Analitica (version 2.2.0)

Posthoc_planner: Post Hoc Planner for FWER and Test Recommendation v1.6

Description

One-shot planner for factor or cell comparisons, reporting m, FWER, suggested adjustments (Bonferroni/Sidak) and a post hoc recommendation (Holm, Tukey, Duncan, Gabriel, Scheffe, SNK, etc.) before testing.

Usage

Posthoc_planner(
  model,
  compare = NULL,
  alpha = 0.05,
  scope = c("factor", "cells"),
  equal_var = TRUE,
  unequal_n = FALSE,
  independence = TRUE,
  liberal_ok = FALSE,
  orientation = c("rows", "cols"),
  digits = 4,
  percent_digits = 1,
  observed_cells = TRUE
)

Value

data.frame. - orientation="rows": first column "Metric", rest columns are units (factor/cells). - orientation="cols": one row per unit, metrics as columns. Includes: g levels, m comparisons, global alpha, Bonferroni/Sidak alphas, FWERs (under independence), "Suggested p-value adjustment" and "Post hoc suggestion".

Arguments

model

aov or lm object (complete model). Data are reconstructed with model.frame().

compare

Character with the name(s) of the factor(s) to compare: - One name: main effect. - Several names: if scope="cells" compares A:B:... cells; if scope="factor", reports each factor. If omitted, uses all factors when scope="factor", or the first factor when scope="cells".

alpha

Overall significance level (FWER target), default 0.05.

scope

"factor" compares each factor separately; "cells" compares interaction cells.

equal_var

Logical; assume homoscedasticity (default TRUE).

unequal_n

Logical; expect moderate imbalance of group sizes (default FALSE).

independence

Logical; if TRUE reports FWER "under independence" (default TRUE).

liberal_ok

Logical; allows more liberal suggestions (LSD/Duncan/SNK) (default FALSE).

orientation

"rows" (metrics as rows, default) or "cols".

digits

Decimal places for numeric output, default 4.

percent_digits

Decimal places for percentages, default 1.

observed_cells

Logical; in scope="cells", count only observed cells (drop NA). Default TRUE.

References

#' Bonferroni, C. (1936). *Teoria statistica delle classi e calcolo delle probabilità*. Pubblicazioni del R. Istituto Superiore di Scienze Economiche e Commerciali di Firenze.

Fisher, R. A. (1935). *The design of experiments*. Oliver & Boyd.

Duncan, D. B. (1955). Multiple range and multiple F tests. *Biometrics, 11*(1), 1–42.

Gabriel, K. R. (1978). A simple method of multiple comparisons of means. *Journal of the American Statistical Association, 73*(364), 724–729.

Games, P. A., & Howell, J. F. (1976). Pairwise multiple comparison procedures with unequal n’s and/or variances: A Monte Carlo study. *Journal of Educational Statistics, 1*(2), 113–125.

Holm, S. (1979). A simple sequentially rejective multiple test procedure. *Scandinavian Journal of Statistics, 6*(2), 65–70.

Newman, D. (1939). The distribution of range in samples from a normal population, expressed in terms of an independent estimate of standard deviation. *Biometrika, 31*(1/2), 20–36.

Scheffé, H. (1953). A method for judging all contrasts in the analysis of variance. *Biometrika, 40*(1–2), 87–104.

Šidák, Z. (1967). Rectangular confidence regions for the means of multivariate normal distributions. *Journal of the American Statistical Association, 62*(318), 626–633.

Tukey, J. W. (1949). Comparing individual means in the analysis of variance. *Biometrics, 5*(2), 99–114.

Examples

## =========================
## Ejemplo 1: One-way ANOVA
## =========================
# Datos: PlantGrowth (3 grupos balanceados)
data(PlantGrowth)
m1 <- aov(weight ~ group, data = PlantGrowth)
# Comparar por factor (default scope="factor")
Posthoc_planner(m1)
# Variante: salida por columnas
Posthoc_planner(m1, orientation = "cols")
# Variante: alpha más estricto
Posthoc_planner(m1, alpha = 0.01)

## ==============================================
## Ejemplo 2: Dos factores y comparación de celdas
## ==============================================
# Datos: ToothGrowth (suplemento x dosis)
data(ToothGrowth)
TG <- ToothGrowth
TG$dose <- factor(TG$dose)  # tratar "dose" como factor
m2 <- aov(len ~ supp * dose, data = TG)
# scope="cells" compara celdas de la interacción (supp:dose)
Posthoc_planner(
  m2,
compare = c("supp","dose"),
scope   = "cells",        # comparar celdas
observed_cells = TRUE     # contar solo celdas observadas (default)
)
# También puedes pedir el resumen por factor dentro del mismo modelo
Posthoc_planner(
m2,
compare = c("supp","dose"),
scope   = "factor"        # reporte por cada factor por separado
)