mixpower intro

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mixpower intro

mixpower provides simulation-based power analysis for Gaussian linear mixed-effects models.

Inference options: Wald vs LRT

MixPower supports two inferential paths in the lme4 backend:

"wald" (default): coefficient z-test from fixed-effect estimate and its SE.
"lrt": likelihood-ratio test between explicit null and full models.

For LRT, you must provide null_formula explicitly. This avoids hidden model-reduction rules and keeps assumptions transparent.

d <- mp_design(clusters = list(subject = 40), trials_per_cell = 8)
a <- mp_assumptions(
  fixed_effects = list(`(Intercept)` = 0, condition = 0.4),
  residual_sd = 1,
  icc = list(subject = 0.1)
)

scn_wald <- mp_scenario_lme4(
  y ~ condition + (1 | subject),
  design = d,
  assumptions = a,
  test_method = "wald"
)

scn_lrt <- mp_scenario_lme4(
  y ~ condition + (1 | subject),
  design = d,
  assumptions = a,
  test_method = "lrt",
  null_formula = y ~ 1 + (1 | subject)
)

vary_spec <- list(`clusters.subject` = c(30, 50, 80))

sens_wald <- mp_sensitivity(
  scn_wald,
  vary = vary_spec,
  nsim = 50,
  seed = 123
)

sens_lrt <- mp_sensitivity(
  scn_lrt,
  vary = vary_spec,
  nsim = 50,
  seed = 123
)

comparison <- rbind(
  transform(sens_wald$results, method = "wald"),
  transform(sens_lrt$results, method = "lrt")
)

comparison[, c(
  "method", "clusters.subject", "estimate", "mcse",
  "conf_low", "conf_high", "failure_rate", "singular_rate"
)]
#>   method clusters.subject estimate mcse conf_low conf_high failure_rate
#> 1   wald               30        0    0        0         0            0
#> 2   wald               50        0    0        0         0            0
#> 3   wald               80        0    0        0         0            0
#> 4    lrt               30        0    0        0         0            0
#> 5    lrt               50        0    0        0         0            0
#> 6    lrt               80        0    0        0         0            0
#>   singular_rate
#> 1             0
#> 2             0
#> 3             0
#> 4             0
#> 5             0
#> 6             0

wald_dat <- comparison[comparison$method == "wald", ]
lrt_dat  <- comparison[comparison$method == "lrt", ]

plot(
  wald_dat$`clusters.subject`, wald_dat$estimate,
  type = "b", pch = 16, lty = 1,
  ylim = c(0, 1),
  xlab = "clusters.subject",
  ylab = "Power estimate",
  col = "steelblue"
)
lines(
  lrt_dat$`clusters.subject`, lrt_dat$estimate,
  type = "b", pch = 17, lty = 2,
  col = "firebrick"
)
legend(
  "bottomright",
  legend = c("Wald", "LRT"),
  col = c("steelblue", "firebrick"),
  lty = c(1, 2), pch = c(16, 17), bty = "n"
)

Interpretation notes:

Differences in estimate reflect inferential method sensitivity under the same DGP.
Compare failure_rate and singular_rate alongside power; higher failures can depress usable inference.
Keep nsim modest in examples, and increase in final study planning runs.
Prefer explicit null_formula documentation in reports for LRT reproducibility.

Binary outcomes (binomial GLMM)

MixPower supports binomial GLMM power analysis via the lme4::glmer() backend. This keeps design assumptions explicit and diagnostics visible.

Extremely large effect sizes can lead to quasi-separation. In those cases, glmer() may warn or return unstable estimates. MixPower records these as failed simulations (via NA p-values), preserving transparency.

d <- mp_design(clusters = list(subject = 40), trials_per_cell = 8)
a <- mp_assumptions(
  fixed_effects = list(`(Intercept)` = 0, condition = 0.5),
  residual_sd = 1,
  icc = list(subject = 0.4)
)

scn_bin <- mp_scenario_lme4_binomial(
  y ~ condition + (1 | subject),
  design = d,
  assumptions = a,
  test_method = "wald"
)

res_bin <- mp_power(scn_bin, nsim = 50, seed = 123)
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
summary(res_bin)
#> $power
#> [1] 0
#> 
#> $mcse
#> [1] 0
#> 
#> $ci
#> [1] 0 0
#> 
#> $diagnostics
#> $diagnostics$fail_rate
#> [1] 0
#> 
#> $diagnostics$singular_rate
#> [1] 0.14
#> 
#> 
#> $nsim
#> [1] 50
#> 
#> $alpha
#> [1] 0.05
#> 
#> $failure_policy
#> [1] "count_as_nondetect"
#> 
#> $conf_level
#> [1] 0.95

Sensitivity curve for binomial outcomes

sens_bin <- mp_sensitivity(
  scn_bin,
  vary = list(`fixed_effects.condition` = c(0.2, 0.4, 0.6)),
  nsim = 50,
  seed = 123
)
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')

plot(sens_bin)

sens_bin$results
#>   fixed_effects.condition estimate mcse conf_low conf_high failure_rate
#> 1                     0.2        0    0        0         0            0
#> 2                     0.4        0    0        0         0            0
#> 3                     0.6        0    0        0         0            0
#>   singular_rate n_effective nsim
#> 1          0.12          50   50
#> 2          0.10          50   50
#> 3          0.12          50   50

# Inspect failure_rate and singular_rate alongside power.
# Increase nsim for final study reporting.

Count outcomes (Poisson vs Negative Binomial)

MixPower supports count outcomes through Poisson and Negative Binomial GLMMs. Poisson is appropriate when variance ≈ mean; NB handles over-dispersion.

d <- mp_design(clusters = list(subject = 40), trials_per_cell = 8)
a <- mp_assumptions(
  fixed_effects = list(`(Intercept)` = 0, condition = 0.4),
  residual_sd = 1,
  icc = list(subject = 0.3)
)

scn_pois <- mp_scenario_lme4_poisson(
  y ~ condition + (1 | subject),
  design = d,
  assumptions = a,
  test_method = "wald"
)

res_pois <- mp_power(scn_pois, nsim = 50, seed = 123)
summary(res_pois)
#> $power
#> [1] 0
#> 
#> $mcse
#> [1] 0
#> 
#> $ci
#> [1] 0 0
#> 
#> $diagnostics
#> $diagnostics$fail_rate
#> [1] 0
#> 
#> $diagnostics$singular_rate
#> [1] 0
#> 
#> 
#> $nsim
#> [1] 50
#> 
#> $alpha
#> [1] 0.05
#> 
#> $failure_policy
#> [1] "count_as_nondetect"
#> 
#> $conf_level
#> [1] 0.95

a_nb <- a
a_nb$theta <- 1.5  # NB dispersion (size parameter)

scn_nb <- mp_scenario_lme4_nb(
  y ~ condition + (1 | subject),
  design = d,
  assumptions = a_nb,
  test_method = "wald"
)

res_nb <- mp_power(scn_nb, nsim = 50, seed = 123)
#> boundary (singular) fit: see help('isSingular')
#> 
#> boundary (singular) fit: see help('isSingular')
summary(res_nb)
#> $power
#> [1] 0
#> 
#> $mcse
#> [1] 0
#> 
#> $ci
#> [1] 0 0
#> 
#> $diagnostics
#> $diagnostics$fail_rate
#> [1] 0
#> 
#> $diagnostics$singular_rate
#> [1] 0.04
#> 
#> 
#> $nsim
#> [1] 50
#> 
#> $alpha
#> [1] 0.05
#> 
#> $failure_policy
#> [1] "count_as_nondetect"
#> 
#> $conf_level
#> [1] 0.95

Sensitivity curves (count outcomes)

sens_pois <- mp_sensitivity(
  scn_pois,
  vary = list(`fixed_effects.condition` = c(0.2, 0.4, 0.6)),
  nsim = 50,
  seed = 123
)

sens_nb <- mp_sensitivity(
  scn_nb,
  vary = list(`fixed_effects.condition` = c(0.2, 0.4, 0.6)),
  nsim = 50,
  seed = 123
)
#> boundary (singular) fit: see help('isSingular')
#> 
#> boundary (singular) fit: see help('isSingular')
#> 
#> boundary (singular) fit: see help('isSingular')

plot(sens_pois)

plot(sens_nb)

# Compare power estimates and failure/singularity rates across Poisson vs NB.
# Overdispersion can reduce power relative to Poisson.
# Increase nsim for final reports.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.