Displays a `csem` object as a sequence of console blocks mirroring the output of the `gtcsem` Stata command: a header summarising the design, the ANOVA table, the D-study population error variances and standard errors of measurement, the reliability-like coefficients, the quadratic smoothing fits (when smoothing was applied), and the mean sampling variance of each estimator across persons.
Usage
# S3 method for class 'csem'
print(x, ...)See also
[summary.csem()] for the score-level table and global statistics; [coef.csem()] and [by_score()] 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, cutpoint = 0.5)
print(fit)
#> ----------------------------------------------------------------
#> Conditional SEMs in Generalizability Theory
#> ----------------------------------------------------------------
#> Design : univariate single-facet (p x i, crossed)
#> Persons (n_p) : 80
#> G-study items : 15
#> D-study items : 15
#> Method : all
#> SE method : analytical
#> Smoothing : quadratic on observed score
#> Cutpoint : 0.500000
#> ANOVA table
#> ----------------------------------------------------------------
#> Effect df SS MS sigma^2
#> ----------------------------------------------------------------
#> p 79 17.213333 0.217890 -0.002251
#> i 14 3.796667 0.271190 0.000244
#> pi 1106 278.336667 0.251661 0.251661
#> D-study error variances and SEMs (n_i' = 15)
#> ----------------------------------------------------------------
#> sigma^2(Delta) = 0.016794 sigma(Delta) = 0.129590 (absolute)
#> sigma^2(delta) = 0.016777 sigma(delta) = 0.129528 (relative)
#> Reliability-like coefficients
#> ----------------------------------------------------------------
#> Generalizability coef. E rho^2 = -0.1550
#> Dependability coef. Phi = -0.1548
#> Dep. coef. for cutpoint Phi(lambda) = -0.1267 (lambda = 0.500)
#> Quadratic smoothing fits (y = b0 + b1*score + b2*score^2)
#> --------------------------------------------------------------------------
#> Quantity b0 b1 b2 R^2 RMSE
#> --------------------------------------------------------------------------
#> abs_ev 0.00000 0.07143 -0.07143 1.0000 0.00000
#> rel_ev_full -0.00191 0.07952 -0.07954 0.6770 0.00103
#> rel_ev_la -0.00189 0.07763 -0.07765 0.6719 0.00102
#> rel_ev_unc -0.00002 0.07143 -0.07143 1.0000 0.00000
#> Mean variance of estimator across persons
#> ----------------------------------------------------------------
#> Quantity Analytical
#> ----------------------------------
#> abs_ev 6.195986e-04
#> rel_ev_full 6.387278e-04
#> rel_ev_la 6.232301e-04
#> rel_ev_unc 6.202104e-04