tidy methods that return the MAIHDA estimates as a tidy
tibble, ready for downstream tables (gt, flextable) and
ggplot2. They read the slots that summary.maihda_model
already computes and add no new statistics.
Usage
# S3 method for class 'maihda_summary'
tidy(x, component = c("strata", "variance", "fixed"), ...)
# S3 method for class 'maihda_model'
tidy(x, component = c("strata", "variance", "fixed"), ...)
# S3 method for class 'maihda_analysis'
tidy(
x,
component = c("strata", "variance", "fixed"),
which = c("null", "adjusted"),
...
)Arguments
- x
A
maihda_summary(fromsummary), amaihda_model(fromfit_maihda), or amaihda_analysis(frommaihda).- component
Which estimates to return:
"strata"(default) the stratum (intersection) random-effect estimates – one row per stratum, with
estimate,std.errorandconf.low/conf.high, plus the human-readable intersectionallabelwhen available."variance"the variance-components table (between-stratum, any other random effects, residual, and total) with each component's variance, SD and proportion.
"fixed"the fixed-effect estimates in broom's
term/estimate/std.error/statistic/p.value/conf.low/conf.highshape.
- ...
Unused, for S3 consistency.
- which
For a
maihda_analysis, whether to tidy the"null"(default) or"adjusted"model's summary.
Value
A tibble. For component = "strata": columns
stratum, label, estimate, std.error,
conf.low, conf.high. For "variance": component,
variance, sd, proportion. For "fixed": term,
estimate, std.error, statistic, df, p.value,
conf.low, conf.high.
Fixed-effect statistics
For the lme4, WeMix and ordinal engines
statistic is the Wald statistic (estimate / std.error),
p.value its two-sided p-value, conf.low/conf.high the
matching Wald interval at the summary's conf_level, and df
the denominator degrees of freedom they were built on.
A Gaussian lme4 fit reports containment (between-within) df
and a \(t\); every other engine leaves df NA and uses a z.
Under summary(df_method = "bootstrap") p.value and the
interval come from the parametric bootstrap and df is NA.
See summary.maihda_model. For brms the estimate is the
posterior mean with its Est.Error and credible interval, and
statistic/df/p.value are NA. Standard errors
are NA where they are undefined (a boundary fit).
See also
glance.maihda_analysis for the one-row model summary.
Examples
data("maihda_health_data")
m <- fit_maihda(BMI ~ Age + (1 | Gender:Race:Education), data = maihda_health_data)
tidy(m) # stratum estimates
#> # A tibble: 50 × 6
#> stratum label estimate std.error conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 male × Hispanic × Some College -0.245 1.04 -2.29 1.80
#> 2 2 male × Black × College Grad 0.772 1.11 -1.41 2.96
#> 3 3 female × White × College Grad -1.82 0.352 -2.51 -1.13
#> 4 4 male × Hispanic × 8th Grade 0.921 1.32 -1.66 3.51
#> 5 5 female × Mexican × 8th Grade 1.82 0.951 -0.0409 3.69
#> 6 6 male × White × College Grad -0.743 0.357 -1.44 -0.0431
#> 7 7 female × White × High School 0.00494 0.419 -0.816 0.826
#> 8 8 male × White × Some College 1.02 0.356 0.326 1.72
#> 9 9 female × White × 9 - 11th Grade 0.685 0.604 -0.498 1.87
#> 10 10 female × Hispanic × High School 0.287 1.07 -1.81 2.38
#> # ℹ 40 more rows
tidy(m, component = "variance")
#> # A tibble: 3 × 4
#> component variance sd proportion
#> <chr> <dbl> <dbl> <dbl>
#> 1 Between-stratum (random) 2.90 1.70 0.0627
#> 2 Within-stratum (residual) 43.4 6.59 0.937
#> 3 Total 46.3 6.80 1
tidy(m, component = "fixed")
#> # A tibble: 2 × 8
#> term estimate std.error statistic df p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 28.2 0.453 62.3 49 2.56e-48 27.3 29.1
#> 2 Age 0.0150 0.00736 2.04 2949 4.11e- 2 0.000610 0.0295