Each domain's estimate against a reference domain: the difference, its standard error from the design (the covariance between the two estimates included, which is what a pair of separate intervals leaves out), a confidence interval and a two-sided p-value from the normal approximation. Use it to test whether two regions or groups differ, rather than checking whether their separate intervals overlap: overlapping intervals do not mean no difference, and domains of one survey are not independent.
Usage
svycmp(
des,
data,
y,
by = NULL,
stat = c("mean", "total", "proportion", "ratio"),
den = NULL,
lvls = NULL,
ref = NULL,
conflvl = 0.95
)Arguments
- des
A
svydes()built ondata.- data
The data frame the design was built on.
- y
Character scalar, the analysis column: numeric for a mean, total or ratio; any type for a proportion (each distinct value is a category).
- by
The domain columns to compare across.
- stat
"mean","total","proportion"or"ratio"(ofytoden).- den
For a ratio, the denominator column.
- lvls
For a proportion, the categories to report (default all observed).
- ref
The reference domain, as its label (for several
bycolumns, the labels joined with" / "); a number is matched as the label it prints as. DefaultNULL: the first domain.- conflvl
Numeric scalar in (0, 1), the confidence level; default
0.95.
Value
A data frame of class c("rmbl_svy_compare", "data.frame"),
one row per non-reference domain (and category): domain,
level (for a proportion only), numeric estimate (the
domain's), reference (the reference domain's), difference,
se, lower, upper and p_value. Attributes
ref, stat, y and conflvl.
Details
The estimates come from svyest() with Wald
intervals. For domain
\(i\) and reference \(0\) the difference is \(d = \hat\theta_i -
\hat\theta_0\) with \(SE(d) = \sqrt{V_{ii} + V_{00} - 2V_{i0}}\) from the
design covariance; the interval is \(d \pm z\,SE(d)\) and the p-value
\(2\Phi(-|d|/SE(d))\), both on the normal distribution (there is no
df argument). For a proportion every category is compared
separately. No adjustment for multiple comparisons is made.
Errors: by missing or empty; fewer than two domains; a ref
that is not a single domain label present in the data; and every error of
svyest().
Examples
set.seed(1)
d <- data.frame(region = rep(c("north", "south", "east"), length.out = 150),
smoker = rbinom(150, 1, 0.3), w = runif(150, 50, 150))
des <- svydes(d, w = "w")
svycmp(des, d, "smoker", by = "region", stat = "proportion",
lvls = "1", ref = "north")
#> ╭── Survey proportion of smoker against north ───────────────────────────╮
#> │ Variable smoker │
#> │ Reference domain north │
#> │ Confidence 95% │
#> │ │
#> │ domain level estimate reference difference se │
#> │ ------ ----- -------- --------- ---------- ------- │
#> │ east 1 0.2538 0.3701 -0.1164 0.09485 │
#> │ south 1 0.2352 0.3701 -0.1350 0.09464 │
#> │ │
#> │ domain 95% CI p_value │
#> │ ------ ----------------- ------- │
#> │ east [-0.3023, 0.0695] 0.220 │
#> │ south [-0.3205, 0.0505] 0.154 │
#> │ │
#> │ Not distinguishable from 0 (interval includes it): east (p = 0.220) │
#> │ and south (p = 0.154). │
#> │ │
#> │ Each difference is a domain's estimate minus the reference domain's, │
#> │ with the design's covariance between the two taken into account. │
#> ╰────────────────────────────────────────────────────────────────────────╯
# means, against the default (first) domain, at 90 percent
d$drinks <- rpois(150, 3)
svycmp(des, d, "drinks", by = "region", conflvl = 0.90)
#> ╭── Survey mean of drinks against east ──────────────────────────────────╮
#> │ Variable drinks │
#> │ Reference domain east │
#> │ Confidence 90% │
#> │ │
#> │ domain estimate reference difference se 90% CI │
#> │ ------ -------- --------- ---------- ------ ----------------- │
#> │ north 3.066 3.361 -0.2952 0.3947 [-0.9444, 0.3539] │
#> │ south 3.083 3.361 -0.2779 0.3811 [-0.9048, 0.3490] │
#> │ │
#> │ domain p_value │
#> │ ------ ------- │
#> │ north 0.454 │
#> │ south 0.466 │
#> │ │
#> │ Not distinguishable from 0 (interval includes it): north (p = 0.454) │
#> │ and south (p = 0.466). │
#> │ │
#> │ Each difference is a domain's estimate minus the reference domain's, │
#> │ with the design's covariance between the two taken into account. │
#> ╰────────────────────────────────────────────────────────────────────────╯
# the reference must be one of the domains
try(svycmp(des, d, "drinks", by = "region", ref = "west"))
#> Error : `ref` is not one of the domains: east, north, south