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Calculate PCO loadings

Source: R/PCOload.R
PCOload.Rd

PCOload() computes the loadings for each principal coordinates (PCOs) analysis score, which are the correlations between the features used to compute the PCOs and the PCOs.

Usage

PCOload(x, scores)

# S3 method for class 'regions_pco_load'
plot(x, ...)

Arguments

x

for PCOload(), a regions_pco object; the output of a call to svdPCO(). For plot(), a regions_pco_load object.

scores

a numeric vector containing the indices of the desired scores.

...

ignored.

Value

PCOload() returns a regions_pco_load object, which is a matrix with a column for each PCO score requested and a row for each variable in the original dataset; values indicate the correlation between each variable and each PCO score. plot() returns a ggplot object, which can be manipulated using ggplot2 syntax, that displays the loadings visually.

Details

the loadings for a constructed variable, vert.size, are also computed and displayed. This is computed as the mean of the features for each vertebra.

See also

svdPCO() for computing the PCOs; plot.regions_pco() for visualizing the correlations between PCO scores.

Examples

data("alligator")

alligator_data <- process_measurements(alligator,
                                       pos = "Vertebra")

# Compute PCOs
alligator_PCO <- svdPCO(alligator_data)

# Compute PCO loadings
loadings <- PCOload(alligator_PCO, scores = 1:4)
loadings
#> - PCO loadings:
#> 
#>           PCO.1   PCO.2    PCO.3     PCO.4
#> CL       0.9214  0.2850 -0.17096  0.000379
#> Chpost  -0.2163 -0.5189 -0.74326  0.240596
#> Cwpost   0.4554  0.1168  0.75907  0.175309
#> Chant    0.7288 -0.2063 -0.53556 -0.028160
#> Cwant    0.5402 -0.3457  0.57294  0.238653
#> ArchH   -0.2635 -0.9168  0.05630 -0.065128
#> ArchW   -0.4975 -0.4906  0.36905  0.357137
#> Mid.LW   0.9664 -0.1441  0.15972  0.021917
#> NSL      0.9580 -0.0427  0.09103 -0.127765
#> NSH      0.0773 -0.8326  0.37861  0.212040
#> totH    -0.4753 -0.8241 -0.09257  0.121630
#> totW     0.9532 -0.0821 -0.20079 -0.150651
#> PreZW    0.9248 -0.0133  0.25234  0.171984
#> InterZL  0.9406  0.1956 -0.00413  0.114973
#> TPL      0.7768 -0.0630 -0.50157  0.132715
#> NSA     -0.0383 -0.7876 -0.38165 -0.145363
#> PreZA    0.8968  0.0704  0.15874  0.304670
#> TPDV    -0.7728  0.5232 -0.02010  0.189908
#> TPAP     0.4419 -0.6879  0.14413 -0.418512
#> 
#>  - Corr w/ vertebra size:
#> 
#>           PCO.1  PCO.2   PCO.3  PCO.4
#> vert.size 0.952 -0.207 -0.0441 0.0624

# Plot loadings
plot(loadings)