Estimate a linear regression model

Estimate a linear regression model

estimate_lm(y, ...)

# S3 method for default
estimate_lm(y, x, data, ...)

# S3 method for formula
estimate_lm(y, data, ...)

Arguments

y

a vector of response values or a formula
...

additional arguments passed to child functions
x

predictor(s), a matrix
data

a data.frame containing the data

Value

a posterior distribution for the regression parameters

Methods (by class)

  • default: Default method

  • formula: Method for a formula

See also

inzposterior

Examples

post <- estimate_lm(Petal.Length ~ Petal.Width, data = iris)
summary(post)
#> 
#> Iterations = 1:1000
#> Thinning interval = 1 
#> Number of chains = 1 
#> Sample size per chain = 1000 
#> 
#> 1. Empirical mean and standard deviation for each variable,
#>    plus standard error of the mean:
#> 
#>          Mean     SD Naive SE Time-series SE
#> beta[1] 1.072 0.3229 0.010212       0.010212
#> beta[2] 2.233 0.2193 0.006933       0.006933
#> sigma2  4.343 0.4996 0.015797       0.015797
#> 
#> 2. Quantiles for each variable:
#> 
#>           2.5%    25%   50%   75% 97.5%
#> beta[1] 0.4419 0.8642 1.076 1.297 1.692
#> beta[2] 1.8081 2.0914 2.233 2.384 2.674
#> sigma2  3.4083 3.9906 4.320 4.677 5.357
#> 
plot(post)