| Title: | Phylogeographic Analysis of Island Colonization Events |
|---|---|
| Description: | Estimation of the number of colonization events between islands of the same archipelago for a species. It uses rarefaction curves to control for both field and genetic sample sizes as it was described in Coello et al. (2022) <doi:10.1111/jbi.14341>. |
| Authors: | Alberto J. Coello [aut, cre] (ORCID = 0000-0002-2665-3726), Mario Fernández-Mazuecos [aut] (ORCID = 0000-0003-4027-6477), Ruben H. Heleno [aut] (ORCID = 0000-0002-4808-4907), Pablo Vargas [aut] (ORCID = 0000-0003-4502-0382) |
| Maintainer: | Alberto J. Coello <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.2 |
| Built: | 2025-02-11 04:11:40 UTC |
| Source: | https://github.com/paicecode/paice |
A package for inferring inter-island colonization events in island-like systems.
Estimation of the number of infer inter-island colonization events in an island-like system by analyzing the geographic distribution of uniparentally inherited haplotypes and their genealogical relationships. Furthermore, by building rarefaction curves based on both genetic sampling (variable positions) and field sampling (populations/individuals), an estimation of the number of colonization events corrected by sampling effort could be done. The method used in the PAICE package is described in Coello et al. (2022).
colonization to infer the minimun number of colonization
events
geneticResampling to simplify the genealogy by deleting a
variable position
maxCol to calculate asymptotic estimators considering genetic
and field sampling
plot.maxCol to plot curves generated by maxCol
plot.rarecol to plot rarefaction curves
rarecol to generate rarefaction curves of colonization events
read.rarecol to read previously saved rarefaction curve files
CmonsData haplotype distribution of
Cistus monspeliensis in the Canary Islands
CmonsNetwork genealogy of Cistus monspeliensis
CmonsRare example data of rarefaction curves for Cistus
monspeliensis
Alberto J. Coello, Mario Fernandez-Mazuecos, Ruben H. Heleno and Pablo Vargas
Maintainer: Alberto J. Coello <[email protected]>
Coello, A.J., Fernandez-Mazuecos, M., Heleno, R.H., Vargas, P. (2022). PAICE: A new R package to estimate the number of inter-island colonizations considering haplotype data and sample size. Journal of Biogeography, 49(4), 577-589.DOI: 10.1111/jbi.14341
# Inference of minimum number of inter-island colonization events data(CmonsData) data(CmonsNetwork) col <- colonization(data = CmonsData, network = CmonsNetwork) col summary(col) # Asumptotic estimators of colonization events # 25 replicates used in each sampling variable set.seed(31) CmonsRare <- rarecol(data = CmonsData, network = CmonsNetwork, replicates_field = 25, replicates_genetic = 25, monitor = TRUE, mode = c(1, 2)) maxcol <- maxCol(data = CmonsRare) maxcol summary(maxcol) # Plotting results old.par <- par(no.readonly = TRUE) # To restore previous options par(mfrow = c(2, 2)) plot(CmonsRare) par(fig = c(0, 1, 0, 0.5), new = TRUE) plot(maxcol) par(old.par)# Inference of minimum number of inter-island colonization events data(CmonsData) data(CmonsNetwork) col <- colonization(data = CmonsData, network = CmonsNetwork) col summary(col) # Asumptotic estimators of colonization events # 25 replicates used in each sampling variable set.seed(31) CmonsRare <- rarecol(data = CmonsData, network = CmonsNetwork, replicates_field = 25, replicates_genetic = 25, monitor = TRUE, mode = c(1, 2)) maxcol <- maxCol(data = CmonsRare) maxcol summary(maxcol) # Plotting results old.par <- par(no.readonly = TRUE) # To restore previous options par(mfrow = c(2, 2)) plot(CmonsRare) par(fig = c(0, 1, 0, 0.5), new = TRUE) plot(maxcol) par(old.par)
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Data of Cistus monspeliensis prepared to be used as example for the PAICE package.
data(CmonsData)data(CmonsData)
A data frame containing a presence matrix of Cistus monspeliensis
haplotypes in the Canary Islands extracted from Coello et al. (2021). Each
row indicates the number of individuals of each haplotype occurring in each
population. The first column indicates the island, the second column
indicates the population and successive columns correspond to haplotypes in
the island system. Missing haplotypes are also included but without any
presence (haplotypes m1 and m2).
Data containing occurrences of each haplotype of Cistus monspeliensis found in the Canary Islands. Data were taken from Coello et al. (2021). This dataset was constructed using three ptDNA regions and 37 populations from the Canarian archipelago.
Coello, A.J., Fernandez-Mazuecos, M., Garcia-Verdugo, C., Vargas, P. (2021). Phylogeographic sampling guided by species distribution modeling reveals the Quaternary history of the Mediterranean-Canarian Cistus monspeliensis (Cistaceae). Journal of Systematics and Evolution, 59(2), 262-277. DOI: 10.1111/jse.12570
data(CmonsData) CmonsData # Show data framedata(CmonsData) CmonsData # Show data frame
Genealogy of Canarian haplotypes of Cistus monspeliensis.
data("CmonsNetwork")data("CmonsNetwork")
A data frame containing the genalogy of Cistus monspeliensis
in the Canary Islands. Each row indicates the connection between each
haplotype and its ancestral haplotype. The first column is the name of the
haplotype, the second column is the name of its ancestral haplotype and the
third column indicates the number of variable positions that change between
both haplotypes. The ancestral haplotype in the archipelago (haplotype C1)
is connected to the outgroup ("OUT"), and is be located in the first
row of the genealogy.
This dataset was taken from Coello et al. (2021). It was constructed using three ptDNA regions and 37 populations from the Canarian archipelago.
Coello, A.J., Fernandez-Mazuecos, M., Garcia-Verdugo, C., Vargas, P. (2021). Phylogeographic sampling guided by species distribution modeling reveals the Quaternary history of the Mediterranean-Canarian Cistus monspeliensis (Cistaceae). Journal of Systematics and Evolution, 59(2), 262-277. DOI: 10.1111/jse.12570
data(CmonsNetwork) CmonsNetwork # Show data framedata(CmonsNetwork) CmonsNetwork # Show data frame
Simulated rarefaction curves to be used as example data for estimation of colonization events.
data(CmonsRare)data(CmonsRare)
A list containing data of both genetic and field rarefaction curves. The first element corresponds to the genetic estimation and the second element corresponds to the field estimation.
This dataset was constructed from CmonsData and
CmonsNetwork with the following code:
set.seed(31)
CmonsRare <- rarecol(data = CmonsData, network = CmonsNetwork,
replicates_field = 25, replicates_genetic = 25)
data(CmonsRare) str(CmonsRare) # Structure of datadata(CmonsRare) str(CmonsRare) # Structure of data
A inference of the minimum number of colonization events between islands of an archipelago considering both haplotype distributions and genealogy.
colonization(data, network)colonization(data, network)
data |
a data frame containing the matrix of occurrences of haplotypes in the islands of an archipelago (applicable to any island-like system). The first two columns indicate islands and populations sampled. Successive columns indicate haplotype occurrences (one column per haplotype). If present, missing haplotypes must also be included (i.e. columns without occurrences). |
network |
a data frame containing the genealogy of haplotypes. The first column
indicates the haplotype, the second column indicates its ancestral
haplotype and the third column indicates the variable position changed
between an haplotype and its ancestral haplotype. If present, missing
haplotypes must also be included. The ancestral haplotype must be
connected to an outgroup named |
Colonization events are inferred following Coello et al. (2022).
Each haplotype produces a number of colonization events equal to the total
number of islands in which the haplotype occurs minus one. These are type 1
colonization events (c1).
Additionally, colonization events between an ancestral haplotype and the
derived haplotypes are also inferred if the ancestral haplotype occurs in
different islands than the derived haplotypes. These inferred colonization
events correspond to type 2 (c2) and type 3 (c3) colonization
events.
A type 2 colonization events is that between a haplotype and its ancestral haplotype that can only be assigned to the connection between these two haplotypes. These colonization events are noted in the derived haplotype.
Type 3 colonization events are those (one or more) inferred between an ancestral haplotype and its derived haplotypes but that cannot be assigned to a specific connection, so colonization events are assigned to the ancestral haplotype.
colonization returns an object of class
"colonization".
The function print shows the total of colonization events inferred.
The function summary returns a more detailed output showing a
description of data used and inferred colonization events by haplotype and
by type.
colonization only considers the complete sampling. To correct the
inference by field and genetic sampling use rarecol.
Coello, A.J., Fernandez-Mazuecos, M., Heleno, R.H., Vargas, P. (2022). PAICE: A new R package to estimate the number of inter-island colonizations considering haplotype data and sample size. Journal of Biogeography, 49(4), 577-589.DOI: 10.1111/jbi.14341
rarecol to build a rarefaction curve of colonization events.
maxCol to calculate the asymptotic estimator for the number of
colonization events from data generated by rarecol.
data(CmonsData) data(CmonsNetwork) col <- colonization(data = CmonsData, network = CmonsNetwork) col # Total of colonization events inferred summary(col) # Detailed description of inferred colonization eventsdata(CmonsData) data(CmonsNetwork) col <- colonization(data = CmonsData, network = CmonsNetwork) col # Total of colonization events inferred summary(col) # Detailed description of inferred colonization events
A reduction of the resolution of the genealogy by supressing a variable position in the genealogy. It simulates a lower level of genetic sampling.
geneticResampling(data, network, position)geneticResampling(data, network, position)
data |
a data frame containing the occurrence matrix of haplotypes in the islands of an archipelago (applicable to any island-like system). The two first columns indicate islands and populations sampled. Successive columns indicate haplotype occurrences (one column per haplotype). If present, missing haplotypes must also be included (i.e. columns without occurrences). |
network |
a data frame containing the genealogy of haplotypes. The first column
indicates the haplotype, the second column indicates its ancestral
haplotype and the third column indicates the variable position changed
between an haplotype and its ancestral haplotype. If present, missing haplotypes must also be
included. The ancestral haplotype must be connected to an outgroup named
|
position |
numeric. Indicates the variable position that will be deleted in the simplified data. |
To simulate a lower level of genetic sampling, this function deletes a
variable position from the original data and thus simplifies the genealogy.
geneticResampling generates a new occurrence matrix of haplotypes and
a new genealogy without the variable position previously indicated and
merging ancestral and derived haplotypes separated by this variable
position. If more than one connection are defined by the variable position
indicated, this function deletes all connections with this variable
position. This function works for both observed and missing haplotypes.
geneticResampling returns a list containing the new occurrence matrix
of haplotypes and the new genealogy after deleting the variable position
indicated. The returned object contains the following components:
data |
a data frame containing the new occurrence matrix of haplotypes after removing the variable position indicated. |
network |
a data frame containing the new genealogy after removing the variable position indicated. |
If the variable position corresponds to the connection between the ancestral
haplotype in the archipelago and the outgroup (denoted as "OUT"), no
change is effected as the ancestral haplotype stays connected to the
outgroup.
This function works inside rarecol.
rarecol to build a rarefaction curve of colonization events.
data(CmonsData) data(CmonsNetwork) # Delete position 462 of Cistus monspeliensis data newdata <- geneticResampling(CmonsData, CmonsNetwork, 462) newdata$data # New presences matrix of haplotypes newdata$network # New genealogydata(CmonsData) data(CmonsNetwork) # Delete position 462 of Cistus monspeliensis data newdata <- geneticResampling(CmonsData, CmonsNetwork, 462) newdata$data # New presences matrix of haplotypes newdata$network # New genealogy
A calculation of asymptotic estimators of colonization events from both curves generated using the rarecol function.
maxCol(data, level = 0.95, del = 0.05, method = 1)maxCol(data, level = 0.95, del = 0.05, method = 1)
data |
an object of |
level |
numeric. Determines the confidence interval used to estimate error in Michaelis-Menten equation parameters. By default 0.95. |
del |
numeric. Determines the interval of values to be deleted to avoid the influence of extreme values. By default 0.05 (i.e. deleted values below 2.5 quantile and above 97.5 quantile). |
method |
numeric. Indicates if the algorithm should try to fit the curve by
assigning a value to the intercept in genetic rarefaction curves
( |
This function calculates the number of colonization events estimated by both
resampling methods used in the function rarecol. The first
estimation (genetic estimation) corresponds to resampling first at genetic
level (number of variable positions) and then, per each variable position, a
complete resampling of the number of populations is done. The second
estimation (field estimation) corresponds to the opposite resampling, it is
done first at field level (number of populations) and then, per each
population, a complete resampling of the number of variable positions is
done.
For each curve, the function first estimates the asymptote (estimated number of colonization events) for each level of the second resampling (populations in the first estimation and variable positions in the second estimation) using the mean value of all replicates at each point. Then, these estimations are used to build the final curve estimating the number of colonization events for each resampling methodology. This final curve uses estimations calculated previously, and the asymptote of the curve is calculated by using mean points for each value of the first resampling method (variable positions in the first estimation and populations in the second estimation). The asymptote is calculated by fitting the curve to a Michaelis-Menten equation following Coello et al. (2022).
The confidence interval for the estimated number of colonization events is
calculated with the confint function. Curve fitting is done using the nls function.
This function returns an object of class "maxCol"
consisting in a list of the following elements:
DataGen |
a data frame containing the mean estimated number of colonization events per number of variable positions in the genetic estimation. |
FormulaGen |
formula used to fit final curve in the genetic estimation. |
DataField |
a data frame containing the mean estimated number of colonization events per population in the field estimation. |
FormulaField |
formula used to fit the final curve in the field estimation. |
Summary |
a matrix containing the estimated number of colonization events of each estimation (genetic and field). Minimum and maximum are calculated using the confidence interval indicated. |
ParametersGen |
a matrix containing the value of each parameter to fit a Michaelis-Menten equation for genetic estimation. The minimum and maximum of each parameter according to the confidence interval indicated. This equation is described as: colonization events = M * positions / (K + positions) + c. |
ParametersField |
a matrix containing the value of each parameter to fit a Michaelis-Menten equation for field estimation. The minimum and maximum of each parameter according to the confidence interval indicated. This equation is described as: colonization events = M * (populations - 1) / (K + populations - 1). |
ConfintLevel |
a vector containing the confidence interval used to calculate minimum and maximum for each parameter. |
DeletedData |
a vector containing the interval of extreme values deleted to do the fit of the second accumulation curve. |
The function print returns the number of colonization events inferred
for each estimation (genetic and field) and the interval of confidence of
these estimations.The function summary shows a detailed description
of parameters used to fit both curves, the formula used to fit these curves
and the confidence of interval of each parameter.
To show a detailed description of inferred colonization events in the most
complete case use the function colonization.
Coello, A.J., Fernandez-Mazuecos, M., Heleno, R.H., Vargas, P. (2022). PAICE: A new R package to estimate the number of inter-island colonizations considering haplotype data and sample size. Journal of Biogeography, 49(4), 577-589.DOI: 10.1111/jbi.14341
rarecol to build rarefaction curves of colonization events. To
describe the number of colonization events inferred in the most complete
case use the function colonization. plot.maxCol
to plot the result of this function.
# Use 'CmonsRare' data, a dataset generated using 25 replicates # in both genetic and field sampling data(CmonsRare) maxcol <- maxCol(data = CmonsRare) maxcol # Number of colonization estimated in each curve summary(maxcol) # Description of curves plot(maxcol) # Plotting estimations # Plot all the information old.par <- par(no.readonly = TRUE) # To restore previous options par(mfrow = c(2, 2)) plot(CmonsRare) # First two plots with rarefaction curves par(fig = c(0, 1, 0, 0.5), new = TRUE) plot(maxcol) # Third plot with estimations par(old.par)# Use 'CmonsRare' data, a dataset generated using 25 replicates # in both genetic and field sampling data(CmonsRare) maxcol <- maxCol(data = CmonsRare) maxcol # Number of colonization estimated in each curve summary(maxcol) # Description of curves plot(maxcol) # Plotting estimations # Plot all the information old.par <- par(no.readonly = TRUE) # To restore previous options par(mfrow = c(2, 2)) plot(CmonsRare) # First two plots with rarefaction curves par(fig = c(0, 1, 0, 0.5), new = TRUE) plot(maxcol) # Third plot with estimations par(old.par)
Plots for the estimators calculated by maxCol.
## S3 method for class 'maxCol' plot(x, xlim, ylim, col, xlabbotton, xlabtop, ylab, main, pch = 16, lty = 1, lwd = 2, cex = 1, estimation = TRUE, legend = TRUE, ...)## S3 method for class 'maxCol' plot(x, xlim, ylim, col, xlabbotton, xlabtop, ylab, main, pch = 16, lty = 1, lwd = 2, cex = 1, estimation = TRUE, legend = TRUE, ...)
x |
|
xlim, ylim
|
numeric vector containing limits of x and y axis of the plot (min, max). |
col |
character vector containing colour of both estimation: genetic and field. |
xlabbotton |
a title of the x axis at the bottom of the plot. It correspond with genetic estimation. |
xlabtop |
a title of the x axis at the top of the plot. It correponds with field estimation. |
ylab |
a title of the y axis of the plot. |
main |
an overall title for the plot. |
pch |
indicate symbol used for points of the plot (by default
|
lty |
type of lines used in the plot for curve fitting (by default
|
lwd |
width of lines used in the plot for curve fitting (by default
|
cex |
size of elements in the plot. |
estimation |
logical. If it is |
legend |
logial. If it is |
... |
aditional graphical parameters (see |
Genetic and field estimation are fitted to Michaelis-Menten equation following Coello et al. (2022).
Plot returned by this function represent estimations calculated by
maxCol. The two curves representing both estimators: genetic
and field. Each point represent the mean of number of colonization events
inferred by all replicates at this sampling level. Curves represent
Michaelis-Menten equation fitted to this dataset. If it is plotted, right
side of the plot represent the number of colonization events estimated by
this fitting curve for each estimation, including the conficende interval of
this estimation.
Coello, A.J., Fernandez-Mazuecos, M., Heleno, R.H., Vargas, P. (2022). PAICE: A new R package to estimate the number of inter-island colonizations considering haplotype data and sample size. Journal of Biogeography, 49(4), 577-589.DOI: 10.1111/jbi.14341
maxCol to fit the accumulation curve of colonization events
and estimate the number of colonization events.
# Use 'CmonsRare' data, a dataset generated using 25 replicates # in both genetic and field sampling data(CmonsRare) maxcol <- maxCol(data = CmonsRare) plot(maxcol)# Use 'CmonsRare' data, a dataset generated using 25 replicates # in both genetic and field sampling data(CmonsRare) maxcol <- maxCol(data = CmonsRare) plot(maxcol)
Plots for the rarefaction curves produced by rarecol.
## S3 method for class 'rarecol' plot(x, xlim1, xlim2, ylim, ylim1, ylim2, palette1, palette2, main1, main2, xlab1, xlab2, ylab1, ylab2, las1 = 1, las2 = 1, cextText = 0.75, legendbar = TRUE, ...)## S3 method for class 'rarecol' plot(x, xlim1, xlim2, ylim, ylim1, ylim2, palette1, palette2, main1, main2, xlab1, xlab2, ylab1, ylab2, las1 = 1, las2 = 1, cextText = 0.75, legendbar = TRUE, ...)
x |
an object generated by the |
xlim1, xlim2
|
x limits (min, max) of the two plots. |
ylim1, ylim2
|
y limits (min, max) of the two plots. |
ylim |
y limits (min, max) of the two plots simultaneously. If |
palette1, palette2
|
vector of color for lines in plot 1 and plot 2. |
main1, main2
|
overall title of plot 1 and plot 2. |
xlab1, xlab2
|
label of x axis of plot 1 and plot 2. |
ylab1, ylab2
|
label of y axis of plot 1 and plot 2 |
las1, las2
|
numeric. Corresponds to the style of axis labels in plot 1 and plot 2.
Values: 0 (always parallel to the axis), 1 (always horizontal), 2
(always perpendicular to the axis), 3 (always vertical). See
|
cextText |
size of legend text. |
legendbar |
logical. If |
... |
aditional graphical parameters (see |
The first plot corresponds to the genetic estimation. This plot shows accumulation of colonization events as a function of population number. Each curve was created for each number of variable positions in the dataset.
The second plot corresponds to the field estimation. This plot shows accumulation of colonization events as a function of the number of variable positions. Each curve is created for each number of populations in the dataset.
This function returns two plots corresponding to the two resampling methods
used in rarecol. The first curve corresponds to the "genetic
estimation" in which a genetic resampling of every possible number of
variable position is done and, for each resample, a complete resampling of
population is done. The second curve represents the opposite method
corresponding to the "field estimation": it first resamples every possible
number of populations and, for each case, a complete resampling of
variable positions is done.
Coello, A.J., Fernandez-Mazuecos, M., Heleno, R.H., Vargas, P. (2022). PAICE: A new R package to estimate the number of inter-island colonizations considering haplotype data and sample size. Journal of Biogeography, 49(4), 577-589.DOI: 10.1111/jbi.14341
rarecol to build a rarefection curve of colonization events.
# Use 'CmonsRare' data, a dataset generated using 25 replicates # in both genetic and field sampling data(CmonsRare) plot(CmonsRare)# Use 'CmonsRare' data, a dataset generated using 25 replicates # in both genetic and field sampling data(CmonsRare) plot(CmonsRare)
A creation of rarefaction curves considering both genetic and field data. First, the function samples variable positions and then samples populations for each variable position. Second, it samples populations and then samples variable positions for each population.
rarecol(data, network, replicates_field = 10, replicates_genetic = 10, mode = c(1, 2), monitor = TRUE, file = NULL)rarecol(data, network, replicates_field = 10, replicates_genetic = 10, mode = c(1, 2), monitor = TRUE, file = NULL)
data |
a data frame containing the occurrence matrix of haplotypes in the islands of an archipelago (applicable to any island-like system). The first two columns indicate islands and populations sampled. Successive columns indicate haplotype occurrences (one column per haplotype). If present, missing haplotypes must also be included (i.e. columns without occurrences). |
network |
a data frame containing the genealogy of haplotypes. The first column
indicates the haplotype, the second column indicates its ancestral
haplotype and the third column indicates the variable position changed
between an haplotype and its ancestral haplotype. If present, missing
haplotypes must also be included. The ancestral haplotype must be
connected to an outgroup named |
replicates_field |
numeric. Number of replicates for field resampling. Each replicate adds populations from one to the total number of populations in the dataset and infers the corresponding number of colonization events. |
replicates_genetic |
numeric. Number of replicates for genetic resampling. Each replicate adds variable positions from none (chorology) to the total number of variable positions in the dataset and infers the corresponding number of colonization events. |
monitor |
logical. If |
mode |
numeric vector. Indicates which estimations must be conducted. 1 for genetic estimation and 2 for field estimation. By default the function conducts both processes. |
file |
character string determining the name of the file to save rarefaction
curves built by this function. Two files are created, one for genetic
estimation and one for field estimation. If a file name is indicated,
the function does not return any result, all the results are saved in
the indicated files. If set to |
rarecol returns an object of class
"rarecol". The return is a list containing information about the two
rarefaction curves generated.
Coello, A.J., Fernandez-Mazuecos, M., Heleno, R.H., Vargas, P. (2022). PAICE: A new R package to estimate the number of inter-island colonizations considering haplotype data and sample size. Journal of Biogeography, 49(4), 577-589.DOI: 10.1111/jbi.14341
To describe the number of colonization events observed in the most complete
case, use the function colonization. maxCol,
which estimates the number of colonization events of data generated by this
funtion. plot.rarecol to plot the result of this function.
read.rarecol to import files generated from this function.
data(CmonsData) data(CmonsNetwork) # Build rarefaction curves with 5 field and genetic replicates ## Note: more replicates are needed to build accurate curves ## Note: 5 replicates are relatively fast and adequate to ## explore the data rcol <- rarecol(data = CmonsData, network = CmonsNetwork, replicates_field = 5, replicates_genetic = 5, monitor = TRUE, mode = c(1, 2)) old.par <- par(no.readonly = TRUE) # To restore previous options par(mfrow = c(1, 2)) plot(rcol) # Plotting results par(old.par)data(CmonsData) data(CmonsNetwork) # Build rarefaction curves with 5 field and genetic replicates ## Note: more replicates are needed to build accurate curves ## Note: 5 replicates are relatively fast and adequate to ## explore the data rcol <- rarecol(data = CmonsData, network = CmonsNetwork, replicates_field = 5, replicates_genetic = 5, monitor = TRUE, mode = c(1, 2)) old.par <- par(no.readonly = TRUE) # To restore previous options par(mfrow = c(1, 2)) plot(rcol) # Plotting results par(old.par)
An import method for data generated by rarecol.
read.rarecol(gen, field)read.rarecol(gen, field)
gen, field
|
filenames of genetic and field estimation data. |
This function uses read.table to import both files
created by rarecol.
This function returns an object of class rarecol.
This object is a list in which each element is a data.frame containing
information about colonization inference.
Coello, A.J., Fernandez-Mazuecos, M., Heleno, R.H., Vargas, P. (2022). PAICE: A new R package to estimate the number of inter-island colonizations considering haplotype data and sample size. Journal of Biogeography, 49(4), 577-589.DOI: 10.1111/jbi.14341
rarecol for building of rarefaction curves of colonization
events.
data(CmonsData) data(CmonsNetwork) # Make rarefaction curves and save it in working directory, ## Note: only one replicate per sampling to it quickly rarecol(data = CmonsData, network = CmonsNetwork, replicates_field = 1, replicates_genetic = 1, monitor = TRUE, file = "rareData") # Genetic estimation has the suffix "_gen" and the field "_field" raredata <- read.rarecol(gen = "rareData_gen.csv", field = "rareData_field.csv") str(raredata) # Show structure of data imported # Remove files created file.remove("rareData_gen.csv", "rareData_field.csv")data(CmonsData) data(CmonsNetwork) # Make rarefaction curves and save it in working directory, ## Note: only one replicate per sampling to it quickly rarecol(data = CmonsData, network = CmonsNetwork, replicates_field = 1, replicates_genetic = 1, monitor = TRUE, file = "rareData") # Genetic estimation has the suffix "_gen" and the field "_field" raredata <- read.rarecol(gen = "rareData_gen.csv", field = "rareData_field.csv") str(raredata) # Show structure of data imported # Remove files created file.remove("rareData_gen.csv", "rareData_field.csv")