Package 'fsthet'

Title: Fst-Heterozygosity Smoothed Quantiles
Description: A program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.
Authors: Sarah P. Flanagan and Adam G. Jones
Maintainer: Sarah P. Flanagan <[email protected]>
License: GPL-2
Version: 1.0.1
Built: 2024-10-31 20:31:47 UTC
Source: https://github.com/cran/fsthet

Help Index

This counts the number of alleles at a locus.

Description

This counts the number of times each allele occurs at a locus from a list of genotypes (the sum of all the counts is 2*number of individuals).

Usage

allele.counts(genotypes)

Arguments

genotypes

A list of genotypes.

Value

AlleleCounts

The number of times each allele is recorded at the locus.

Examples

#create a random sample of genotypes
  genotypes<-sample(c("0101","0102","0202"),50,replace=TRUE)
  counts<-allele.counts(genotypes)

Example heterozygosity bins from fsthet.

Description

This is a list with a data.frame of bins (the lower and upper bounds for each heterozygosity bin) and a list of fsts that fall into each bin, with the name of each set of Fst values being the upper heterozygosity bound from the data.frame of bins.

Usage

bins

Format

list

Source

bins<-make.bins(fsts)

References

See Flanagan & Jones

This calcualtes global Fsts from a genepop dataframe.

Description

This calcualtes global Fsts from a genepop dataframe. This does not include bootstrapping.

Usage

calc.actual.fst(df, fst.choice="fst")

Arguments

df

Provide the genepop dataframe (from my.read.genepop).
fst.choice

Specify which type of fst calculation should be used. See fst.options.print for the choices.

Value

fsts

This returns a dataframe with Locus, Ht, and Fst characters.

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20),
    loc0=sample(c("0101","0102","0202"),40,replace=TRUE))
  fsts<-calc.actual.fst(gpop)
  ## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.genepop(gfile)
    fsts<-calc.actual.fst(gpop)
  
## End(Not run)

This calculates allele frequencies.

Description

This calculates allele frequencies from a list of genotypes.

Usage

calc.allele.freq(genotypes)

Arguments

genotypes

A list of genotypes.

Value

obs.af

A list of observed allele frequencies in the genotypes list.

Examples

#create a random sample of genotypes
  genotypes<-sample(c("0101","0102","0202"),50,replace=TRUE)
  af<-calc.allele.freq(genotypes)

This calculates beta-hat, the Fst value used in Lositan.

Description

This calculates Weir & Cockerham (1993)'s beta-hat. Beaumont & Nichols (1996) used this formulation in FDIST2 (and is implemented in Lositan) See the vignette for details on the calculation of beta.

Usage

calc.betahat(df, i)

Arguments

df

A dataframe containing the genepop information, where the first column is the population ID.
i

Column number containing genotype information.

Value

ht

HB (or 1-F1). This is a single numerical value.
fst

The calculated betahat value ((F0-F1)/(1-F1))for this locus.

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20))
     for(i in 1:40){
      gpop[1:20,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
      gpop[21:40,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
     }
  bh<-calc.betahat(gpop, 3) #calculate betahat for the SNP
  
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.genepop(gfile)
    beta1<-calc.betahat(gpop,3) #calculate betahat for the first SNP

This calculates expected heterozygosities.

Description

This calculates expected heterozygosities from a list of allele frequencies.

Usage

calc.exp.het(af)

Arguments

af

is a list of allele frequencies.

Value

ht

The expected heterozygosity under Hardy-Weinberg expectations. This is a single numerical value.

Examples

#create a random sample of genotypes
  genotypes<-sample(c("0101","0102","0202"),50,replace=TRUE)
  af<-calc.allele.freq(genotypes)
  hs<-calc.exp.het(af)

This calculates Fst.

Description

This calculates Fst. The caluclation is done as (Ht-Hs)/Ht, where Ht is the expected heterozygosity for all populations and Hs is the expected heterozygosity for each population. This calculation is used in bootstrapping functions.

Usage

calc.fst(df, i)

Arguments

df

A dataframe containing the genepop information, where the first column is the population ID.
i

Column number containing genotype information.

Value

ht

The expected heterozygosity under Hardy-Weinberg expectations. This is a single numerical value.
fst

The calculated Fst value for this locus.

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20),
    loc0=sample(c("0101","0102","0202"),40,replace=TRUE))
  fst1<-calc.fst(gpop,3)
  ## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.gpop(gfile)
    fst1<-calc.fst(gpop,3) #calculate fst for the first SNP
  
## End(Not run)

This calculates theta.

Description

This calculates Weir (1990)'s theta. See the vignette for details on the calculation of beta.

Usage

calc.theta(df, i)

Arguments

df

A dataframe containing the genepop information, where the first column is the population ID.
i

Column number containing genotype information.

Value

ht

T2. This is a single numerical value.
fst

The calculated theta value (T1/T2) for this locus.

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20),
    loc0=sample(c("0101","0102","0202"),40,replace=TRUE))
  theta1<-calc.theta(gpop, 3)
  ## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.gpop(gfile)
    theta1<-calc.theta(gpop,3) #calculate theta for the first SNP
  
## End(Not run)

This calculates the average confidence intervals from multiple bootstrap outputs.

Description

This calculates the mean upper and lower confidence intervals from a list of bootstrap CI matrices.

Usage

ci.means(boot.out.list)

Arguments

boot.out.list

A list of matrices. Each matrix is the CIs from fst.boot (boot.out[[3]]).

Value

avg.cil

A list of the average lower CI values
avg.ciu

A list of the average upper CI values

Examples

## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.genepop(gfile)
    quant.out<-fst.boot(gpop, bootstrap = FALSE)
    quant.list<-ci.means(quant.out[[3]])
  
## End(Not run)

Example dataframe of smoothed quantiles from fsthet

Description

Example list of data.frames with smoothed quantiles from fsthet output from numerical simulations The data were generated using a numerical analysis with Nm = 10, 75 demes, and 5 population samples taken. No selection was imposed. This is a list with a single data.frame containing values from 95 percent smoothed quantiles.

Usage

cis

Format

list

Source

Ninety-five percent smoothed quantiles, using the dataframe gpop.

References

See Flanagan & Jones

Example list of CI matrices from bootstrap output from numerical simulations

Description

Example list of CI data.frames from fsthet output from numerical simulations The data were generated using a numerical analysis with Nm = 10, 75 demes, and 5 population samples taken. No selection was imposed. This is a list of data.frames containing values from 99 percent and 95 percent smoothed quantiles.

Usage

cis.list

Format

list

Source

From multiple smoothed quantile alpha thresholds, using the dataframe gpop.

References

See Flanagan & Jones

This is a wrapper to run the bootstrapping and plot the confidence intervals and significant loci.

Description

This calcualtes global Fsts from a genepop dataframe and then does: p-value calculations plots the Heterozygosity-Fst relationship with smoothed CIs outputs the loci lying outside the confidence intervals. Returns a data frame containing Locus ID, Ht, Fst, P-value, a Benjamini-Hochberg-corrected P-value, and a true/false value of whether it's an outlier.

Usage

fhetboot(gpop, fst.choice="fst", alpha=0.05,nreps=10)

Arguments

gpop

Provide the genepop dataframe (from my.read.genepop).
fst.choice

Specify which type of fst calculation should be used. See fst.options.print for the choices.
alpha

The alpha value for the confidence intervals and the p-value adjustment calculations (default is 0.05).
nreps

The number of bootstrap replicates to use. The default is 10.

Value

fsts

This returns a dataframe with Locus, Ht, Fst, P-value, correcte P-value, and True/False of whether it's an outlier.

Examples

## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.genepop(gfile)
    out.dat<-fhetboot(gpop, fst.choice="fst", alpha=0.05,nreps=10)
  
## End(Not run)

This identifies all of the SNPs outside of the smoothed quantiles in the dataset.

Description

This identifies all of the SNPs outside of the smoothed quantiles in the dataset.

Usage

find.outliers(df, boot.out, ci.df = NULL, file.name = NULL)

Arguments

df

Provide the dataframe with Ht and Fst values.
boot.out

Bootstrap output. You must provide this.
ci.df

List of confidence intervals. You may provide this in addition to bootstrap output to save a small amount of time.
file.name

You may provide a file name to output the outliers to a csv file. Otherwise, the function will only return the outliers.

Value

out

A list of the outlier loci

Examples

## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.gpop(gfile)
    fsts<-calc.actual.fst(gpop)
    boot.out<-as.data.frame(t(replicate(10, fst.boot(gpop))))
    outliers<-find.outliers(fsts,boot.out)
  
## End(Not run)

Generates quantiles from binned Fst values

Description

This takes the output from make.bins and calculates the smoothed quantiles.

Usage

find.quantiles(bins,bin.fst,ci=0.05)

Arguments

bins

A dataframe containing with upper and lower het and Fst values for each bin (output from make.bins).
bin.fst

A list with the Fst values for each bin (output from make.bins).

ci

A value for the confidence intervals alpha (default is 0.05).

Value

fst.CI

A list of data.frames, one for each ci value with the upper and lower Fst quantiles for each Heterozygosity bin.

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20))
     for(i in 1:40){
      gpop[1:20,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
      gpop[21:40,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
     }
  fsts<-calc.actual.fst(gpop)
  nloci<-(ncol(gpop)-2)
  boot.out<-as.data.frame(t(replicate(nloci, fst.boot.onecol(gpop,"fst"))))
  bins<-make.bins(boot.out,25,Ht.name="V1",Fst.name="V2")
  fst.CI<-find.quantiles(bins$bins,bins$bin.fst)

This is the major bootstrapping function to calculate confidence intervals.

Description

This randomly samples all of the loci, with replacement (so if you have 200 loci, it will choose 200 loci to calculate Fst for, but some may be sampled multiply) It makes use of fst.boot.onerow. To calculate the confidence intervals, this function bins the Fst values based on heterozygosity values. The bins are overlapping and each bin is the width of smooth.rate. The Fst value which separates the top 100*(ci/2) and bottom 100*(ci/2) percent in each bin are the upper and lower CIs. This function can be slow. We recommend running it 10 times to generate confidence intervals for analysis.

Usage

fst.boot(df,fst.choice="fst",ci=0.05,num.breaks=25, bootstrap = TRUE,min.per.bin=20)

Arguments

df

A dataframe containing the genepop information, where the first column is the population ID.
fst.choice

A character defining which fst calculation is to be used. See fst.options.print() for the choices.
ci

A value for the confidence intervals alpha (default is 0.05).
num.breaks

The number of breaks used to create bins (default is 25)
bootstrap

A TRUE/FALSE statement telling the program whether to bootstrap and then determine the bins or to calculate bins and confidence intervals from the empirical dataset without bootstrapping. The default is TRUE, which means bootstrapping occurs.
min.per.bin

The minimum number of loci that are required for a bin to be retained. Default is 20.

Value

Fsts

The bootstrapped Fst and Ht values
Bins

A dataframe containing the bins start and stop Ht values.
fst.CI

A list of dataframes containing the lower and upper confidence intervals' Ht values.

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20))
   for(i in 1:40){
    gpop[1:20,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
    gpop[21:40,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
   }
  fsts<-calc.actual.fst(gpop)
  quant.out<-as.data.frame(t(replicate(1, fst.boot(gpop,bootstrap=FALSE))))
  ## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.genepop(gfile)
    fsts<-calc.actual.fst(gpop)
    quant.out<-as.data.frame(t(replicate(1, fst.boot(gpop,bootstrap=FALSE))))
  
## End(Not run)

Calculates mean values within the bins.

Description

This calculates mean heterozygosity and Fst values for each bin used in bootstrapping.

Usage

fst.boot.means(boot.out)

Arguments

boot.out

The first item in the output lists from fst.boot (aka boot.out[[1]].

Value

bmu

A dataframe containing four columns: heterozygosity Fst the number of loci in the bin the lower Ht value for the bin and the upper Ht value for the bin.

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20))
   for(i in 1:40){
    gpop[1:20,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
    gpop[21:40,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
   }
    fsts<-calc.actual.fst(gpop)
  boot.out<-as.data.frame(t(replicate(1, fst.boot(gpop))))
  outliers<-find.outliers(fsts,boot.out)
## Not run: 
  gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
  gpop<-my.read.genepop(gfile)
  fsts<-calc.actual.fst(gpop)
  boot.out<-as.data.frame(t(replicate(10, fst.boot(gpop))))
  outliers<-find.outliers(fsts,boot.out)

## End(Not run)

This bootstraps across all individuals to calculate a bootstrapped Fst for a randomly-sampled locus.

Description

This calculates Fst using calc.fst. It randomly selects a column containing genotype information for all individuals. It then calculates Fst and Ht for that locus.

Usage

fst.boot.onecol(df, fst.choice)

Arguments

df

A dataframe containing the genepop information, where the first column is the population ID.
fst.choice

A character defining which fst calculation is to be used. The three options are: Wright's Fst (Wright, wright, WRIGHT, W, w) Weir and Cockerham 1993's beta (WeirCockerham,weircockerham,wc,WC) Corrected Weir and Cockerham 1993's beta from Beaumont and Nichols 1996 (WeirCockerhamCorrected, weircockerhamcorrected,corrected,wcc,WCC)

Value

ht.fst

A vector containin Ht and Fst

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20))
   for(i in 1:40){
    gpop[1:20,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
    gpop[21:40,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
   }
   fsts<-calc.actual.fst(gpop)
  nloci<-(ncol(gpop)-2)
  boot.out<-as.data.frame(t(replicate(nloci, fst.boot.onecol(gpop,"fst"))))
## Not run: 
  gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
  gpop<-my.read.genepop(gfile)
  fsts<-calc.actual.fst(gpop)
  nloci<-(ncol(gpop)-2)
  boot.out<-as.data.frame(t(replicate(nloci, fst.boot.onecol(gpop,"fst"))))
  outliers<-find.outliers(fsts,boot.out)

## End(Not run)

This prints the options for choosing an Fst calculation.

Description

This prints the options for choosing an Fst calculation.

Usage

fst.options.print()

Examples

fst.options.print()

This is a wrapper to generate and plot the smoothed quantiles and identify outliers.

Description

This calcualtes global Fsts from a genepop dataframe and then does: calculates smoothed quantiles plots the Heterozygosity-Fst relationship with smoothed quantiles outputs the loci lying outside the quantiles. Returns a data frame containing Locus ID, Ht, Fst, and a true/false value of whether it's an outlier.

Usage

fsthet(gpop, fst.choice="fst", alpha=0.05)

Arguments

gpop

Provide the genepop dataframe (from my.read.genepop).
fst.choice

Specify which type of fst calculation should be used. See fst.options.print for the choices.
alpha

The alpha value for the quantiles (default is 0.05 to generate 95 percent quantiles).

Value

fsts

This returns a dataframe with Locus, Ht, Fst, and True/False of whether it's an outlier.

Examples

## Not run: 
  gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
  gpop<-my.read.gpop(gfile)
  out.dat<-fsthet(gpop)

## End(Not run)

Example fst calculations from a genepop file.

Description

Example fst calculations from a genepop file. The original data were generated by using a numerical analysis with Nm = 10, 75 demes, and 5 population samples taken. No selection was imposed. The fsts were calculated using calc.actual.fst(gpop) This file contains a dataframe with 2000 columns and 3 rows. The first column is the Locus ID, the second column is the Ht for that locus, and the third column is the Fst for that locus.

Usage

fsts

Format

data.frame

Source

Generated by numerical analysis

References

See Flanagan & Jones

Example fst calculations from a genepop file.

Description

Example fst calculations using beta (fst.choice="var") from a genepop file. The original data were generated by using a numerical analysis with Nm = 10, 75 demes, and 5 population samples taken. No selection was imposed. The fsts were calculated using calc.actual.fst(gpop,fst.choice="var") This file contains a dataframe with 2000 columns and 3 rows. The first column is the Locus ID, the second column is the Ht for that locus, and the third column is the Fst for that locus.

Usage

fsts.beta

Format

data.frame

Source

Generated by numerical analysis

References

See Flanagan & Jones

Example fst calculations from a genepop file.

Description

Example fst calculations using betahat (fst.choice="betahat") from a genepop file. The original data were generated by using a numerical analysis with Nm = 10, 75 demes, and 5 population samples taken. No selection was imposed. The fsts were calculated using calc.actual.fst(gpop,fst.choice="betahat") This file contains a dataframe with 2000 columns and 3 rows. The first column is the Locus ID, the second column is the Ht for that locus, and the third column is the Fst for that locus.

Usage

fsts.betahat

Format

data.frame

Source

Generated by numerical analysis

References

See Flanagan & Jones

Example fst calculations from a genepop file.

Description

Example fst calculations using theta (fst.choice="theta") from a genepop file. The original data were generated by using a numerical analysis with Nm = 10, 75 demes, and 5 population samples taken. No selection was imposed. The fsts were calculated using calc.actual.fst(gpop,fst.choice="theta") This file contains a dataframe with 2000 columns and 3 rows. The first column is the Locus ID, the second column is the Ht for that locus, and the third column is the Fst for that locus.

Usage

fsts.theta

Format

data.frame

Source

Generated by numerical analysis

References

See Flanagan & Jones

Example genepop file from numerical simulations

Description

Example genepop file from numerical simulations. It was generated by using a numerical analysis with Nm = 10, 75 demes, and 5 population samples taken. No selection was imposed. This file contains a dataframe with 2002 columns and 250 rows. The first two columns are the population name and the individual name. The remaining columns are genotypes for each locus (one column per locus). Each row is an individual.

Usage

gpop

Format

data.frame

Source

Generated by numerical analysis

References

See Flanagan & Jones

This sorts Fst values into a designated number of overlapping heterozygosity bins.

Description

This breaks up Fst values into a designated number of overlapping heterozygosity bins. It returns a list containing a data.frame called bins a list called bin.fst with the Fst values for each of the Het categories.

Usage

make.bins(fsts,num.breaks=25, Ht.name="Ht", Fst.name="Fst",min.per.bin=20)

Arguments

fsts

A dataframe containing at least the columns with heterozygosity and Fst values.
num.breaks

The number of breaks used to create bins (default is 25)
Ht.name

Provide the name of the column with the heterozygosity values, unless the column is named "Ht".
Fst.name

Provide the name of the column with the Fst values, unless the column is named "Fst".
min.per.bin

If you have a smaller dataset, you can change the minimum number of loci required to be in each bin. Default is 20.

Value

list(bins, bin.fst)

A list with a data.frame called bins with the upper and lower Fst and Ht values and a list called bin.fst with the Fst values for each of the Het categories.

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20))
     for(i in 1:40){
      gpop[1:20,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
      gpop[21:40,(i+2)]<-sample(c("0101","0102","0202"),20,replace=TRUE)
     }
  fsts<-calc.actual.fst(gpop)
  nloci<-(ncol(gpop)-2)
  boot.out<-as.data.frame(t(replicate(nloci, fst.boot.onecol(gpop,"fst"))))
  bins<-make.bins(boot.out,25,Ht.name="V1",Fst.name="V2")
  ## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.gpop(gfile)
    fsts<-calc.actual.fst(gpop)
    nloci<-(ncol(gpop)-2)
    boot.out<-as.data.frame(t(replicate(nloci, fst.boot.onecol(gpop))))
    make.bins(boot.out,25)
  
## End(Not run)

This reads a genepop file into R

Description

This reads a genepop file into R. It was adapted from a similar functionin adegenet.

Usage

my.read.genepop(file, ncode = 2L, quiet = FALSE)

Arguments

file

is the filename of the genpop file.
quiet

If quiet = FALSE updates will be printed. If quiet = T status updates will not be printed.

ncode

Do not change this argument.

Value

res

A dataframe with the Population ID in column 1, the Individual ID in column 2, and the genotypes in columns following that. There is one row per individual.

References

http://adegenet.r-forge.r-project.org/

Examples

gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
  gpop<-my.read.genepop(gfile)

Calculates mean values within the bins.

Description

This calculates mean heterozygosity and Fst values for each bin used in bootstrapping.

Usage

p.boot(actual.fsts, boot.out,boot.means=NULL)

Arguments

actual.fsts

The first item in the output lists from fst.boot.

boot.out

The output from a bootstrapping run. Either supply this or boot.means.

boot.means

The output from fst.boot.means. Either supply this or bootstrapping output.

Value

pvals

A numeric containing uncorrected p-values for each locus. The names attribute are the locus names.

Examples

## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.genepop(gfile)
    fsts<-calc.actual.fst(gpop)
    boot.out<-as.data.frame(t(replicate(10, fst.boot(gpop))))
    boot.pvals<-p.boot(fsts,boot.out=boot.out)
  
## End(Not run)

This plots a dataframe of fsts with bootstrapped confidence intervals.

Description

This plots a dataframe of fsts with bootstrapped confidence intervals.

Usage

plotting.cis(df,boot.out,ci.df=NULL,sig.list=NULL,Ht.name="Ht",Fst.name="Fst",
	ci.col="red", pt.pch=1,file.name=NULL,sig.col=ci.col,make.file=TRUE)

Arguments

df

A dataframe of Fst and Ht values. It must have at least two columns, one named "Ht" and one named "Fst". Or you must pass the column names to the function
boot.out

Bootstrap output. You must either provide this or a list of confidence interval values.
ci.df

Data frame of confidence intervals. You must either provide this or bootstrap output.
sig.list

List of significant locus names (this acts as a way to highlight particular loci). This is optional and colors some of the points using the same shape as pt.pch and the color of sig.col (default sig.color is same as ci.col).
Ht.name

Provide the name of the column with the heterozygosity values, unless the column is named "Ht".
Fst.name

Provide the name of the column with the Fst values, unless the column is named "Fst".
ci.col

You can input the colors of the confidence intervals to be plotted. First is the 95 percent CI, second is the 99 percent CI. Defaults are "red" and "gold".
pt.pch

You can change the point shape here. Default is 1 (open circles)
sig.col

The color of the significant loci, if that option is taken. The default is the same color as the confidence interval.
file.name

You can provide the filename. If not provided, default is "OutlierLoci" in the current directory.
make.file

A boolean value (TRUE or FALSE). If TRUE, a file will be created with the plot. If FALSE, the plot will be made in R only (and can be further annotated).

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20),
                  loc0=sample(c("0101","0102","0202"),40,replace=TRUE),
                  loc1=sample(c("0101","0102","0202"),40,replace=TRUE))
  fsts<-calc.actual.fst(gpop)
  bins<-make.bins(fsts)
  cis<-find.quantiles(bins = bins$bins,bin.fst = bins$bin.fst)
  quant.list<-cis$CI0.95
  plotting.cis(df=fsts,ci.df=quant.list,make.file=FALSE)
  ## Not run: 
  load(fsts)
  bins<-make.bins(fsts)
  cis<-find.quantiles(bins = bins$bins,bin.fst = bins$bin.fst)
  quant.list<-cis$CI0.95
  plotting.cis(df=fsts,ci.df=quant.list,make.file=FALSE)
  
## End(Not run)

Example fsthet output based on numerical simulations

Description

Example fsthet output based on numerical simulations Allelic information was generated by using a numerical analysis with Nm = 10, 75 demes, and 5 population samples taken. No selection was imposed. This is a list of three structures. The first is a data.frame containing the Ht and Fst values. The second is a data.frame of the bins with the lower heterozygosity values and the upper heterozygosity values for each bin. The third is a list of data.frames with the lower (Low) and upper (Upp) Fst values for each bin (the bins are in "LowHet" and "UppHet" columns.)

Usage

quant.out

Format

list

Source

Smoothed quantiles generated from the dataframe gpop.

References

See Flanagan & Jones

This removes spaces from a character vector

Description

This removes spaces from a before and after words in a character vector. It was adapted from a similar function in adegenet.

Usage

remove.spaces(charvec)

Arguments

charvec

is a vector of characters containing spaces to be removed.

Value

charvec

A vector of characters without spaces

References

http://adegenet.r-forge.r-project.org/

Examples

charvec<-c("this ", " is"," a"," test")
remove.spaces(charvec)

This calculates Cockerham & Weir's Beta.

Description

This calculates Weir & Cockerham (1993)'s Fst. The caluclation is based on variance in allele frequencies. See the vignette for details on the calculation of beta.

Usage

var.fst(df, i)

Arguments

df

A dataframe containing the genepop information, where the first column is the population ID.
i

Column number containing genotype information.

Value

ht

2pbar(1-pbar). This is a single numerical value.
fst

The calculated beta value for this locus.

Examples

gpop<-data.frame(popinfo=c(rep("POP 1", 20),rep("POP 2", 20)),ind.names=c(1:20,1:20),
                  loc0=sample(c("0101","0102","0202"),40,replace=TRUE),
                  loc1=sample(c("0101","0102","0202"),40,replace=TRUE))
  var1<-var.fst(gpop,3) 
  ## Not run: 
    gfile<-system.file("extdata", "example.genepop.txt",package = 'fsthet')
    gpop<-my.read.gpop(gfile)
    var1<-var.fst(gpop,3) #calculate variance-based for the first SNP
  
## End(Not run)