StatsCovariate: Covariate

In bfw: Bayesian Framework for Computational Modeling

Covariate

Description

Covariate estimations (including correlation and Cronbach's alpha)

Usage

StatsCovariate(
  y = NULL,
  y.names = NULL,
  x = NULL,
  x.names = NULL,
  DF,
  params = NULL,
  job.group = NULL,
  initial.list = list(),
  jags.model,
  ...
)

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()
jags.model	specify which module to use
...	further arguments passed to or from other methods

Value

covariate, correlation and (optional) Cronbach's alpha

See Also

complete.cases

Examples

## Create normal distributed data with mean = 0 and standard deviation = 1
### r = 0.5
#data <- MASS::mvrnorm(n=100,
#                      mu=c(0, 0),
#                      Sigma=matrix(c(1, 0.5, 0.5, 1), 2),
#                      empirical=TRUE)
## Add names
#colnames(data) <- c("X","Y")
## Create noise with mean = 10 / -10 and sd = 1
### r = -1.0
#noise <- MASS::mvrnorm(n=2,
#                       mu=c(10, -10),
#                       Sigma=matrix(c(1, -1, -1, 1), 2),
#                       empirical=TRUE)
## Combine noise and data
#biased.data <- rbind(data,noise)
#
#
## Run analysis on normal distributed data
#mcmc <- bfw(project.data = data,
#            y = "X,Y",
#            saved.steps = 50000,
#            jags.model = "covariate",
#            jags.seed = 100,
#            silent = TRUE)
## Run robust analysis on normal distributed data
#mcmc.robust <- bfw(project.data = data,
#                   y = "X,Y",
#                   saved.steps = 50000,
#                   jags.model = "covariate",
#                   run.robust = TRUE,
#                   jags.seed = 101,
#                   silent = TRUE)
## Run analysis on data with outliers
#biased.mcmc <- bfw(project.data = biased.data,
#                   y = "X,Y",
#                   saved.steps = 50000,
#                   jags.model = "covariate",
#                   jags.seed = 102,
#                   silent = TRUE)
## Run robust analysis on data with outliers
#biased.mcmc.robust <- bfw(project.data = biased.data,
#                          y = "X,Y",
#                          saved.steps = 50000,
#                          jags.model = "covariate",
#                          run.robust = TRUE,
#                          jags.seed = 103,
#                          silent = TRUE)
## Print frequentist results
#stats::cor(data)[2]
## [1] 0.5
#stats::cor(noise)[2]
## [1] -1
#stats::cor(biased.data)[2]
## [1] -0.498
## Print Bayesian results
#mcmc$summary.MCMC
##                   Mean Median  Mode   ESS HDIlo HDIhi   n
## cor[1,1]: X vs. X 1.000  1.000 0.999     0 1.000 1.000 100
## cor[2,1]: Y vs. X 0.488  0.491 0.496 19411 0.337 0.633 100
## cor[1,2]: X vs. Y 0.488  0.491 0.496 19411 0.337 0.633 100
## cor[2,2]: Y vs. Y 1.000  1.000 0.999     0 1.000 1.000 100
#mcmc.robust$summary.MCMC
##                   Mean Median  Mode   ESS HDIlo HDIhi   n
## cor[1,1]: X vs. X 1.00  1.000 0.999     0 1.000 1.000 100
## cor[2,1]: Y vs. X 0.47  0.474 0.491 18626 0.311 0.626 100
## cor[1,2]: X vs. Y 0.47  0.474 0.491 18626 0.311 0.626 100
## cor[2,2]: Y vs. Y 1.00  1.000 0.999     0 1.000 1.000 100
#biased.mcmc$summary.MCMC
##                    Mean Median   Mode   ESS  HDIlo  HDIhi   n
## cor[1,1]: X vs. X  1.000  1.000  0.999     0  1.000  1.000 102
## cor[2,1]: Y vs. X -0.486 -0.489 -0.505 19340 -0.627 -0.335 102
## cor[1,2]: X vs. Y -0.486 -0.489 -0.505 19340 -0.627 -0.335 102
## cor[2,2]: Y vs. Y  1.000  1.000  0.999     0  1.000  1.000 102
#biased.mcmc.robust$summary.MCMC
##                   Mean Median  Mode   ESS HDIlo HDIhi   n
## cor[1,1]: X vs. X 1.000  1.000 0.999     0 1.000 1.000 102
## cor[2,1]: Y vs. X 0.338  0.343 0.356 23450 0.125 0.538 102
## cor[1,2]: X vs. Y 0.338  0.343 0.356 23450 0.125 0.538 102

bfw documentation built on March 18, 2022, 6:19 p.m.