prComp {GCDkit} | R Documentation |
Performs principal components analysis (scaled variables, covariance or correlation matrix) and plots a biplot (Gabriel, 1971).
prComp(elems = "SiO2,TiO2,Al2O3,FeOt,MnO,MgO,CaO,Na2O,K2O",...)
elems |
numerical columns to be used for principal components analysis, typically major elements |
... |
additional parameters |
Biplot aims to represent both the observations and variables of a data matrix on a single bivariate plot (Gabriel, 1971; Buccianti & Peccerillo, 1999).
In the biplots, the length of the individual arrows is proportional to the relative variation of each variable. A comparable direction of two arrows implies that both variables are positively correlated; the opposite one indicates a strong negative correlation. When two links are perpendicular it indicates independence of the two variables (Buccianti & Peccerillo, 1999).
The samples can be selected based on combination of three searching mechanisms
(by sample name/label, range or a Boolean condition) - see
selectSamples
for details.
Even though a list of major elements is assumed as a default, different
variables can be specified by the function 'selectColumnsLabels
'.
Vector of the scores of the supplied data on the principal components
is stored in a variable 'results'.
Returns invisibly the complete output from the underlying function
'princomp
'.
Names of existing numeric data columns and not formulae involving these can be handled at this stage. Only complete cases are used for the principal components analysis.
Vojtech Janousek, vojtech.janousek@geology.cz
Buccianti A & Peccerillo A (1999) The complex nature of potassic and ultrapotassic magmatism in Central-Southern Italy: a multivariate analysis of major element data. In: Lippard S J, Naess A, Sinding-Larsen R (eds) Proceedings of the 5th Annual Conference of the International Association for Mathematical Geology. Tapir, Trondheim, p. 145-150
Gabriel K R (1971) The biplot graphical display of matrices with application to principal component analysis. Biometrika 58: 453-467
For further details on the used principal components algorithm and biplots,
see the R manual entries of 'princomp
' and 'biplot.princomp
'.