glmB | R Documentation |
Generalized Linear Models using bayesian inference (logistic regression)
glmB(
formula,
data = NULL,
graphOutput = FALSE,
nIter = 10000,
thin = 1,
returnCodaSamples = FALSE,
priorPrec = c(0.001, 0.001)
)
formula |
an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. |
data |
an optional data frame containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which blm is called. |
graphOutput |
regression parameters graphical output (MCMC Trace and posterior density) |
nIter |
number of iterations |
thin |
thinning interval for monitors |
returnCodaSamples |
if TRUE, return the cosa samples output as a mcmc.list |
priorPrec |
precisions of alpha and beta dnorm distributions |
Models for glm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response.
regression parameters
JuG
dtf1 <- data.frame(Y = rbinom(n = 60 ,size=1,prob = .3), X = rnorm(60, 10,2))
mod1 <- glmB(Y ~ X , data= dtf1)
dtf2 <- data.frame(Y = rbinom(n = 60 ,size=1,prob = .3), X = rnorm(60, 10,2), X2 = rnorm(60, 10,2))
mod2 <- glmB(Y ~ X + X2, data= dtf2 )
mod3 <- glmB(Y ~ X + X2, data= dtf2 ,priorPrec = c(.001, .1) )
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