gfitpca {abc}  R Documentation 
Goodness of fit with principal component analysis
Description
Perform a priori goodness of fit using the two first components obtained with PCA.
Usage
gfitpca(target, sumstat, index, cprob=0.1, xlim=NULL, ylim=NULL, ...)
Arguments
target 
a data frame or vector of the observed summary statistic. 
sumstat 
a matrix or data frame of the simulated summary statistics. 
index 
a vector of models names. It must be character and have the same
length as the number of row in 
cprob 

xlim , ylim 
optional, numeric vectors of length 2, giving the x and y coordinates ranges. 
... 
other parameters passed to 
Details
The function performs PCA using the a priori simulated summary
statistics. It displays envelopes containing 1hprob
percent of
the simulations.
The projection of the observed summary statistics is displayed in
order to check if they are contained or not in the envelopes.
If the projection lies outside the envelope of a given model, it is
an indication of poor fit.
Author(s)
Louisiane Lemaire and Michael Blum
See Also
abc
, plot.gfit
, summary.gfit
,
gfit
Examples
## human demographic history
require(abc.data)
data(human)
## five R objects are loaded. See ?human and vignette("abc") for details.
## Perform a priori goodness of fit for 3 different demographic models
## The envelopes containing 90% of the simulations are displayed.
## For the European data, a reasonable fit is only provided by the
## bottleneck model.
## The number of simulations is reduced to improve speed (do not do that
## with your own data)
index<c(1:5000,50001:55000,100001:105000)
gfitpca(target=stat.voight["italian",], sumstat=stat.3pops.sim[index,],
index=models[index], cprob=0.1)