pair.pops.D(filename, object=FALSE, format.table=TRUE, p.Val=TRUE, bt=1000)
pair.pops.Dest(filename, object=FALSE, format.table=TRUE, p.Val=TRUE, bt=1000)
pair.pops.Dest.Chao(filename, object=FALSE, format.table=TRUE, p.Val=TRUE, bt=1000)
pair.pops.Gst(filename, object=FALSE, format.table=TRUE, p.Val=TRUE,bt=1000)
pair.pops.Gst.est(filename, object=FALSE, format.table=TRUE, p.Val=TRUE, bt=1000)
When p.Val is set as TRUE, an INTERMEDIATE RESULT is printed after each
pairwise comparison and the according data tables are saved in
'.txt'-format (space-delimited) to the actual working directory. Its
location can be requested by typing 'getwd()' and changed by using the
function setwd. You will be informed about the filenames
under which the data tables have been saved automatically. The name
includes the argument 'filename' and the actual date. The next
INTERMEDIATE RESULT is printed to the same file, separated from the
preceding result by a row of column names. When the whole analysis is
completed, the END RESULT containing the information of all the
INTERMEDIATE RESULTs in a single data frame is printed and saved to the
same file, separated from the preceding intermediate results by a row of
column names.
Appending the results one below the other avoids loss of data. But you have to be careful. If you want to work with the INTERMEDIATE RESULTs that have already been saved, it is recommended to copy the respective file and work with the copy. Otherwise, problems can arise, when you work with the original file and R tries to write new results to it. This could cause the analysis to interrupt.
If an analysis is carried out more than once at the same day, the results will all be found, one written below the other, separated by a row of column names in the same file (if the working directory wasn't changed).
If an analysis runs on more than one day, the INTERMEDIATE RESULTs will be saved in different files, according to the date, they had been analysed. But all the INTERMEDIATE RESULTs will be included in the END RESULT that is finally saved.
To the contrary, when p.Val is set as FALSE, you are only given one data table containing all results at once.
inputformat. Format 2 equals the input of the function
inputformat. Please refer to this description file for
details. If format 1 is used, the argument 'format.table' has to be set
as FALSE. Using format 2, the argument 'format.table' has to be set as
TRUE (default). In this case, the data table is automatically
transformed to format 1.In the data table that will be returned at the end, the loci will be sorted alphabetically and numerically if numbers are included in.
The functions described here, need format (1) to calculate the
measurements of genetic differentiation. If the argument 'filename' is
of this format, the second argument 'format.table' can be set as FALSE
(the default). Data tables of format (2) must be transformed to format
(1). This can be done automatically by setting the argument
'format.table' as TRUE (format.table=TRUE). The data table is then
transformed by the function inputformat.
The bootstrap 95% confidence limits are obtained automatically when the
argument p.Val is set as TRUE (default). For further details of the
bootstrapping and the calculation of the confidence limits, see the help
file Bootstrapping.D.
The p.values are also calculated automatically (when p.Val=TRUE) using
the function p.val that is included in this package.
Due to the multiple pairwise comparison between populations (when more
than two populations are compared with one another), the p-value has to
be adjusted. The adjusted p-values take the multiple comparison from one
data set into account and represent the smallest overall significance
levels, at which the hypothesis would be rejected (Wright, 1992). Those
p-values giving the significance levels for different loci, are adjusted
independently from each other. Those p-values giving the significance
levels for the averaged differentiation over all loci, are adjusted to
one another. The adjustment is performed by Bonferroni correction, by
Holm's method, by Hommel's method and by a method provided by Benjamini
and Hochberg. See the help file of the function p.adjust
for further information about these methods.
After the completion of a pairwise comparison, you will be informed about the time the process took and the estimated end when all the pairwise comparisons will be completed. The more data you analyse parallel using more than one workspace, the longer each analysis takes. When the argument p.Val is set as FALSE, the calculation is fast and you are not informed about the estimated end of the analysis.
Jost, L. 2008 Gst and its relatives do not measure differentiation. Molecular Ecology 17, 18, p. 4015--4026.
Wright, S. P. 1992 Adjusted p-values for simultaneous inference. Biometrics 48, p. 1005--1013
Bootstrapping.D, p.val,
p.value.correcture, Hardy.Weinberg,
allelefreq, setwd, getwddata(Example.transformed)
Example1 <- Example.transformed
pair.pops.Gst("Example1", object=TRUE, format.table=FALSE, p.Val=FALSE)
pair.pops.Gst.est("Example1", object=TRUE, format.table=FALSE, p.Val=FALSE)
data(Example.untransformed)
Example2 <- Example.untransformed
pair.pops.D("Example2", object=TRUE, format.table=TRUE, p.Val=FALSE)
pair.pops.Dest("Example2", object=TRUE, format.table=TRUE, p.Val=FALSE)
pair.pops.Dest.Chao("Example2", object=TRUE, format.table=TRUE, p.Val=FALSE)
# If you don't know where the results of these example tables have been
# saved, type getwd()Run the code above in your browser using DataLab