Function reference • mulgar

Data

Sample data sets for examples.

aflw: AFLW player statistics

anomaly1 anomaly2 anomaly3 anomaly4 anomaly5: Data sets with anomalies

assoc1 assoc2 assoc3: Data sets with different types of association

box: 3D plane in 5D

bushfires: Australian bushfires 2019-2020

c1 c2 c3 c4 c5 c6 c7: Cluster challenge data sets

clusters_nonlin: Four unusually shaped clusters in 4D

clusters: Three clusters in 5D

multicluster: Multiple clusters of different sizes, shapes and distance from each other

pisa: PISA scores

plane_nonlin: Non-linear relationship in 5D

plane: 2D plane in 5D

simple_clusters: Two clusters in 2D

sketches_test: Images of sketches for testing

sketches_train: Images of sketches for training

Utility

Useful functions

calc_mv_dist(): Compute Mahalanobis distances between all pairs of observations

calc_norm(): Calculate the norm of a vector

convert_proj_tibble(): This function turns a projection sequence into a tibble

gen_vc_ellipse(): Generate points on the surface of an ellipse

gen_xvar_ellipse(): Ellipse matching data center and variance

norm_vec(): Normalise a vector to have length 1

pooled_vc(): Compute pooled variance-covariance matrix

rmvn(): Generate a sample from a multivariate normal

ggslice(): Generate an axis-parallel slice display

ggslice_projection(): Generate slice display

Principal Component Analysis

Useful functions for PCA

ggscree(): This function produces a simple scree plot

pca_model(): Create wire frame of PCA model

Clustering

Useful functions for cluster analysis

ggmcbic(): Produces an mclust summary plot with ggplot

hierfly(): Generate a dendrogram to be added to data

mc_ellipse(): Computes the ellipses of an mclust model

som_model(): Process the output from SOM to display the map and data