Produces a score-level summary of a `csem` object. The returned object carries the full `by_score` table augmented with two columns, `cum_freq` (the cumulative proportion of persons at or below each observed score) and `percentile` (`100 * cum_freq`), together with the population-level global statistics: the relative and absolute standard errors of measurement and their companion reliability-like coefficients (the generalizability coefficient `E rho^2` and the dependability coefficient `Phi`).
Usage
# S3 method for class 'csem'
summary(object, ...)Value
An object of class `summary.csem`: a list with components `call`, `paradigm`, `methods`, `error_types`, `n_persons`, `n_items`, `global_stats` (a list with `relative_sem`, `erho2`, `absolute_sem`, `phi`) and `by_score_summary` (the augmented `by_score` table).
Details
The returned object retains every column of the original `by_score` table; [print.summary.csem()] displays a curated subset.
See also
[print.csem()] for the full-object display; [by_score()] and [coef.csem()] for programmatic access to the underlying components.
Examples
set.seed(1)
d <- matrix(rbinom(80 * 15, 1, 0.5), nrow = 80)
fit <- csem_gt(d, error_type = "absolute")
s <- summary(fit)
s$global_stats
#> $relative_sem
#> [1] 0.1295275
#>
#> $erho2
#> [1] -0.1549878
#>
#> $absolute_sem
#> [1] 0.1295903
#>
#> $phi
#> [1] -0.1548144
#>
head(s$by_score_summary)
#> observed_score group_size cov_xim csem_var.absolute csem.absolute
#> 1 0.2000000 1 0.011250000 0.01142857 0.1069045
#> 2 0.2666667 4 0.007113095 0.01396825 0.1181874
#> 3 0.3333333 9 0.001587302 0.01587302 0.1259882
#> 4 0.4000000 16 0.002578125 0.01714286 0.1309307
#> 5 0.4666667 19 0.003536967 0.01777778 0.1333333
#> 6 0.5333333 12 0.005000000 0.01777778 0.1333333
#> csem_var.analytic.absolute se.analytic.absolute ci_low.analytic.absolute
#> 1 0.0009033233 0.03005534 0.04799712
#> 2 0.0007390827 0.02718608 0.06490364
#> 3 0.0006503928 0.02550280 0.07600359
#> 4 0.0006022155 0.02454008 0.08283306
#> 5 0.0005807078 0.02409788 0.08610236
#> 6 0.0005807078 0.02409788 0.08610236
#> ci_up.analytic.absolute smoothed_csem.absolute cum_freq percentile
#> 1 0.1658119 0.1069045 0.0125 1.25
#> 2 0.1714711 0.1181874 0.0625 6.25
#> 3 0.1759727 0.1259882 0.1750 17.50
#> 4 0.1790284 0.1309307 0.3750 37.50
#> 5 0.1805643 0.1333333 0.6125 61.25
#> 6 0.1805643 0.1333333 0.7625 76.25