autocorrelation computes kinship coefficients (Loiselle or Ritland)
between pairs of individuals within specific ranges of geographic distance.
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)a Rclone table with one allele per column.
logical, option, haploid indicates the ploidy level of data1.
a table with coordinates of every units in data1.
vector, option, vecpop indicates the population name of each unit
of data1, if data1 contains several populations.
If data1 contains only one population, leave vecpop = NULL.
option, a custom list of MLL.
logical, if TRUE, Loiselle kinship coefficients are computed.
logical, if TRUE, Ritland kinship coefficients are computed.
option, TRUE keeps only MLG of data1.
option, if genet = TRUE, central_coords computes central
coordinates for each MLG/MLL.
option, if genet = TRUE, random_unit keeps coordinates of
only one unit per MLG/MLL.
option, if genet = TRUE, weighted computes a weighted
matrix over ramets.
option, if TRUE, computes distance classes of d equidistant
classes.
option, if TRUE, computes distance classes of d
classes with the same number of units pairs each.
numeric, option, number of distance classes. By default, d = 10.
option, a custom vector distance to construct distance classes.
option, if TRUE, displays kinship coefficient between pairs plotted
against distance.
numeric, option, if pvalue = TRUE, nbrepeat is the number of
resampling to enable pvalues computation.
option, if TRUE, graph is saved as .eps into working directory.
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.
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.
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.
# NOT RUN {
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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