eff_size | R Documentation |

Standardized effect sizes are typically calculated using pairwise differences of estimates,
divided by the SD of the population providing the context for those effects.
This function calculates effect sizes from an `emmGrid`

object,
and confidence intervals for them, accounting for uncertainty in both the estimated
effects and the population SD.

eff_size(object, sigma, edf, method = "pairwise", ...)

`object` |
an |

`sigma` |
numeric scalar, value of the population SD. |

`edf` |
numeric scalar that specifies the equivalent degrees of freedom
for the |

`method` |
the contrast method to use to define the effects.
This is passed to |

`...` |
Additional arguments passed to |

Any `by`

variables specified in `object`

will remain in force in the returned
effects, unless overridden in the optional arguments.

For models having a single random effect, such as those fitted using
`lm`

; in that case, the `stats::sigma`

and
`stats::df.residual`

functions may be useful for specifying `sigma`

and `edf`

. For models with more than one random effect, `sigma`

may
be based on some combination of the random-effect variances.

Specifying `edf`

can be rather unintuitive but is also relatively
uncritical; but the smaller the value, the wider the confidence intervals for
effect size. The value of `sqrt(2/edf)`

can be interpreted as the
relative accuracy of `sigma`

; for example, with `edf = 50`

,
*√(2/50) = 0.2*, meaning that `sigma`

is accurate to plus or
minus 20 percent. Note in an example below, we tried two different `edf`

values as kind of a bracketing/sensitivity-analysis strategy. A value of
`Inf`

is allowable, in which case you are assuming that `sigma`

is
known exactly. Obviously, this narrows the confidence intervals for the
effect sizes – unrealistically if in fact `sigma`

is unknown.

an `emmGrid`

object containing the effect sizes

This function uses calls to `regrid`

to put the estimated
marginal means (EMMs) on the log scale. Then an extra element is added to
this grid for the log of `sigma`

and its standard error (where we assume
that `sigma`

is uncorrelated with the log EMMs). Then a call to
`contrast`

subtracts `log{sigma}`

from each of the log EMMs,
yielding values of `log(EMM/sigma)`

.
Finally, the results are re-gridded back to the original scale and the
desired contrasts are computed using `method`

. In the log-scaling
part, we actually rescale the absolute values and keep track of the signs.

The effects are always computed on the scale of the *linear-predictor*;
any response transformation or link function is completely ignored. If you
wish to base the effect sizes on the response scale, it is *not* enough
to replace `object`

with `regrid(object)`

, because this
back-transformation changes the SD required to compute effect sizes.

**Disclaimer:** There is substantial disagreement among practitioners on
what is the appropriate `sigma`

to use in computing effect sizes; or,
indeed, whether *any* effect-size measure is appropriate for some
situations. The user is completely responsible for specifying
appropriate parameters (or for failing to do so).

The examples here illustrate a sobering message that effect sizes are often not nearly as accurate as you may think.

fiber.lm <- lm(strength ~ diameter + machine, data = fiber) emm <- emmeans(fiber.lm, "machine") eff_size(emm, sigma = sigma(fiber.lm), edf = df.residual(fiber.lm)) # or equivalently: eff_size(pairs(emm), sigma(fiber.lm), df.residual(fiber.lm), method = "identity") ### Mixed model example: if (require(nlme)) withAutoprint({ Oats.lme <- lme(yield ~ Variety + factor(nitro), random = ~ 1 | Block / Variety, data = Oats) # Combine variance estimates VarCorr(Oats.lme) (totSD <- sqrt(214.4724 + 109.6931 + 162.5590)) # I figure edf is somewhere between 5 (Blocks df) and 51 (Resid df) emmV <- emmeans(Oats.lme, ~ Variety) eff_size(emmV, sigma = totSD, edf = 5) eff_size(emmV, sigma = totSD, edf = 51) }, spaced = TRUE) # Multivariate model for the same data: MOats.lm <- lm(yield ~ Variety, data = MOats) eff_size(emmeans(MOats.lm, "Variety"), sigma = sqrt(mean(sigma(MOats.lm)^2)), # RMS of sigma() edf = df.residual(MOats.lm))

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