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Find r given inferential stat and descriptives for a within-subject design
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| library(faux) | |
| library(afex) | |
| f <- function(r, reported) { | |
| # simulate data for 3-level within design (change for your design) | |
| dat <- faux::sim_design( | |
| # you can add these parameters to f() arguments | |
| # if you need to check varying analyses | |
| n = 100, within = 3, | |
| mu = c(100, 105, 110), | |
| sd = c(10, 11, 12), | |
| r = r, # assume all r are the same | |
| empirical = TRUE, # paramaters describe sample, not population | |
| long = TRUE, # table format for analysis | |
| plot = FALSE | |
| ) | |
| # analyse data (change for your design/analysis) | |
| a <- afex::aov_ez( | |
| # generic column names from faux; | |
| # change if you customise the simulation code | |
| id = "id", | |
| dv = "y", | |
| within = "W1", | |
| data = dat) | |
| # get the relevant statistic | |
| stat <- a$anova_table$F | |
| # compare it to the reported stat | |
| abs(reported - stat) | |
| } | |
| # search over a plausible range for the reported stat | |
| op <- optimise(f, | |
| interval = c(-.99, .99), | |
| reported = 30.6) | |
| r <- op$minimum |
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