StatsBernoulli: Bernoulli Trials

In bfw: Bayesian Framework for Computational Modeling

Bernoulli Trials

Description

Conduct bernoulli trials

Usage

StatsBernoulli(
  x = NULL,
  x.names = NULL,
  DF,
  params = NULL,
  initial.list = list(),
  ...
)

Arguments

x	predictor variable(s), Default: NULL
x.names	optional names for predictor variable(s), Default: NULL
DF	data for analysis
params	define parameters to observe, Default: NULL
initial.list	initial values for analysis, Default: list()
...	further arguments passed to or from other methods

See Also

complete.cases

Examples

## Create coin toss data: heads = 50 and tails = 50
#fair.coin<- as.matrix(c(rep("Heads",50),rep("Tails",50)))
#colnames(fair.coin) <- "X"
#fair.coin <- bfw(project.data = fair.coin,
#                 x = "X",
#                 saved.steps = 50000,
#                 jags.model = "bernoulli",
#                 jags.seed = 100,
#                 ROPE = c(0.4,0.6),
#                 silent = TRUE)
#fair.coin.freq <- binom.test( 50000 * 0.5, 50000)

## Create coin toss data: heads = 20 and tails = 80
#biased.coin <- as.matrix(c(rep("Heads",20),rep("Tails",80)))
#colnames(biased.coin) <- "X"
#biased.coin <- bfw(project.data = biased.coin,
#                   x = "X",
#                   saved.steps = 50000,
#                   jags.model = "bernoulli",
#                   jags.seed = 101,
#                   initial.list = list(theta = 0.7),
#                   ROPE = c(0.4,0.6),
#                   silent = TRUE)
#biased.coin.freq <- binom.test( 50000 * 0.8, 50000)

## Print Bayesian and frequentist results of fair coin
#fair.coin$summary.MCMC[,c(3:6,9:12)]

## Mode       ESS     HDIlo     HDIhi    ROPElo    ROPEhi    ROPEin         n
## 0.505 50480.000     0.405     0.597     2.070     2.044    95.886   100.00

#sprintf("Frequentist: %.3f [%.3f , %.3f], p = %.3f" ,
#        fair.coin.freq$estimate ,
#        fair.coin.freq$conf.int[1] ,
#        fair.coin.freq$conf.int[2] ,
#        fair.coin.freq$p.value)

## [1] "Frequentist: 0.500 [0.496 , 0.504], p = 1.000"

## Print Bayesian and frequentist results of biased coin
#biased.coin$summary.MCMC[,c(3:6,9:12)]

## Mode       ESS     HDIlo     HDIhi    ROPElo    ROPEhi    ROPEin         n
## 0.803 50000.000     0.715     0.870     0.000    99.996     0.004   100.000

#sprintf("Frequentist: %.3f [%.3f , %.3f], p = %.3f" ,
#        biased.coin.freq$estimate ,
#        biased.coin.freq$conf.int[1] ,
#        biased.coin.freq$conf.int[2] ,
#        biased.coin.freq$p.value)

## [1] "Frequentist: 0.800 [0.796 , 0.803], p = 0.000"

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