diffCalc: A faster function for calculating genetic differentiation...
In diveRsity: A Comprehensive, General Purpose Population Genetics Analysis Package

Description Usage Arguments Value Author(s) References Examples

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.)

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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)

`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.

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:

`std_stats`	A `data.frame`, containing locus estimates for Gst, G'st, G”st, D (and Weir and Cockerham's F-statistics, if `fst = TRUE`). The last row of this dataframe contains the global estimate for each statistic across all samples and loci.
`global_bs`	If `bs_locus = TRUE`, this object is returned. It is a `dataframe` containing global estimates and lower and upper 95% CIs for all relevant statistics.
`bs_locus`	If `bs_locus = TRUE`, this object is returned. It is a list of either 4 or 7 dataframes the number of which depend on `fst`. Each dataframe contains the locus statistics estimate across all samples along with lower and upper 95% confidence limits.
`pw_locus`	If `pairwise = TRUE`, this list of dataframes is returned. The list contains a dataframe for each relevant statistics, where rows correspond to loci and columns correspond to all possible pairwise combinations of samples. If `outfile` is provided, these results are also written to file.
`pairwise`	If `pairwise = TRUE`, this object is returned. It is a list of either 3 or 4 matrices (depending on the value of `fst`) containing pairwise estimate of relevant statistics.
`bs_pairwise`	A list of either 4 or 5 (depending on the value of `fst`) containing pairwise estimates and lower and upper 95% confidence intervals for all relevant statistics.

Kevin Keenan <kkeenan02@qub.ac.uk>

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).

## 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)