jhelvy
commented
1 year ago You sometimes can get NA values with small sample sizes. That's my first guess of what's happening since you used head() to only view the first few rows of the result.
In the example that you referenced here, I cannot replicate the NA result. Here's what I get with that example:
set.seed(1234)
library(cbcTools)
# A simple conjoint experiment about apples
# Generate all possible profiles
profiles <- cbc_profiles(
price = c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5),
type = c("Fuji", "Gala", "Honeycrisp"),
freshness = c('Poor', 'Average', 'Excellent')
)
# Make a randomized survey design
design <- cbc_design(
profiles = profiles,
n_resp = 300, # Number of respondents
n_alts = 3, # Number of alternatives per question
n_q = 6 # Number of questions per respondent
)
# Simulate random choices
data <- cbc_choices(
design = design,
obsID = "obsID"
)
# Conduct a power analysis
power <- cbc_power(
data = data,
pars = c("price", "type", "freshness"),
outcome = "choice",
obsID = "obsID",
nbreaks = 10,
n_q = 6
)Now preview the results:
head(power)
#> sampleSize coef est se
#> 1 30 price -0.07194700 0.07609126
#> 2 30 typeGala 0.34172607 0.22791822
#> 3 30 typeHoneycrisp 0.50663546 0.22807907
#> 4 30 freshnessAverage 0.01794571 0.22327851
#> 5 30 freshnessExcellent 0.13248210 0.22751630
#> 6 60 price 0.02152378 0.05237815tail(power)
#> sampleSize coef est se
#> 45 270 freshnessExcellent -0.17115823 0.07369265
#> 46 300 price 0.03658552 0.02240770
#> 47 300 typeGala 0.05830221 0.07098261
#> 48 300 typeHoneycrisp 0.04437270 0.07072744
#> 49 300 freshnessAverage -0.20545772 0.06966451
#> 50 300 freshnessExcellent -0.17473701 0.07007619
STEVEAP28
John --
When running cbc_tools with the Example from (https://rdrr.io/github/jhelvy/cbcTools/man/cbc_power.html) I get the following Output with "NA"s for Estimated Coefficients and Standard Errors:
I re-ran this several times -- but still get this "NA" Result. Am I missing something?