diveRsity (version 1.9.89)

diffCalc: A faster function for calculating genetic differentiation statistics

Description

This function allows the calculation of pairwise differentiation using a range of population statistics, such as Gst (Nei & Chesser, 1983), G'st (Hedrick, 2005), theta (Weir & Cockerham, 1984) and D (Jost, 2008). These parameters can also be calculated at the global and locus levels. Significance of differentiation can be assessed through the calculation of 95% confidence limits using a bias corrected bootstrapping method. The functionality diffCalc is similar to the fastDivPart function. However diffCalc is much faster and more memory efficient than fastDivPart. This function also only allows results to be written to text files rather than xlsx file (as in fastDivPart. No plotting options are provide in diffCalc.)

Usage

diffCalc(infile = NULL, outfile = NULL, fst = FALSE, pairwise = FALSE,  bs_locus = FALSE, bs_pairwise = FALSE, boots = NULL,  ci_type = "individuals", alpha = 0.05, para = FALSE)

Arguments

infile

Specifying the name of the `genepop' (Rousset, 2008) file from which the statistics are to be calculated This file can be in either the 3 digit of 2 digit format. See http://genepop.curtin.edu.au/help_input.html for detail on the genepop file format.

outfile

A character string specifying the name of the folder to which results should be written.

fst

A Logical argument indicating whether Weir & Cockerham's 1984 F-statistics should be calculated. NOTE - Calculating these statistics adds significant time to analysis when carrying out pairwise comparisons.

pairwise

A logical argument indicating whether standard pairwise diversity statistics should be calculated and returned as a diagonal matrix.

bs_locus

Gives users the option to calculate bias corrected 95% confidence intervals for locus statistic species.

bs_pairwise

Gives users the option to calculate bias corrected 95% confidence intervals for pairwise statistics.

boots

Specified the number of bootstraps for the calculation of 95% confidence intervals.

ci_type

A character string indicating whether bootstrapping should be carried out over individuals within samples (``individuals''.), or across loci (``loci'').

alpha

A numeric argument, specifying the alpha value used to estimate confidence limits for relevant parameters. Both the alpha/2 and the 1-(alpha/2) quantiles will be returned. Default value results in 95% CI.

para

A logical argument indicating whether computations should be carried out over multiple CPUs, if available.

Value

If outfile is given as a character string, all results will be written to text files. The files will be written to a directory under the current working directory. The number of files written depends on the options choose. As well as this a list object is returned to the R workspace, containing the following results:

References

Eddelbuettel, D., and Francois, R., (2011). Rcpp: Seamless R and C++ Integration. Journal of Statistical Software, 40(8), 1-18. URL http://www.jstatsoft.org/v40/i08/.

Hedrick, P., ``A standardized genetic differentiation measure,'' Evolution, vol. 59, no. 8, pp. 1633-1638, (2005).

Jost, L., ``G ST and its relatives do not measure differentiation,'' Molec- ular Ecology, vol. 17, no. 18, pp. 4015-4026, (2008).

Manly, F.J., ``Randomization, bootstrap and Monte Carlo methods in biology'', Chapman and Hall, London, 1997.

Meirmans, P.G., and Hedrick, P.W., (2011), Assessing population structure: Fst and related measures., Molecular Ecology, Vol. 11, pp5-18. doi: 10.1111/j.755-0998.2010.02927.x

Nei, M. and Chesser, R., ``Estimation of fixation indices and gene diver- sities,'' Ann. Hum. Genet, vol. 47, no. Pt 3, pp. 253-259, (1983).

R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.

Rousset, F., ``genepop'007: a complete re-implementation of the genepop software for Windows and Linux.,'' Molecular ecology resources, vol. 8, no. 1, pp. 103-6, (2008).

Weir, B.S. & Cockerham, C.C., Estimating F-Statistics, for the Analysis of Population Structure, Evolution, vol. 38, No. 6, pp. 1358-1370 (1984).

Examples

## Not run: 
# # simply use the following format to run the function
# library(diveRsity)
# data(Test_data)
# Test_data[is.na(Test_data)] <- ""
# 
# test_result <- diffCalc(infile = Test_data, outfile = "myresults",
#                         fst = TRUE, pairwise = TRUE, bs_locus = TRUE,
#                         bs_pairwise = TRUE, boots = 1000, para = TRUE)
# ## End(Not run)