gainLoss: Merges gain and loss matrices

In avilesd/productConfig: Tools to analyze behavioral patterns from Product Configurators

Description

The matrices are composed by row-binded gain matrices (on top) and loss matrices (below). This is more of an auxiliary functions, since it is easiert to work with united matrices. Rows and columns are unnamed. The output of this function is further used by norm.gainLoss.

Usage

gainLoss(dataset, userid = NULL, attr = NULL, rounds = NULL,
  refps = NULL, cost_ids = NULL)

Arguments

dataset	a data.frame with the user generated data from a product configurator. See Details of decisionMatrix for more information about which data should included in this argument.
userid	an integer vector indicating for which user the output of this function should be calculated. This functions is vectorised in this argument, i.e. you may enter more userIDs simultaneously.
attr	attribute IDs, vector of integer numbers corresponding to the attributes (columns) you desire to use.
rounds	integer vector, text option or a list of integer vectors. Which steps of the configuration process should be shown? Defaults are first and last step. Text options are all, first, last. Alternatively, a vector of arbitrarily chosen rounds can be entered as well.
refps	a list of numeric vectors, one for each user. Reference Points: each point corresponds to one attribute, therefore the amount of attributes and of refps entered, should be equal. Default assumes the refps as the default values of the initial product configuration for each user. You may fully or partially enter your own reference points, check below for more info.
cost_ids	argument used to convert selected cost attributes into benefit attributes. Integer vector. Cost type attributes have the characteristic, that a lower value means the user is better off than with a higher value. E.g. price is often considered a cost type attribute. Should be equal to attr input or a subset of it.

Details

dataset We assume the input data.frame has following columns usid = User IDs, round = integers indicating which round the user is in (0-index works best for 'round'), atid = integer column for referring the attribute ID (1 indexed), selected = numeric value of the attribute for a specific, given round, selectable = amount of options the user can chose at a given round, with the current configuration. This is a necessary parameter.

dataset We assume the input data.frame has following columns usid = User IDs, round = integers indicating which round the user is in (0-index works best for 'round'), atid = integer column for referring the attribute ID (1 indexed), selected = numeric value of the attribute for a specific, given round, selectable = amount of options the user can chose at a given round, with the current configuration. This is a necessary parameter.

userid is a necessary parameter, without it you'll get a warning. Default is NULL.

attr Default calculates with all attributes. Attributes are automatically read from provided table, it is important you always provide the complete dataset so that the package functions properly. Moreover the attributes will not be sorted. Output columns are returned in the ordered they were inputed.

rounds If you need to compute different rounds for each user you enter, this argument accepts a list of integer vectors indicating which rounds should be used for each user. The function does not read names, it works in the order the list was given.

refps If you only want to see the results for one attribute you may enter only a couple of reference points but you have to tell the function which attributes you want to use those referene points for. So the amount of attr and of refps should be the same. Moreover the functions always orders de attr, so be sure to input the reference point also in an ascending order corresponding to their attributes.

Value

a list of combined matrices.

Examples

#Not runnable yet
gainLoss(pc_config_data, 11:12)
gainLoss(my_data, userid = 10:100)
gainLoss(monitor_data, c(50,51), rounds = "last", refps = c(0.1,0.3,0.4,0.5), cost_ids = 3)
gainLoss(data1, 40, attr = 1)
gainLoss(data, 3, attr = c(1,2,3,4))

avilesd/productConfig documentation built on May 11, 2019, 4:08 p.m.