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Extract a tidy table of estimates

Source: R/tidy_estimates.R
tidy_estimates.Rd

tidy_estimates() turns the output of a model (or an existing data frame of results) into a single standardised table with the columns term, estimate, std.error, conf.low and conf.high. It is the common currency used by coefficient_plot() and compare_models(), but is useful on its own.

Usage

tidy_estimates(x, conf_level = 0.95, exponentiate = FALSE, ...)

Arguments

x

A fitted model or a data frame of results.

conf_level

Confidence (or credible) level for the interval, a single number strictly between 0 and 1.

exponentiate

Whether to exponentiate the estimates and interval limits, which turns log-odds into odds ratios and log rates into rate ratios. The std.error column stays on the link scale, as in broom::tidy(). A data frame is read as estimates on the link scale. Defaults to FALSE.

...

Passed to methods (and to broom::tidy() in the fallback). The merMod method additionally accepts effects, which currently only supports "fixed".

Value

A data frame with one row per term and the columns term, estimate, std.error, conf.low and conf.high.

Details

Methods are provided for lm, glm and merMod (mixed models fitted with 'lme4') objects, and for data frames. For any other model class the function falls back to broom::tidy() when the 'broom' package is installed.

Confidence intervals are Wald intervals, estimate +/- multiplier * standard error, with the multiplier the model's own tests use. An lm object gets the t interval of stats::confint(). A glm whose dispersion is estimated (Gaussian, Gamma, inverse Gaussian and quasi families) gets t on the residual degrees of freedom, since stats::summary.glm() then tests with t. One whose dispersion is fixed (binomial, Poisson and MASS::glm.nb()) gets z. The choice is read from the column the summary reports, "t value" or "z value". At 12 residual degrees of freedom, the t interval is about 11% wider than the z interval, and at 100 about 1% wider. A merMod fit gets z, since 'lme4' gives no degrees of freedom. Supply a data frame with your own intervals (for example profiled or posterior intervals) to override this.

Examples

fit <- lm(yield ~ rainfall + fertiliser + treatment, data = crop_yield)
tidy_estimates(fit)
#>                term     estimate    std.error     conf.low   conf.high
#> 1       (Intercept) -0.764921362 0.3933784207 -1.540719161 0.010876437
#> 2          rainfall  0.004527229 0.0007284476  0.003090628 0.005963831
#> 3        fertiliser  0.010488592 0.0013233496  0.007878760 0.013098424
#> 4 treatmentenhanced  1.290135752 0.1189761405  1.055498001 1.524773503

# Odds ratios from a logistic regression:
gfit <- glm(accuracy ~ word_frequency + condition,
            data = lexical_decision, family = binomial)
tidy_estimates(gfit, exponentiate = TRUE)
#>                 term  estimate std.error  conf.low conf.high
#> 1        (Intercept) 6.4237841 0.3895402 2.9937372 13.783776
#> 2     word_frequency 1.3544310 0.1132865 1.0847440  1.691167
#> 3 conditionunrelated 0.8622706 0.2726054 0.5053607  1.471247

# A data frame of pre-computed estimates is standardised, not re-fitted:
df <- data.frame(
  parameter = c("a", "b"),
  Estimate = c(0.2, -0.4),
  "2.5 %" = c(0.1, -0.6),
  "97.5 %" = c(0.3, -0.2),
  check.names = FALSE
)
tidy_estimates(df)
#>   term estimate std.error conf.low conf.high
#> 1    a      0.2        NA      0.1       0.3
#> 2    b     -0.4        NA     -0.6      -0.2