Display and coercion methods for the object n_panel() and
prec_panel() return. print() gives the number to recruit, the
responding sample it is expected to leave, the precision at the target
and the wave-by-wave table. summary() adds what the design implies
around that answer: the standing sample a rotating design holds, the
response and retention it assumes, where the life's loss falls, and the
launch a design reaching its steady state passes through.
The coercions return the recruitment count, which is a number of units to
release and not the analysis sample: those differ by the whole of the
panel's attrition, and it is why svyplan_panel is a sibling of
svyplan_n rather than a subtype.
Usage
# S3 method for class 'svyplan_panel'
print(x, ...)
# S3 method for class 'svyplan_panel'
summary(object, ...)
# S3 method for class 'summary.svyplan_panel'
print(x, ...)
# S3 method for class 'svyplan_panel'
format(x, ...)
# S3 method for class 'svyplan_panel'
as.double(x, ...)
# S3 method for class 'svyplan_panel'
as.integer(x, ...)
# S3 method for class 'svyplan_panel'
as.data.frame(
x,
row.names = NULL,
optional = FALSE,
stringsAsFactors = FALSE,
validRN = TRUE,
...
)Arguments
- x
A
svyplan_panelobject, or thesummary.svyplan_panelobjectsummary()returns.- ...
Additional arguments are not supported and produce an error.
- object
A
svyplan_panelobject.- row.names, optional, stringsAsFactors, validRN
Standard
as.data.frame()arguments.
Value
print() returns its argument invisibly; summary() an object of
class summary.svyplan_panel carrying the plan, the full wave table,
the launch path and the cohort composition; format() a string;
as.double() the recruitment count and as.integer() the whole units
that count rounds up to; as.data.frame() the wave table.
Examples
plan <- n_panel(
n_prop(p = 0.5, moe = 0.031),
retention = c(0.878, 0.963, 0.936, 0.956),
resp_rate = 0.728
)
plan
#> Panel recruitment (fixed, 5-wave life)
#> issued: 1815 -> 1000 responding at wave 5
#> proportion (wald): se = 0.01582, moe = 0.031, cv = 0.0316
#>
#> wave retention n_resp se moe
#> 1 1321 0.01376 0.02696
#> 2 0.878 1160 0.01468 0.02878
#> 3 0.963 1117 0.01496 0.02932
#> 4 0.936 1046 0.01546 0.03031
#> 5 0.956 1000 0.01582 0.031
#>
#> # summary() for the launch, the loss and per-wave cv
summary(plan)
#> Analysis of a panel recruitment (fixed, 5-wave life)
#>
#> issued: 1815 -> 1000 responding at wave 5
#> rates: response 0.728, retention 0.878 to 0.963 (61% of the loss at wave 1)
#> proportion (wald): se = 0.01582, moe = 0.031, cv = 0.0316
#>
#> Waves of the life
#> wave retention q loss n_resp se moe cv cases
#> 1 0.728 0.606 1321 0.01376 0.02696 0.0275 661
#> 2 0.878 0.6392 0.198 1160 0.01468 0.02878 0.0294 580
#> 3 0.963 0.6155 0.0526 1117 0.01496 0.02932 0.0299 559
#> 4 0.936 0.5761 0.0877 1046 0.01546 0.03031 0.0309 523
#> 5 0.956 0.5508 0.0564 1000 0.01582 0.031 0.0316 500
# A rotating design reaching its steady state: the launch table is the
# occasions before it gets there
rot <- n_panel(
n_prop(p = 0.5, moe = 0.031),
retention = c(0.878, 0.963, 0.936, 0.956),
resp_rate = 0.728,
design = "rotating",
start = "immediate"
)
summary(rot)$launch
#> occasion entrants in_sample n_resp se moe cv steady
#> 1 1 1610 1610 1172 0.01462 0.02865 0.0292 FALSE
#> 2 2 322 1610 1058 0.01539 0.03016 0.0308 FALSE
#> 3 3 322 1610 1035 0.01556 0.03049 0.0311 FALSE
#> 4 4 322 1610 1009 0.01575 0.03087 0.0315 FALSE
#> 5 5 322 1610 1001 0.01582 0.031 0.0316 TRUE
#> 6 6 322 1610 1001 0.01582 0.031 0.0316 TRUE
summary(rot)$composition
#> period wave n_issued n_resp
#> 1 1 1 1606.8361 1169.7767
#> 2 2 1 321.3672 233.9553
#> 3 2 2 1285.4689 821.6511
#> 4 3 1 321.3672 233.9553
#> 5 3 2 321.3672 205.4128
#> 6 3 3 964.1016 593.4375
#> 7 4 1 321.3672 233.9553
#> 8 4 2 321.3672 205.4128
#> 9 4 3 321.3672 197.8125
#> 10 4 4 642.7344 370.3050
#> 11 5 1 321.3672 233.9553
#> 12 5 2 321.3672 205.4128
#> 13 5 3 321.3672 197.8125
#> 14 5 4 321.3672 185.1525
#> 15 5 5 321.3672 177.0058
#> 16 6 1 321.3672 233.9553
#> 17 6 2 321.3672 205.4128
#> 18 6 3 321.3672 197.8125
#> 19 6 4 321.3672 185.1525
#> 20 6 5 321.3672 177.0058
as.integer(plan)
#> [1] 1815
as.data.frame(plan)
#> wave retention q loss_share n_resp se moe
#> 1 1 NA 0.7280000 0.60550724 1320.8645 0.01375754 0.02696429
#> 2 2 0.878 0.6391840 0.19771592 1159.7190 0.01468228 0.02877675
#> 3 3 0.963 0.6155342 0.05264754 1116.8094 0.01496168 0.02932436
#> 4 4 0.936 0.5761400 0.08769657 1045.3336 0.01546474 0.03031033
#> 5 5 0.956 0.5507898 0.05643274 999.3389 0.01581662 0.03100000
#> cv expected_cases
#> 1 0.02751509 660.4322
#> 2 0.02936457 579.8595
#> 3 0.02992337 558.4047
#> 4 0.03092947 522.6668
#> 5 0.03163323 499.6695