inCalc: A function to calculate locus informative for the inference...
In diveRsity: A Comprehensive, General Purpose Population Genetics Analysis Package

Description Usage Arguments Details Value Note Author(s) References Examples

inCalc allows the calculation of locus informativeness for ancestry (In), (Rosenberg et al., 2003), both across all population samples under consideration and for all pairwise combinations of population samples. These data can be bootstrapped using the same procedure as above, to obtain 95% confidence intervals.

1 2	inCalc(infile = NULL, outfile = NULL, pairwise = FALSE, xlsx = FALSE, boots = NULL, 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`	Allows users to specify a prefix for an output folder. Name must a character string enclosed in either “” or ''.
`pairwise`	Specified whether pairwise I\_n should be calculated.
`xlsx`	A logical argument indicating whether results should be written to an xlsx file. If `xlsx = FALSE` (default), results will be written to text files.
`boots`	Determines the number of bootstrap iterations to be carried out. The default value is `boots = 0`, this is only valid when all bootstrap options are false.
`para`	Allows for parallel computation of pairwise locus In. The number of available core is automatically detected if `para = TRUE`.

All results will be written to a user defined folder ("working\_directory/outfile"). The format of outputs will vary depending value of the xlsx argument. If xlsx = TRUE, results will be written to a .xlsx workbook using the xlsx package. If xlsx = FALSE, results will be written to .txt files.

inCalc return a list object to the R workspace, with elements described below. In addition to this results can be optionally written to file using the outfile argument. If xlsx = TRUE results will be written to a multi-sheet xlsx file. If xlsx = FALSE results are written to multiple text file, the number of which depends on the function arguments used.

`global`	A `data.frame` containing the In values for each locus, calculated across all samples in `infile`. If `boots` is an integer greater than 0, this data.frame will also contain lower and upper 95% confidence limits for each locus.
`pairwise`	A `data.frame` containing the pairwise locus In values for all possible pairwise population comparisons. This object is returned when `boots` is an integer greater than 0.
`lower_CI`	If `pairwise = TRUE` and `boots` is an integer greater than 0, `lower_CI` is returned. It is a `data.frame` containing the lower 95% confidence limit for the corresponding pairwise estimate in the `pairwise` data.
`upper_CI`	If `pairwise = TRUE` and `boots` is an integer greater than 0, `upper_CI` is returned. It is a `data.frame` containing the upper 95% confidence limit for the corresponding pairwise estimate in the `pairwise` data.

Since version 1.9.0, the speed of this function has been greatly improved. Users can expect up to x10 speed up on previous versions. The output data structure is also slightly different from v1.9.0 onwards.

Kevin Keenan <kkeenan02@qub.ac.uk>

Dragulescu, A.D., “xlsx: Read, write, formal Excel 2007 and Excel 97/2000/xp/2003 files”, R package version 0.4.2, url:http://CRAN.R-project.org/package=xlsx, (2012).

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

Rosenberg, N., Li, L., Ward, R., and Pritchard, J., “Informativeness of genetic markers for inference of ancestry.,” American Journal of Human Genetics, vol. 73, no. 6, pp. 1402-22, (2003).

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).

## Not run: 
# To run an example use the following format
library(diveRsity)
data(Test_data)
Test_data[is.na(Test_data)] <- ""

test_results<-inCalc(infile = Test_data, outfile = 'out', pairwise = TRUE,
                     xlsx = FALSE, boots = 1000, para = TRUE)

## End(Not run)