This module is used for investigating allometry in a multivariate morphometric data set. It expects a multivariate data set with variables (distance measurements) in columns, specimens in rows.
This method for investigating allometry in a multivariate data set is based on Jolicoeur (1963) with extensions by Kowalewski et al. (1997). The data are (automatically) log-transformed and subjected to PCA. The first principal component (PC1) is then regarded as a size axis (this is only valid if the variation accounted for by PC1 is large, say more than 80%). The allometric coefficient for each original variable is estimated by dividing the PC1 loading for that variable by the mean PC1 loading over all variables.
95% confidence intervals for the allometric coefficients are estimated by bootstrapping specimens. 2000 bootstrap replicates are made.
Missing data is supported by column average substitution.
References
Jolicoeur, P. 1963. The multivariate generalization of the allometry equation. Biometrics 19:497-499.
Kowalewski, M., E. Dyreson, J.D. Marcot, J.A. Vargas, K.W. Flessa & D.P. Hallmann. 1997. Phenetic discrimination of biometric simpletons: paleobiological implications of morphospecies in the lingulide brachiopod Glottidia. Paleobiology 23:444-469.