Correlation Matrix using rmcorr_mat

Running Examples Requires corrplot (Wei and Simko 2021)

#Install corrplot
install.packages("corrplot")
require(corrplot)

Plotting a Correlation Matrix

The output from rmcorr_mat can be used be used to plot a correlation matrix.

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$matrix) Plotting Multiple Models The output can also be used to plot multiple models side-by-side. #Number of models being plotted n.models <- length(dist_rmc_mat$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$models[[i]]) } #Reset graphing parameters #dev.off() Adjusting for Multiple Comparisons The third component of the output from rmcorr_mat() contains a summary of results. Using the summary component, we demonstrate adjusting for multiple comparisons using two methods: the Bonferroni correction and the False Discovery Rate (FDR). This example also compares the unadjusted p-values to both adjustment methods. Because most of the unadjusted p-values are quite small, many of the adjusted p-values tend to be similar to the unadjusted ones and the two adjustment methods also tend to produce similar p-values. #Third component: Summary dist_rmc_mat$summary
#>            measure1         measure2  df  rmcorr.r    lowerCI   upperCI
#> 1    Blindwalk Away Blindwalk Toward 175 0.8065821 0.74808182 0.8526427
#> 2    Blindwalk Away  Triangulated BW 174 0.2382857 0.09366711 0.3730565
#> 3    Blindwalk Away           Verbal 175 0.7355813 0.65965209 0.7966468
#> 4    Blindwalk Away  Visual matching 174 0.7758245 0.70930425 0.8286489
#> 5  Blindwalk Toward  Triangulated BW 176 0.2254866 0.08109132 0.3606114
#> 6  Blindwalk Toward           Verbal 177 0.7160551 0.63619996 0.7807308
#> 7  Blindwalk Toward  Visual matching 177 0.7575109 0.68718940 0.8137687
#> 8   Triangulated BW           Verbal 178 0.1835838 0.03835025 0.3212218
#> 9   Triangulated BW  Visual matching 177 0.2537431 0.11120971 0.3860478
#> 10           Verbal  Visual matching 179 0.7341831 0.65888265 0.7949162
#>          p.vals effective.N
#> 1  8.228992e-42         177
#> 2  1.449081e-03         176
#> 3  2.056415e-31         177
#> 4  1.226384e-36         176
#> 5  2.476132e-03         178
#> 6  1.937983e-29         179
#> 7  1.302874e-34         179
#> 8  1.362964e-02         180
#> 9  6.095365e-04         179
#> 10 6.400493e-32         181

#p-values only
dist_rmc_mat$summary$p.vals
#>  [1] 8.228992e-42 1.449081e-03 2.056415e-31 1.226384e-36 2.476132e-03
#>  [6] 1.937983e-29 1.302874e-34 1.362964e-02 6.095365e-04 6.400493e-32

#Vector of original, unadjusted p-values for all 10 comparisons
p.vals <- dist_rmc_mat$summary$p.vals

method = "bonferroni",
n = length(p.vals))

method = "fdr",
n = length(p.vals))

#All p-values together
all.pvals <- cbind(p.vals, p.vals.bonferroni, p.vals.fdr)
#> [10,]    0.00000    0.00000 0.00000