rt_screen() returns one row per trial with the per-group diagnostics
attached as an attribute. print() reports what was removed and why before
the rows, because the count is usually the answer wanted and the rows are
usually too many to read.
Usage
# S3 method for class 'rtprep_screen'
format(x, ...)
# S3 method for class 'rtprep_screen'
print(x, n = 6L, ...)
# S3 method for class 'rtprep_screen'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)
# S3 method for class 'rtprep_screen'
x[i, j, drop = TRUE]Arguments
- x
A screening result from
rt_screen().- ...
Ignored.
- n
Number of trials to show.
print()shows the screen summary in full whatever this is.- row.names, optional
Passed to
base::as.data.frame().- i, j
Row and column indices.
- drop
Whether to drop to a vector when one column is selected.
Value
print() returns x invisibly. format() returns the summary as a
character vector. as.data.frame() returns a plain data.frame.
Details
The fits attribute describes the screen, so it survives column subsetting
and is dropped by row subsetting: after scr[scr$.keep, ] its n_dropped
and prop_dropped count rows that are no longer there, and carrying it along
would attach a table that quietly disagrees with the object. The class
survives for as long as the four columns do.
The rule and group_names attributes, which report_screening() reads,
name the screen rather than count it, so they survive row subsetting too.
as.data.frame() strips the class and the attributes, for when a plain frame
is wanted.
Examples
scr <- rt_screen(rt_example$rt, rule_mad(2.5), .by = rt_example$id)
scr
#> <rtprep screen> 800 trials, sd(2.5, median, mad), 4 groups
#> kept 712 (89.0%), dropped 88 (11.0%)
#> reasons: too_slow 88
#> policy: keep where .prob > 0.5
#> per-group diagnostics: screen_fits(), or attr(x, "fits") -- 4 rows
#>
#> .keep .prob .rule .reason
#> 1 TRUE 1 sd(2.5, median, mad) <NA>
#> 2 TRUE 1 sd(2.5, median, mad) <NA>
#> 3 FALSE 0 sd(2.5, median, mad) too_slow
#> 4 TRUE 1 sd(2.5, median, mad) <NA>
#> 5 TRUE 1 sd(2.5, median, mad) <NA>
#> 6 TRUE 1 sd(2.5, median, mad) <NA>
#> # 794 more trials; as.data.frame(x) for all of them
# the per-group diagnostics the summary points at
head(attr(scr, "fits"))
#> .group n_trials n_dropped prop_dropped center scale lower upper
#> 1 p1 200 23 0.115 0.5385 0.1393644 0.19008900 0.8869110
#> 2 p2 200 23 0.115 0.5270 0.1551555 0.13911121 0.9148888
#> 3 p3 200 17 0.085 0.5390 0.1842486 0.07837841 0.9996216
#> 4 p4 200 25 0.125 0.4915 0.1475187 0.12270325 0.8602967