R: Examples for 'emmeans::eff

### ** Examples

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))

 contrast effect.size    SE df lower.CL upper.CL
 A - B         -0.650 0.650 11   -2.081    0.781
 A - C          0.993 0.726 11   -0.604    2.590
 B - C          1.643 0.800 11   -0.118    3.405

sigma used for effect sizes: 1.595 
Confidence level used: 0.95

# or equivalently:
eff_size(pairs(emm), sigma(fiber.lm), df.residual(fiber.lm), method = "identity")

 contrast effect.size    SE df lower.CL upper.CL
 (A - B)       -0.650 0.650 11   -2.081    0.781
 (A - C)        0.993 0.726 11   -0.604    2.590
 (B - C)        1.643 0.800 11   -0.118    3.405

sigma used for effect sizes: 1.595 
Confidence level used: 0.95

### 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)

Loading required package: nlme

> Oats.lme <- lme(yield ~ Variety + factor(nitro), random = ~1 | Block/Variety, 
+     data = Oats)

> VarCorr(Oats.lme)
            Variance     StdDev  
Block =     pdLogChol(1)         
(Intercept) 214.4724     14.64488
Variety =   pdLogChol(1)         
(Intercept) 109.6931     10.47345
Residual    162.5590     12.74986

> (totSD <- sqrt(214.4724 + 109.6931 + 162.559))
[1] 22.06183

> emmV <- emmeans(Oats.lme, ~Variety)

> eff_size(emmV, sigma = totSD, edf = 5)
 contrast                 effect.size    SE df lower.CL upper.CL
 Golden Rain - Marvellous      -0.240 0.330  5   -1.087    0.608
 Golden Rain - Victory          0.312 0.336  5   -0.551    1.174
 Marvellous - Victory           0.551 0.365  5   -0.387    1.490

Results are averaged over the levels of: nitro 
sigma used for effect sizes: 22.06 
Degrees-of-freedom method: inherited from containment when re-gridding 
Confidence level used: 0.95 

> eff_size(emmV, sigma = totSD, edf = 51)
 contrast                 effect.size    SE df lower.CL upper.CL
 Golden Rain - Marvellous      -0.240 0.322  5   -1.067    0.587
 Golden Rain - Victory          0.312 0.322  5   -0.517    1.140
 Marvellous - Victory           0.551 0.325  5   -0.285    1.388

Results are averaged over the levels of: nitro 
sigma used for effect sizes: 22.06 
Degrees-of-freedom method: inherited from containment when re-gridding 
Confidence level used: 0.95

# 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))

 contrast                 effect.size    SE df lower.CL upper.CL
 Golden Rain - Marvellous      -0.237 0.496 15   -1.295     0.82
 Golden Rain - Victory          0.308 0.498 15   -0.752     1.37
 Marvellous - Victory           0.545 0.504 15   -0.529     1.62

Results are averaged over the levels of: rep.meas 
sigma used for effect sizes: 22.31 
Confidence level used: 0.95

Examples for 'emmeans::eff_size'

Calculate effect sizes and confidence bounds thereof