prComp {GCDkit} | R Documentation |
Performs principal components analysis (scaled variables, covariance or correlation matrix) and plots a biplot (Gabriel, 1971).
prComp(comp.data=NULL,use.cov=FALSE,scale=TRUE,GUI=FALSE)
comp.data |
a numerical matrix; the data to be normalized.
Or just names of variables in the data matrix ' |
use.cov |
logical; should be the covariance matrix used instead of correlation matrix? |
scale |
logical; the scalings applied to each variable. |
GUI |
logical; is the function called from a menu (GUI)? |
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).
If called from menu (GUI version), a list of major elements
(SiO2, TiO2, Al2O3, FeOt, MnO, MgO, CaO, Na2O, K2O) is assumed as a default, but
different variables can be specified by the function
'selectColumnsLabels
'.
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.
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.
Vojtěch Janoušek, 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 KR (1971) The biplot graphical display of matrices with application to principal component analysis. Biometrika 58: 453-467
The compositional data should be first transformed to centred-log-ratios (clr) using the
function 'clr.trans
'. See example.
For further details on the used principal components algorithm and biplots,
see the R manual entries of 'princomp
' and 'biplot.princomp
'.
data(sazava) accessVar("sazava") ox<-c("SiO2","Al2O3","FeOt","MgO","CaO") clr.trans(ox) addResults() # Needed to append the clr-transformed data to the matrix 'WR' pr.comp.clr(ox)