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Extract the score-level table from a `csem` object

Source: R/methods-csem.R
by_score.Rd

`by_score()` returns the score-level summary table of a `csem` object: one row per distinct observed score, with the estimation columns collapsed to that score. It is the accessor-style equivalent of the `$by_score` component and of `as.data.frame(x, by = "score")`.

Usage

by_score(x, ...)

# S3 method for class 'csem'
by_score(x, ...)

Arguments

x

A `csem` object.

...

Currently ignored; present for S3 extensibility.

Value

A data frame with one row per distinct observed score.

See also

[as.data.frame.csem()] for the person-level and score-level tables through the `as.data.frame` interface; [coef.csem()] for the variance components.

Examples

set.seed(1)
d <- matrix(rbinom(60 * 12, 1, 0.5), nrow = 60)
fit <- csem_gt(d, error_type = "absolute")
head(by_score(fit))
#>   observed_score group_size       cov_xim csem_var.absolute csem.absolute
#> 1      0.1666667          1 -0.0022727273        0.01262626     0.1123666
#> 2      0.2500000          3  0.0008838384        0.01704545     0.1305582
#> 3      0.3333333          7  0.0034632035        0.02020202     0.1421338
#> 4      0.4166667         15  0.0076010101        0.02209596     0.1486471
#> 5      0.5000000         14  0.0042207792        0.02272727     0.1507557
#> 6      0.5833333         10  0.0052272727        0.02209596     0.1486471
#>   csem_var.analytic.absolute se.analytic.absolute ci_low.analytic.absolute
#> 1               0.0016585424           0.04072521               0.03254671
#> 2               0.0012285499           0.03505068               0.06186018
#> 3               0.0010365890           0.03219610               0.07903061
#> 4               0.0009477385           0.03078536               0.08830890
#> 5               0.0009214124           0.03035478               0.09126140
#> 6               0.0009477385           0.03078536               0.08830890
#>   ci_up.analytic.absolute smoothed_csem.absolute
#> 1               0.1921866              0.1123666
#> 2               0.1992563              0.1305582
#> 3               0.2052370              0.1421338
#> 4               0.2089853              0.1486471
#> 5               0.2102499              0.1507557
#> 6               0.2089853              0.1486471