# StatsBernoulli: Bernoulli Trials In bfw: Bayesian Framework for Computational Modeling

## Description

Conduct bernoulli trials

## Usage

 ```1 2``` ```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

`complete.cases`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56``` ```# 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" ```