Description Usage Arguments Details Value Author(s) References See Also Examples
autocorrelation
computes kinship coefficients (Loiselle or Ritland)
between pairs of individuals within specific ranges of geographic distance.
1 2 3 4 5 | autocorrelation(data1, haploid = FALSE, coords = NULL, vecpop = NULL, listMLL = NULL,
Loiselle = FALSE, Ritland = FALSE,
genet = FALSE, central_coords = FALSE, random_unit = FALSE, weighted = FALSE,
class1 = FALSE, class2 = FALSE, d = NULL, vecdist = NULL,
graph = FALSE, nbrepeat = NULL, export = FALSE)
|
data1 |
a |
haploid |
logical, option, |
coords |
a table with coordinates of every units in |
vecpop |
vector, option, |
listMLL |
option, a custom list of MLL. |
Loiselle |
logical, if |
Ritland |
logical, if |
genet |
option, |
central_coords |
option, if |
random_unit |
option, if |
weighted |
option, if |
class1 |
option, if |
class2 |
option, if |
d |
numeric, option, number of distance classes. By default, d = 10. |
vecdist |
option, a custom vector distance to construct distance classes. |
graph |
option, if |
nbrepeat |
numeric, option, if |
export |
option, if |
By default, d = 10
and autocorrelation
computes 10
equidistant distance classes for all the ramets pairs.
The function proposes 3 others options:
class1
fixing d
equidistant classes,
class2
fixing d
distance classes with the same
number of units pairs,
maxdist = TRUE
allowing the user to give a vector
vecdist
of intervals.
The function computes one of the two average kinship coefficients: Loiselle and Ritland.
Autocorrelation can be compute on ramets level, or genet level with:
central coordinates of each MLG/MLL,
a re-sampling approach which randomly allocates one of the unit's coordinates per MLG/MLL (Alberto 2005),
keeping all the ramets but weighting the matrix distances by a weighted matrix (Wagner 2005) where units of the same MLG/MLL are set to 0.
A permutation approach could be perform to assess pvalue and confidence
intervals by permutation of the geographic coordinates among units.
For the re-sampling approach, a unit of each MLG/MLL is randomly picked at each
permutation.
The p-value of mean kinship coefficients is related with the overall mean kinship
coefficient: upper p-value (Monte Carlo) if greater or equal to the overall;
otherwise, lower p-value.
For b
and Sp
, their p-value correspond to upper p-value.
autocorrelation
returns a list (one population) or lists of list
(several populations) of:
Main_results
, a table with for each class, min, max, mean and
ln(mean) of distance between two units, the number of pairs, the mean
kinship coefficient and
if pvalue = TRUE
, the pvalue.
Slope_and_Sp_index
, a table with slopes of the regression between
genetic and geographic/log(geographic) distances and Sp
and
Sp_log
(used to quantify Spatial Genetic Structure, Vekemans and Hardy, 2004)
as observed values, mean and
standard deviation of the simulated values, 95% and 90% confidence
intervals and p-value.
Slope_resample
, a table with slopes of the regression between
genetic and geographic/log(geographic) distances at each pvalue.
Kinship_resample
, a table with for each class in column and each
pvalue in row the mean kinship coefficient.
Matrix_kinship_results
, a dist object with kinship coefficients.
Class_kinship_results
, a list of kinship coefficients by distance
class.
Class_distance_results
, a list of geographical distances by distance
class.
Creator/Author: Diane Bailleul <diane.bailleul.pro@gmail.com>
Author: Sophie Arnaud-Haond <sophie.arnaud@ifremer.fr>
Contributor: Solenn Stoeckel
The R implementation of RClone
was written by Diane Bailleul.
The design was inspired by GenClone program described in Arnaud-Haond & Belkhir (2007).
Loiselle et al., 1995, Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae).
Ritland, 1996, A marker-based method for inferences about quantitative inheritance in natural populations.
Arnaud-Haond et al., 2007, Standardizing methods to address clonality in population studies.
Vekemans & Hardy, 2004, New insights from fine-scale spatial genetic structure analyses in plant populations.
kinship_Loiselle
, kinship_Ritland
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | data(posidonia)
data(coord_posidonia)
distGC <- c(0,10,15,20,30,50,70,76.0411073)
#res1 <- autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE, nbrepeat = 1000)
#res2 <- autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE,
#class2 = TRUE, d = 7)
#res2[[1]] #Main_results
#res1[[2]] #Slope_and_Sp_index
#res2[[3]] #Slope_and_Sp_index
#res3 <- autocorrelation(posidonia, coords = coord_posidonia, Loiselle = TRUE,
#vecdist = distGC, graph = TRUE)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.