Compute proportions of variance explained by temporal distortion at each locus

prop_variance returns a vector of squared correlation coefficients representing the proportion of variation captured by the specified covariance model at each locus.

prop_variance(model, Y)

Arguments

model	an object of class 'tfa' adjusted with `tfa()`.
Y	an nxp numeric matrix containing genetic information for n individuals recorded in p columns. Genetic information could be encoded by any numeric value, not necessarily an integer value. Missing data are not allowed. The matrix should have the same number of rows as in the adjusted model.

Value

A vector of probabilities representing the proportion of variation captured the specified covariance model at each locus.

Details

This function requires that a preliminary model has been adjusted with K factors, where K is the number of source populations.

References

François, O., Liégeois, S., Demaille, B., Jay, F. (2019). Inference of population genetic structure from temporal samples of DNA. bioRxiv, 801324. https://www.biorxiv.org/content/10.1101/801324v3

François, O., Jay, F. (2020). Factor analysis of ancient population genomic samples. Under review.

Examples

library(tfa)

# Ancient DNA from Bronze Age Great Britain samples
data(england_ba)

attach(England_BA)

# Remove HG from Serbia to keep k = 2 ancestral populations
age2 <- age[meta$Group.ID != "Serbia_HG"]
geno2 <- genotype[meta$Group.ID != "Serbia_HG",]

# Adjust an FA model
mod  <- tfa(age2,
            geno2,
            k = 2,
            lambda = 5e-2,
            center = TRUE)

r_squared <- prop_variance(mod, geno2)
summary(r_squared)
#>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
#> 0.0000017 0.0085017 0.0205287 0.0288081 0.0403540 0.3205529 
hist(r_squared, col = "darkviolet")
detach(England_BA)
# rm(list = ls())