Description Usage Arguments Details Value Examples
Computes a bayesian linear regression fit.
1 |
formula |
an object of class "formula": a symbolic description of the model to be fitted. |
prior |
the prior distribution, either a |
beta |
the precision of the data used for the model. |
... |
Additional arguments and values. |
Models for blm
are provided as for lm
. If prior
distribution prior
of the weights is not provided the prior means
are set to 0 and the variances to 1.
A object of class blm
. An object of class "lm" is a list
containing at least the following components:
call: the matched call
formula: the formula used
df.residual: the degrees of freedom of the model
frame: the model frame used
matrix: the model matrix used
beta: the precision of the data
prior: the prior distribution used
posterior: the posterior distribution
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | w0 <- 0.3 ; w1 <- 1.1 ; b <- 1.3
x <- rnorm(50)
y <- rnorm(50, w1 * x + w0, 1/b)
mod <- blm(y~x, data=data.frame(x=x, y=y))
mod
plot(mod)
#use with known precision
mod_det <- blm(y~x, beta=b, data=data.frame(x=x, y=y))
mod_det
#use of a prior, typically from an existing model
x2 <- rnorm(50)
y2 <- rnorm(50, w1 * x2 + w0, 1/b)
mod2 <- blm(y~x, prior=mod, data=data.frame(x=x2, y=y2))
mod2
#use with 2 explanatory variables
w2 <- 3.3
z <- rnorm(50)
y <- rnorm(50, w2 * z + w1 * x + w0, 1/b)
mod <- blm(y~x+z, data=data.frame(x=x, y=y, z=z))
mod
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