Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup, eval = FALSE------------------------------------------------------
# install.packages("mc.heterogeneity")
## ---- echo=FALSE--------------------------------------------------------------
library("mc.heterogeneity")
## -----------------------------------------------------------------------------
selfconcept <- mc.heterogeneity:::selfconcept
## -----------------------------------------------------------------------------
# n1 and n2 are lists of samples sizes in two groups
n1 <- selfconcept$n1
n2 <- selfconcept$n2
# g is a list of effect sizes
g <- selfconcept$g
## -----------------------------------------------------------------------------
cm <- (1-3/(4*(n1+n2-2)-1)) #correct factor to compensate for small sample bias (Hedges, 1981)
d <- cm*g
## ---- eval=FALSE, results = 'hide'--------------------------------------------
# mc.run <- mc.d(n1, n2, est = d, model = 'random', p_cut = 0.05)
## ---- eval=FALSE, results = 'hide'--------------------------------------------
# mc.run2 <- mc.d(n1, n2, est = g, model = 'random', adjust = TRUE, p_cut = 0.05)
## ---- eval=FALSE--------------------------------------------------------------
# mc.run
# #> stat p_value Heterogeneity
# #> Qtest 23.391659 0.136929 n.s
# #> mc.ML 1.610239 0.051200 n.s
# #> mc.REML 2.037578 0.053100 n.s
## ---- eval=FALSE--------------------------------------------------------------
# mc.run2
# #> stat p_value Heterogeneity
# #> Qtest 23.391659 0.136929 n.s
# #> mc.ML 1.610239 0.051200 n.s
# #> mc.REML 2.037578 0.053100 n.s
## -----------------------------------------------------------------------------
hypo_moder <- mc.heterogeneity:::hypo_moder
## -----------------------------------------------------------------------------
head(hypo_moder)
## ---- eval=FALSE, results = 'hide'--------------------------------------------
# mc.run3 <- mc.d(n1 = hypo_moder$n1,
# n2 = hypo_moder$n2,
# est = hypo_moder$d,
# model = 'mixed',
# mods = cbind(hypo_moder$cov.z1, hypo_moder$cov.z2, hypo_moder$cov.z3),
# p_cut = 0.05)
## ---- eval=FALSE--------------------------------------------------------------
# mc.run3
# #> stat p_value Heterogeneity
# #> Qtest 31.849952 0.0008061727 sig
# #> mc.ML 5.187700 0.0004000000 sig
# #> mc.REML 9.283428 0.0004000000 sig
## -----------------------------------------------------------------------------
sensation <- mc.heterogeneity:::sensation
## -----------------------------------------------------------------------------
# n is a list of samples sizes
n <- sensation$n
# Pearson's correlation
r <- sensation$r
# Fisher's Transformation
z <- 1/2*log((1+r)/(1-r))
## ---- eval=FALSE, results = 'hide'--------------------------------------------
# mc.run <- mc.fcor(n, z, model = 'random', p_cut = 0.05)
## ---- eval=FALSE--------------------------------------------------------------
# mc.run
# #> stat p_value Heterogeneity
# #> Qtest 29.060970 0.00385868 sig
# #> mc.ML 5.204299 0.00420000 sig
# #> mc.REML 6.133111 0.00400000 sig
## -----------------------------------------------------------------------------
library(HSAUR3)
data(smoking)
## -----------------------------------------------------------------------------
# Y1: receive treatment; Y2: stop smoking
n_00 <- smoking$tc - smoking$qc # not receive treatement yet not stop smoking
n_01 <- smoking$qc # not receive treatement but stop smoking
n_10 <- smoking$tt - smoking$qt # receive treatement but not stop smoking
n_11 <- smoking$qt # receive treatement and stop smoking
## -----------------------------------------------------------------------------
lnOR <- log(n_11*n_00/n_01/n_10)
lnOR
## ---- eval=FALSE, results = 'hide'--------------------------------------------
# mc.run <- mc.lnOR(n_00, n_01, n_10, n_11, model = 'random', p_cut = 0.05)
## ---- eval=FALSE--------------------------------------------------------------
# mc.run
# #> stat p_value Heterogeneity
# #> Qtest 34.873957 0.09050857 n.s
# #> mc.ML 2.557171 0.02160000 sig
# #> mc.REML 3.071329 0.02240000 sig
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