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tidy_es

Source: R/tidy_es.R
tidy_es.Rd

a modification of the renamed tidy command to adds the effect size (confidence intervals for the regression coefficient and adjusted R^2)

Usage

tidy_es(
  model,
  ci = 0.95,
  mcc = c("single-step", "none", "uncorrected", "fdr", "FDR", "tukey", "tukey's HSD",
    "Tukey"),
  test = c("t", "F"),
  ...
)

Arguments

model

model fit from lm(), lmerTest::lmer(), lme4::lmer(), or multcomp::glht()

ci

confidence interval (0, 1)

mcc

multiple comparison correction method. Options include "single-step" (default, which is what glht provides for Tukey's HSD), "none" (uncorrected), "fdr", "FDR", "tukey", "tukey's HSD", and "Tukey".

test

statistical test for `glht` models. Options are "t" (default) for t-tests (multiple comparisons/pairwise contrasts) and "F" for joint F-tests.

...

additional arguments passed to the underlying tidying functions (e.g. `label` for `tidy_es_cope_F`).

Value

data.frame

See also

Other model summary helpers: get_lm_multicollinearity(), models2coefs(), models2omni(), r_sq_to_adj_r_sq(), renamed_tidy(), tidy_es_cope_F(), tidy_es_cope_t(), tidy_es_lm(), tidy_es_lmer()

Examples

# example: lm
tidy_es(lm(salary ~ yrs.since.phd, carData::Salaries))
#>       lh op            rh          b        se         t             p
#> 1 salary  ~   (Intercept) 91718.6854 2765.7923 33.161813 3.332606e-116
#> 2 salary  ~ yrs.since.phd   985.3421  107.3651  9.177488  2.495042e-18
#>    r_sq_adj    b_ci_ll   b_ci_ul r_sq_adj_ci_ll r_sq_adj_ci_ul
#> 1 0.7350649 86281.1714 97156.199      0.7026265              1
#> 2 0.1736680   774.2636  1196.421      0.1210838              1

# example: lmer
tidy_es(lmerTest::lmer(Reaction ~ 1 + Days + (1 + Days | Subject), lme4::sleepstudy))
#> Computing profile confidence intervals ...
#>         lh op          rh         b       se       df         t            p
#> 1 Reaction  ~ (Intercept) 251.40510 6.824597 16.99973 36.838090 1.171558e-17
#> 2 Reaction  ~        Days  10.46729 1.545790 16.99998  6.771481 3.263825e-06
#>    r_sq_adj    b_ci_ll   b_ci_ul r_sq_adj_ci_ll r_sq_adj_ci_ul
#> 1 0.9869002 237.680695 265.12951      0.9743608              1
#> 2 0.7136175   7.358653  13.57592      0.4836195              1

# example: glht
model_fit <- lm(salary ~ rank + discipline, carData::Salaries)
c <- rbind(
  AssocProf_minus_AsstProf = c(0, 1, 0, 0),
  Prof_minus_AsstProf      = c(0, 0, 1, 0),
  Prof_minus_AssocProf     = c(0, -1, 1, 0)
)
model_fit_cope_t <- multcomp::glht(model_fit, c)
tidy_es(model_fit_cope_t)
#>       lh op                       rh        b       se  df         t
#> 1 salary  ~ AssocProf_minus_AsstProf 13761.54 3960.661 393  3.474557
#> 2 salary  ~      Prof_minus_AsstProf 47843.84 3111.552 393 15.376197
#> 3 salary  ~     Prof_minus_AssocProf 34082.30 3159.885 393 10.785929
#>             p   r_sq_adj   b_ci_ll  b_ci_ul r_sq_adj_ci_ll r_sq_adj_ci_ul
#> 1 0.001630696 0.02733473  4485.219 23037.87    0.007003391              1
#> 2 0.000000000 0.37403428 40556.225 55131.45    0.314935373              1
#> 3 0.000000000 0.22644424 26681.481 41483.11    0.169579185              1