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 resampling 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 resampling approach, a unit of each MLG/MLL is randomly picked at each
permutation.
The pvalue of mean kinship coefficients is related with the overall mean kinship
coefficient: upper pvalue (Monte Carlo) if greater or equal to the overall;
otherwise, lower pvalue.
For b
and Sp
, their pvalue correspond to upper pvalue.
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 pvalue.
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 <[email protected]>
Author: Sophie ArnaudHaond <[email protected]>
Contributor: Solenn Stoeckel
The R implementation of RClone
was written by Diane Bailleul.
The design was inspired by GenClone program described in ArnaudHaond & Belkhir (2007).
Loiselle et al., 1995, Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae).
Ritland, 1996, A markerbased method for inferences about quantitative inheritance in natural populations.
ArnaudHaond et al., 2007, Standardizing methods to address clonality in population studies.
Vekemans & Hardy, 2004, New insights from finescale 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)

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