Empirical-Bayes unit effects of a random-intercept fit

Posterior modes and posterior standard deviations of the unit random intercept(s), from the adaptive quadrature at the fitted parameters.

Usage

ranef(object, ...)

# S3 method for class 'iord'
ranef(object, ...)

Arguments

object

An "iord" object fit with re.

...

Unused.

Value

A data frame with one row per unit: unit, u (outcome-equation intercept), u_sd, and for re_inflation = TRUE also v, v_sd.

See also

The re, re_inflation, and nAGQ arguments of oprobit() and iop(); predict.iord() for marginal and conditional probabilities.

Other panel tools: mundlak()

Examples

set.seed(3)
d <- riop(300, beta = c(0.8, -0.5), tau = c(-0.5, 0.7))
d$unit <- rep(1:15, each = 20)
m <- oprobit(y ~ x1 + x2, d, re = "unit", nAGQ = 7)
#> Warning: oprobit: a random-intercept standard deviation is at zero: the data show no unit-level heterogeneity in that equation.
head(ranef(m))
#>   unit             u       u_sd
#> 1    1 -2.339409e-11 3.1621e-06
#> 2    2  1.595808e-11 3.1621e-06
#> 3    3  2.199489e-11 3.1621e-06
#> 4    4  5.633959e-11 3.1621e-06
#> 5    5 -1.857501e-11 3.1621e-06
#> 6    6 -2.565354e-11 3.1621e-06
## marginal (population-averaged) vs conditional (u = 0) probabilities
head(cbind(predict(m)[, 1], predict(m, type = "prob_conditional")[, 1]), 3)
#>           [,1]      [,2]
#> [1,] 0.3397051 0.3397051
#> [2,] 0.5192576 0.5192576
#> [3,] 0.1306866 0.1306866