Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, tidy = FALSE)
options(width = 80)
library(knitr)
library(rmarkdown)
library(rmcorr)
library(cocor)
## ---- eval = FALSE------------------------------------------------------------
# #Install cocor
# install.packages("cocor")
# require(cocor)
## -----------------------------------------------------------------------------
#1) Run rmcorr on two different datasets
model1.marusich2016_exp2 <- rmcorr(Pair, HVT_capture, MARS, marusich2016_exp2)
model1.marusich2016_exp2
model2.gilden2010 <- rmcorr(sub, rt, acc, gilden2010 )
model2.gilden2010
#2) Extract relevant parameters
#Model 1
rmcorr1 <- model1.marusich2016_exp2$r
rmcorr1
n1 <- model1.marusich2016_exp2$df + 2 #note the same kludge as power above
n1 #this is the effective sample size
#Model 2
rmcorr2 <- model2.gilden2010$r
rmcorr2
n2 <- model2.gilden2010$df + 2
n2
#3) Compare the two indendent rmcorr coefficients
cocor.indep.groups(rmcorr1, rmcorr2, n1, n2,
var.labels = c(model1.marusich2016_exp2$var[2:3],
model2.gilden2010$vars[2:3]))
## -----------------------------------------------------------------------------
variables.overlap<- c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW")
dist_rmc_mat_overlap <- rmcorr_mat(participant = Subject,
variables = variables.overlap,
dataset = twedt_dist_measures,
CI.level = 0.95)
#dist_rmc_mat_action$summary
#Use summary component
model1.bwa.bwt <- dist_rmc_mat_overlap$summary[1,]
model2.bwa.tri <- dist_rmc_mat_overlap$summary[2,]
model3.bwt.tri <- dist_rmc_mat_overlap$summary[3,]
r.jk <- model1.bwa.bwt$rmcorr.r
r.jh <- model2.bwa.tri$rmcorr.r #overlap
r.kh <- model3.bwt.tri$rmcorr.r
#Since there is missing data, the results are unbalanced. We use the average effective sample size.
n <- mean(dist_rmc_mat_overlap$summary$effective.N)
cocor.dep.groups.overlap(r.jk,
r.jh,
r.kh,
n,
alternative = "two.sided",
test = "all",
var.labels = variables.overlap) #Same as variables used in rmcorr_mat()
## -----------------------------------------------------------------------------
variables.nonoverlap <- c("Blindwalk Away",
"Blindwalk Toward",
"Verbal",
"Visual matching")
dist_rmc_mat_nonoverlap <- rmcorr_mat(participant = Subject,
variables = variables.nonoverlap,
dataset = twedt_dist_measures,
CI.level = 0.95)
dist_rmc_mat_nonoverlap$summary
#Use summary component
model1.bwa.bwt <- dist_rmc_mat_nonoverlap$summary[1,]
model2.verb.vis <- dist_rmc_mat_nonoverlap$summary[6,]
model3.bwa.verb <- dist_rmc_mat_nonoverlap$summary[2,]
model4.bwa.vis <- dist_rmc_mat_nonoverlap$summary[3,]
model5.bwt.verb <- dist_rmc_mat_nonoverlap$summary[4,]
model6.bwt.vis <- dist_rmc_mat_nonoverlap$summary[5,]
#Cheatsheet
#j = bwa
#k = bwt
#h = verb
#m = vis
r.jk <- model1.bwa.bwt$rmcorr.r #Action measures
r.hm <- model2.verb.vis$rmcorr.r #Direct measures
r.jh <- model3.bwa.verb$rmcorr.r #bwa ~ verb
r.jm <- model4.bwa.vis$rmcorr.r #bwa ~ vis
r.kh <- model5.bwt.verb$rmcorr.r #bwt ~ verb
r.km <- model6.bwt.vis$rmcorr.r #bwt ~ vis
#Since there is missing data, we use the average effective sample size.
n <- round(mean(dist_rmc_mat_nonoverlap$summary$effective.N), digits = 0) + 2
cocor.dep.groups.nonoverlap(r.jk,
r.hm,
r.jh,
r.jm,
r.kh,
r.km,
n,
alternative = "two.sided",
test = "all",
var.labels = variables.nonoverlap) #Same as variables used in rmcorr_mat()
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