The printed summary shows the outcome equation, the cutpoints, and – for
inflated models – the inflation equation (with the observed share of the
inflated category and the mean fitted probability of the ordered regime)
and the error correlation, followed by the random-intercept and
fixed-effect summaries where relevant and the fit statistics. Unit
fixed-effect dummies are counted but not printed (see coef()).
Usage
# S3 method for class 'iord'
summary(object, ...)Value
An object of class "summary.iord" holding the coefficient table
(coefficients: estimate, standard error, z, p) with a parallel block
vector naming each row's block ("outcome", "cutpoint",
"inflation", "rho", "random", "fixed effect"), plus the fit
statistics (loglik, aic, bic, df, n, se.type, converged).
See also
confint.iord(), vcov.iord(), tidy.iord() for a data-frame
version of the table, compare_models().
Other inference methods:
confint.iord(),
vcov.iord()
Examples
data(bp)
m <- iop(violence ~ loggdppc + parliament + disaster | loggdppc + parliament + disaster,
data = bp, inflate = "bottom")
#> The inflation equation contains no covariate that is excluded from the outcome equation; the split is then identified by functional form alone. An exclusion restriction is advisable.
s <- summary(m)
s
#>
#> Inflated ordered probit (inflated category: none)
#> Call: iop(formula = violence ~ loggdppc + parliament + disaster | loggdppc +
#> parliament + disaster, data = bp, inflate = "bottom")
#> Response levels (in order): none < repression < civil war
#> N = 1984 inference: analytic
#>
#> Outcome equation (ordered probit):
#> Estimate Std. Error z value Pr(>|z|)
#> loggdppc 0.005329 0.056463 0.094 0.925
#> parliament -0.084935 0.172131 -0.493 0.622
#> disaster 0.269058 0.032781 8.208 2.25e-16 ***
#>
#> Cutpoints:
#> Estimate Std. Error z value Pr(>|z|)
#> none|repression 0.5189 0.4073 1.274 0.202673
#> repression|civil war 1.4112 0.4160 3.392 0.000693 ***
#>
#> Inflation equation (P(ordered regime); inflated category "none", observed share 0.716, mean fitted P(ordered regime) 0.783):
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 19.4107 3.5153 5.522 3.36e-08 ***
#> loggdppc -2.1231 0.3763 -5.643 1.68e-08 ***
#> parliament -0.4271 0.3735 -1.143 0.253
#> disaster -0.1204 0.1412 -0.853 0.394
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> logLik = -1392.86 AIC = 2803.72 BIC = 2854.05 df = 9
s$coefficients[s$block == "inflation", ]
#> Estimate Std. Error z value Pr(>|z|)
#> infl_(Intercept) 19.4106697 3.5153426 5.521701 3.357342e-08
#> infl_loggdppc -2.1231474 0.3762778 -5.642500 1.675983e-08
#> infl_parliament -0.4271020 0.3735361 -1.143402 2.528717e-01
#> infl_disaster -0.1203971 0.1411580 -0.852924 3.937014e-01