Print summary for brsmm models

Print summary for brsmm models

Usage

# S3 method for class 'summary.brsmm'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x

A "summary.brsmm" object.

digits

Number of digits.

...

Passed to printCoefmat.

Value

Invisibly returns x.

See also

summary.brsmm, brsmm, print.brsmm

Examples

# \donttest{
dat <- data.frame(
  y = c(
    0, 5, 20, 50, 75, 90, 100, 30, 60, 45,
    10, 40, 55, 70, 85, 25, 35, 65, 80, 15
  ),
  x1 = rep(c(1, 2), 10),
  id = factor(rep(1:4, each = 5))
)
prep <- brs_prep(dat, ncuts = 100)
#> brs_prep: n = 20 | exact = 0, left = 1, right = 1, interval = 18
fit <- brsmm(y ~ x1, random = ~ 1 | id, data = prep)
print(summary(fit))
#> 
#> Call:
#> brsmm(formula = y ~ x1, random = ~1 | id, data = prep)
#> 
#> Randomized Quantile Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -2.0612 -0.4803 -0.1664  0.6832  2.7743 
#> 
#> Coefficients (mean model with logit link):
#>             Estimate Std. Error z value Pr(>|z|)
#> (Intercept)   0.4223     0.8675   0.487    0.626
#> x1           -0.3380     0.5278  -0.640    0.522
#> 
#> Phi coefficients (precision model with logit link):
#>             Estimate Std. Error z value Pr(>|z|)  
#> (Intercept)  -0.5806     0.3246  -1.788   0.0737 .
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Random-effects parameters (Cholesky scale):
#>                      Estimate Std. Error z value Pr(>|z|)
#> logSD.(Intercept)|id  -0.6273     0.7474  -0.839    0.401
#> ---
#> Mixed beta interval model (Laplace)
#> Observations: 20  | Groups: 4 
#> Log-likelihood: -92.1831 on 4 Df | AIC: 192.3663 | BIC: 196.3492 
#> Pseudo R-squared: 0.0029 
#> Number of iterations: 36 (BFGS) 
#> Censoring: 18 interval | 1 left | 1 right 
#> 
# }