StatsSoftmax {bfw}

Softmax Regression

Description

Perform softmax regression (i.e., multinomial logistic regression)

Usage

StatsSoftmax(
  y = NULL,
  y.names = NULL,
  x = NULL,
  x.names = NULL,
  DF,
  params = NULL,
  job.group = NULL,
  initial.list = NULL,
  run.robust = FALSE,
  ...
)

Arguments

y

criterion variable(s), Default: NULL

y.names

optional names for criterion variable(s), Default: NULL

x

predictor variable(s), Default: NULL

x.names

optional names for predictor variable(s), Default: NULL

DF

data to analyze

params

define parameters to observe, Default: NULL

job.group

for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL

initial.list

initial values for analysis, Default: list()

run.robust

logical, indicating whether or not robust analysis, Default: FALSE

...

further arguments passed to or from other methods

See Also

complete.cases

Examples

## Conduct softmax regression on Cats data
### Reward is 0 = Food and 1 = Dance
### Sample 100 datapoints from Cats data
#mcmc <- bfw(project.data = bfw::Cats,
#            y = "Alignment",
#            x = "Ratings,Reward",
#            saved.steps = 50000,
#            jags.model = "softmax",
#            jags.seed = 100)
## Conduct binominal generalized linear model
#model <- glm(Alignment ~ Ratings + Reward, data=bfw::Cats, family = binomial(link="logit"))
## Print output from softmax
#mcmc$summary.MCMC
#
##                               Mean Median      Mode   ESS  HDIlo  HDIhi    n
##beta[1,1]: Evil vs. Ratings   0.000   0.00 -0.000607     0  0.000  0.000 2000
##beta[1,2]: Evil vs. Reward    0.000   0.00 -0.000607     0  0.000  0.000 2000
##beta[2,1]: Good vs. Ratings   1.289   1.29  1.283403 19614  1.187  1.387 2000
##beta[2,2]: Good vs. Reward    1.276   1.27  1.279209 20807  0.961  1.597 2000
##beta0[1]: Intercept: Evil     0.000   0.00 -0.000607     0  0.000  0.000 2000
##beta0[2]: Intercept: Good    -7.690  -7.68 -7.659198 17693 -8.472 -6.918 2000
##zbeta[1,1]: Evil vs. Ratings  0.000   0.00 -0.000607     0  0.000  0.000 2000
##zbeta[1,2]: Evil vs. Reward   0.000   0.00 -0.000607     0  0.000  0.000 2000
##zbeta[2,1]: Good vs. Ratings  2.476   2.47  2.464586 19614  2.280  2.664 2000
##zbeta[2,2]: Good vs. Reward   0.501   0.50  0.501960 20807  0.377  0.626 2000
##zbeta0[1]: Intercept: Evil    0.000   0.00 -0.000607     0  0.000  0.000 2000
##zbeta0[2]: Intercept: Good   -1.031  -1.03 -1.024178 22812 -1.185 -0.870 2000
#
## Print (truncated) output from GML
##               Estimate   Std. Error z value Pr(>|z|)
##(Intercept)     -6.39328    0.27255 -23.457  < 2e-16 ***
##Ratings          1.28480    0.05136  25.014  < 2e-16 ***
##RewardAffection  1.26975    0.16381   7.751  9.1e-15 ***