# construct_question_frame: Convert from choice_sets to a question data In ExpertChoice: Design of Discrete Choice and Conjoint Analysis

## Description

Convert from choice_sets to a question data

## Usage

 ```1 2 3 4 5``` ```construct_question_frame( augmented_full_factorial, choice_sets, randomise_choice_sets = TRUE ) ```

## Arguments

 `augmented_full_factorial` The augmented full factorial object. `choice_sets` The choice sets list generated from one of the methods. (See Step 6 of the tutorial) `randomise_choice_sets` A binary variable indicating if the order of the choice sets should be randomised. Some methods create choice sets which have a systematic order. Randomising the order of the choice sets does not change the alternatives within the choice sets. It simply rearranges the choice_set object in a random manner.

## Value

a data.frame object

## Examples

 ``` 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``` ```#See Step 9 of Practical Introduction to ExpertChoice vignette. # Step 1 attrshort = list(condition = c("0", "1", "2"), technical =c("0", "1", "2"), provenance = c("0", "1")) #Step 2 # ff stands for "full fatorial" ff <- full_factorial(attrshort) af <- augment_levels(ff) # af stands for "augmented factorial" # Step 3 # Choose a design type: Federov or Orthogonal. Here an Orthogonal one is used. nlevels <- unlist(purrr::map(ff, function(x){length(levels(x))})) fractional_factorial <- DoE.base::oa.design(nlevels = nlevels, columns = "min34") # Step 4 & 5 # The functional draws out the rows from the original augmented full factorial design. colnames(fractional_factorial) <- colnames(ff) fractional <- search_design(ff, fractional_factorial) # Step 5 (skipped, but important, see vignette) # Step 6 # Two modulators c(1,1,1) and c(0,1,1) are specified. dce_modulo <- modulo_method( fractional, list(c(1,1,1),c(0,1,1)) ) # Step 7 and Step 8 are very important for the design, but skipped here. # Step 9! -- Construct a question frame to use with your study. # Note the use of af here. questions <- construct_question_frame(af, dce_modulo) levels(questions\$condition) <- c("bad", "good", "excellent") levels(questions\$technical) <- c("poor", "fair", "skilled") levels(questions\$provenance) <- c("none", "present") questions ```

ExpertChoice documentation built on April 14, 2020, 7:36 p.m.