc$design)
#' names(osc.conjoint)
#' # [1] "summary" "fit" "part.worths" "prediction"
#' #head(osc.conjoint$summary)
#' head(osc.conjoint$fit)
#' head(osc.conjoint$part.worths)
#' head(osc.conjoint$prediction)
#' ms.logit.pw(osc$market.profiles, osc.conjoint$part.worths, osc$design)
#' class(ms.logit.pw(osc$market.profiles, osc$ratings, osc$bundles))
##################### other variables in the function #############
# up the results of the utilities.of.profiles() function
# exp.row.profiles.clients compute clients' probability of buying each brand
# ms for each brand compute the mean across respondents
# brand.name collects brands' names
up <- uop(mp, pw, design.l)
exp.up <- exp(up)
exp.row.profiles.clients <- prop.table(as.matrix(exp.up), 1) #compute de probability of buying each brand
#sum.exp.util.client <- rowSums(exp.up) #Sum rows across respondents
#profiles.prob.client <- exp.up/sum.exp.util.client #compute de probability of buying each brand
# head(profiles.prob.client) sum(profiles.prob.client[1,]) check
# whether sum = 1
options(scipen=999)
ms <- colMeans(exp.row.profiles.clients)*100 #for each brand compute the mean across respondents
# for each brand compute the mean across respondents
ms<-round(ms, digits=4)
brand.names <- row.names(mp)
names(ms) <- brand.names
return(as.data.frame(ms))
}
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