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(corrplot)
## ---- eval = FALSE------------------------------------------------------------
# #Install corrplot
# install.packages("corrplot")
# require(corrplot)
## -----------------------------------------------------------------------------
dist_rmc_mat <- rmcorr_mat(participant = Subject,
variables = c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW",
"Verbal",
"Visual matching"),
dataset = twedt_dist_measures,
CI.level = 0.95)
corrplot(dist_rmc_mat$matrix)
## -----------------------------------------------------------------------------
#Number of models being plotted
n.models <- length(dist_rmc_mat$models)
#Change graphing parameters to plot side-by-side
#with narrower margins
par(mfrow = c(3,4),
mar = c(2.75, 2.4, 2.4, 1.4))
for (i in 1:n.models) {
plot(dist_rmc_mat$models[[i]])
}
#Reset graphing parameters
#dev.off()
## -----------------------------------------------------------------------------
#Third component: Summary
dist_rmc_mat$summary
#p-values only
dist_rmc_mat$summary$p.vals
#Vector of original, unadjusted p-values for all 10 comparisons
p.vals <- dist_rmc_mat$summary$p.vals
p.vals.bonferroni <- p.adjust(p.vals,
method = "bonferroni",
n = length(p.vals))
p.vals.fdr <- p.adjust(p.vals,
method = "fdr",
n = length(p.vals))
#All p-values together
all.pvals <- cbind(p.vals, p.vals.bonferroni, p.vals.fdr)
colnames(all.pvals) <- c("Unadjusted", "Bonferroni", "fdr")
round(all.pvals, digits = 5)
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