This function converts categorical variables between different coding schemes. Standard coding keeps categorical variables as-is (factor or character). Dummy coding uses a reference category (all zeros) with indicator variables for other levels. Effects coding uses -1 for the reference category to ensure coefficients sum to zero.
Arguments
- data
A
cbc_designorcbc_choicesobject- coding
Character. Type of encoding: "standard", "dummy", or "effects". If NULL and ref_levels is NULL, data is returned unchanged. If NULL and ref_levels is specified, the current encoding is maintained.
- ref_levels
Named list specifying reference levels for categorical variables. For example:
list(powertrain = "Gasoline", brand = "A"). If NULL (default), uses the first level of each categorical variable as reference.
Examples
library(cbcTools)
# Create profiles with categorical variables
profiles <- cbc_profiles(
price = c(10, 20, 30),
quality = c("Low", "Medium", "High"),
brand = c("A", "B")
)
# Create design (defaults to standard coding)
design <- cbc_design(
profiles = profiles,
n_alts = 2,
n_q = 4
)
# Convert to dummy coding
design_dummy <- cbc_encode(design, coding = "dummy")
head(design_dummy)
#> Design method: random
#> Encoding: dummy
#> Structure: 100 respondents × 4 questions × 2 alternatives
#> Profile usage: 18/18 (100.0%)
#>
#> 💡 Use cbc_inspect() for a more detailed summary
#> 💡 Use cbc_encode(design, 'standard') to view categorical format
#>
#> First few rows of design:
#> profileID respID qID altID obsID price qualityMedium qualityHigh brandB
#> 1 17 1 1 1 1 20 0 1 1
#> 2 8 1 1 2 1 20 0 1 0
#> 3 6 1 2 1 2 30 1 0 0
#> 4 5 1 2 2 2 20 1 0 0
#> 5 9 1 3 1 3 30 0 1 0
#> 6 3 1 3 2 3 30 0 0 0
# Convert to effects coding
design_effects <- cbc_encode(design, coding = "effects")
head(design_effects)
#> Design method: random
#> Encoding: effects
#> Structure: 100 respondents × 4 questions × 2 alternatives
#> Profile usage: 18/18 (100.0%)
#>
#> 💡 Use cbc_inspect() for a more detailed summary
#> 💡 Use cbc_encode(design, 'standard') to view categorical format
#>
#> First few rows of design:
#> profileID respID qID altID obsID price qualityMedium qualityHigh brandB
#> 1 17 1 1 1 1 20 0 1 1
#> 2 8 1 1 2 1 20 0 1 -1
#> 3 6 1 2 1 2 30 1 0 -1
#> 4 5 1 2 2 2 20 1 0 -1
#> 5 9 1 3 1 3 30 0 1 -1
#> 6 3 1 3 2 3 30 -1 -1 -1
# Convert back to standard
design_standard <- cbc_encode(design_dummy, coding = "standard")
head(design_standard)
#> Design method: random
#> Encoding: standard
#> Structure: 100 respondents × 4 questions × 2 alternatives
#> Profile usage: 18/18 (100.0%)
#>
#> 💡 Use cbc_inspect() for a more detailed summary
#>
#> First few rows of design:
#> profileID respID qID altID obsID price quality brand
#> 1 17 1 1 1 1 20 High B
#> 2 8 1 1 2 1 20 High A
#> 3 6 1 2 1 2 30 Medium A
#> 4 5 1 2 2 2 20 Medium A
#> 5 9 1 3 1 3 30 High A
#> 6 3 1 3 2 3 30 Low A
# Custom reference levels with dummy coding
design_dummy2 <- cbc_encode(
design,
coding = "dummy",
ref_levels = list(quality = "Medium", brand = "B")
)
head(design_dummy2)
#> Design method: random
#> Encoding: dummy
#> Structure: 100 respondents × 4 questions × 2 alternatives
#> Profile usage: 18/18 (100.0%)
#>
#> 💡 Use cbc_inspect() for a more detailed summary
#> 💡 Use cbc_encode(design, 'standard') to view categorical format
#>
#> First few rows of design:
#> profileID respID qID altID obsID price qualityLow qualityHigh brandA
#> 1 17 1 1 1 1 20 0 1 0
#> 2 8 1 1 2 1 20 0 1 1
#> 3 6 1 2 1 2 30 0 0 1
#> 4 5 1 2 2 2 20 0 0 1
#> 5 9 1 3 1 3 30 0 1 1
#> 6 3 1 3 2 3 30 1 0 1
# Update reference levels without changing encoding
design_updated <- cbc_encode(
design_dummy,
ref_levels = list(quality = "High")
)
head(design_updated)
#> Design method: random
#> Encoding: dummy
#> Structure: 100 respondents × 4 questions × 2 alternatives
#> Profile usage: 18/18 (100.0%)
#>
#> 💡 Use cbc_inspect() for a more detailed summary
#> 💡 Use cbc_encode(design, 'standard') to view categorical format
#>
#> First few rows of design:
#> profileID respID qID altID obsID price qualityLow qualityMedium brandB
#> 1 17 1 1 1 1 20 0 0 1
#> 2 8 1 1 2 1 20 0 0 0
#> 3 6 1 2 1 2 30 0 1 0
#> 4 5 1 2 2 2 20 0 1 0
#> 5 9 1 3 1 3 30 0 0 0
#> 6 3 1 3 2 3 30 1 0 0