hello forum,
at first i want to appologize for may be asking some simple straighforward question, because i don't have much background in statistics.
let's say i have several hundreds records(islands), that are measured by certain variables (compactness of shape, elongation, fractal dimension of coastline, roundness etc. etc.).
then i made PCA analysis in R. and now i have just three variables (because 3 first PCA explained over 90% of whole variance).
now i want to measure how similair my islands are between each other.
how to do this? the most obvious solution that came into my mind is to measure Euclidean distance \|(PCA1i-PCA1j)^2 + (PCA2i-PCA2j)^2 + (PCA3i-PCA3j)^2
between pairs of islands.
may be there are some better ways to compare this similairity? will be gratefull for your advices.
best regards,
zmicier
at first i want to appologize for may be asking some simple straighforward question, because i don't have much background in statistics.
let's say i have several hundreds records(islands), that are measured by certain variables (compactness of shape, elongation, fractal dimension of coastline, roundness etc. etc.).
then i made PCA analysis in R. and now i have just three variables (because 3 first PCA explained over 90% of whole variance).
now i want to measure how similair my islands are between each other.
how to do this? the most obvious solution that came into my mind is to measure Euclidean distance \|(PCA1i-PCA1j)^2 + (PCA2i-PCA2j)^2 + (PCA3i-PCA3j)^2
between pairs of islands.
may be there are some better ways to compare this similairity? will be gratefull for your advices.
best regards,
zmicier