Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = ">", dev = 'pdf')
## -----------------------------------------------------------------------------
library(RClone)
data(posidonia)
## ---- echo = FALSE, results = 'asis'------------------------------------------
knitr::kable(posidonia[1:10,1:8], align = "c")
## ---- eval = FALSE------------------------------------------------------------
# data(zostera)
# head(zostera)
## ---- echo = FALSE------------------------------------------------------------
data(zostera)
knitr::kable(head(zostera), align = "c")
## -----------------------------------------------------------------------------
popvec <- zostera[,1] #futur vecpop
coord_zostera <- zostera[,2:3] #futur coordinates
zostera <- zostera[,4:ncol(zostera)] #dataset
zostera <- convert_GC(zostera, 3) #We used "3" because this is the length of each allele.
## ---- eval = FALSE------------------------------------------------------------
# head(zostera)
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(zostera[1:6,1:7], align = "c")
## ---- eval = FALSE------------------------------------------------------------
# sort_all(zostera)
## ---- eval = FALSE------------------------------------------------------------
# #library(adegenet)
# #with data1, a genind object from Adegenet:
#
# test <- genind2df(data1)
# data2 <- convert_GC(test, 3, "/")
# #only if yours alleles are of length "3"
## ---- eval = FALSE------------------------------------------------------------
# list_all_tab(zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# list_all_tab(haplodata, haploid = TRUE, vecpop = haplovec)
## ---- eval = FALSE------------------------------------------------------------
# list_all_tab(zostera, vecpop = popvec)
## -----------------------------------------------------------------------------
#SaintMalo
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(list_all_tab(zostera, vecpop = popvec)[[1]], align = "c")
## -----------------------------------------------------------------------------
#Arcouest
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(list_all_tab(zostera, vecpop = popvec)[[2]], align = "c")
## ---- eval = FALSE------------------------------------------------------------
# MLG_tab(zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# MLG_tab(haplodata, vecpop = haplovec)
## ---- eval = FALSE------------------------------------------------------------
# MLG_tab(zostera, vecpop = popvec)[[1]]
# #SaintMalo
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(MLG_tab(zostera, vecpop = popvec)[[1]][1:5,], align = "c")
## ---- eval = FALSE------------------------------------------------------------
# freq_RR(zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# freq_RR(haplodata, haploid = TRUE, vecpop = haplovec)
## ---- eval = FALSE------------------------------------------------------------
# freq_RR(zostera, vecpop = popvec) #on ramets
# freq_RR(zostera, vecpop = popvec, genet = TRUE) #on genets
# freq_RR(zostera, vecpop = popvec, RR = TRUE) #Round-Robin methods
## ---- eval = FALSE------------------------------------------------------------
# freq_RR(zostera, vecpop = popvec)[[1]]
# #SaintMalo
## ---- echo = FALSE------------------------------------------------------------
res <- cbind(freq_RR(zostera, vecpop = popvec)[[1]], freq_RR(zostera, vecpop = popvec, genet = TRUE)[[1]][,3], freq_RR(zostera, vecpop = popvec, RR = TRUE)[[1]][,3])[1:7,]
colnames(res)[3:5] <- c("freq_ramet", "freq_genet", "freq_RR")
knitr::kable(res, align = "c")
## ---- eval = FALSE------------------------------------------------------------
# sample_loci(zostera, vecpop = popvec, nbrepeat = 1000)
## ---- eval = FALSE------------------------------------------------------------
# sample_loci(haplodata, haploid = TRUE, vecpop = haplovec, nbrepeat = 1000)
## ---- eval = FALSE------------------------------------------------------------
# sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, He = TRUE) #with He results
# sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, graph = TRUE) #graph displayed
# sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, bar = TRUE)
# #progression bar, could be time consuming
# sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, export = TRUE)
# #graph export in .eps
## ---- eval = FALSE------------------------------------------------------------
# res <- sample_loci(zostera, vecpop = popvec, nbrepeat = 1000, He = TRUE)
# names(res)
## ---- echo = FALSE------------------------------------------------------------
data(resvigncont2)
names(resvigncont2$res2_SU1)
## ---- eval = FALSE------------------------------------------------------------
# names(res$SaintMalo)
## ---- eval = FALSE------------------------------------------------------------
# names(resvigncont2$res2_SU1$SaintMalo)
## ---- eval = FALSE------------------------------------------------------------
# #Results: MLG
# res$Arcouest$res_MLG
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(resvigncont2$res2_SU1$Arcouest$res_MLG, align = "c")
## ---- eval = FALSE------------------------------------------------------------
# #Results: alleles
# res$Arcouest$res_alleles
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(res$Arcouest$res_alleles, align = "c")
## -----------------------------------------------------------------------------
#Results: raw data
#res$Arcouest$raw_He
#res$Arcouest$raw_MLG
#res$Arcouest$raw_all
## ---- eval = FALSE------------------------------------------------------------
# boxplot(res$SaintMalo$raw_MLG, main = "Genotype accumulation curve",
# xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")
## ---- echo = FALSE------------------------------------------------------------
boxplot(resvigncont2$res2_SU1$SaintMalo$raw_MLG, main = "Genotype accumulation curve", xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")
## ---- eval = FALSE------------------------------------------------------------
# boxplot(res$Arcouest$raw_MLG, main = "Genotype accumulation curve",
# xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")
## ---- echo = FALSE------------------------------------------------------------
boxplot(resvigncont2$res2_SU1$Arcouest$raw_MLG, main = "Genotype accumulation curve", xlab = "Number of loci sampled", ylab = "Number of multilocus genotypes")
## ---- eval = FALSE------------------------------------------------------------
# sample_units(zostera, vecpop = popvec, nbrepeat = 1000)
## ---- eval = FALSE------------------------------------------------------------
# sample_units(haplodata, haploid = TRUE, vecpop = haplovec, nbrepeat = 1000)
## ---- eval = FALSE------------------------------------------------------------
# pgen(zostera, vecpop = popvec)
# psex(zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# pgen(haplodata, haploid = TRUE, vecpop = haplovec)
# psex(haplodata, haploid = TRUE, vecpop = haplovec)
## ---- eval = FALSE------------------------------------------------------------
# #allelic frequencies computation:
# psex(zostera, vecpop = popvec) #psex on ramets
# psex(zostera, vecpop = popvec, genet = TRUE) #psex on genets
# psex(zostera, vecpop = popvec, RR = TRUE) #psex with Round-Robin method
# #psex computation
# psex(zostera, vecpop = popvec) #psex with one psex per replica
# psex(zostera, vecpop = popvec, MLGsim = TRUE) #psex MLGsim method
# #pvalues:
# psex(zostera, vecpop = popvec, nbrepeat = 100) #with p-values
# psex(zostera, vecpop = popvec, nbrepeat = 1000, bar = TRUE)
# #with p-values and a progression bar
## ---- eval = FALSE------------------------------------------------------------
# res <- psex(zostera, vecpop = popvec, RR = TRUE, nbrepeat = 1000)
# res$Arcouest[[1]]
# #if nbrepeat != 0, res contains a table of psex values and a vector of sim-psex values
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(resvigncont2$res2_PS2, align = "c")
## ---- eval = FALSE------------------------------------------------------------
# res$Arcouest[[2]] #a part of sim-psex values
## ---- echo = FALSE------------------------------------------------------------
resvigncont2$res2_PS1$Arcouest[[2]][1:10]
## ---- eval = FALSE------------------------------------------------------------
# Fis(zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# Fis(zostera, vecpop = popvec) #Fis on ramets
# Fis(zostera, vecpop = popvec, genet = TRUE) #Fis on genets
# Fis(zostera, vecpop = popvec, RR = TRUE) #Fis with Round-Robin methods
# #RR = TRUE contains two results : a table with allelic frequencies
# #and a table with Fis results
## ---- eval = FALSE------------------------------------------------------------
# Fis(zostera, vecpop = popvec, RR = TRUE)$Arcouest[[2]]
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(Fis(zostera, vecpop = popvec, RR = TRUE)$Arcouest[[2]], align = "c")
## ---- eval = FALSE------------------------------------------------------------
# pgen_Fis(zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# #allelic frequencies:
# psex_Fis(zostera, vecpop = popvec) #psex Fis on ramets
# psex_Fis(zostera, vecpop = popvec, genet = TRUE) #psex Fis on genets
# psex_Fis(zostera, vecpop = popvec, RR = TRUE) #psex Fis with Round-Robin method
# #psex computation
# psex_Fis(zostera, vecpop = popvec) #psex Fis, one for each replica
# psex_Fis(zostera, vecpop = popvec, MLGsim = TRUE) #psex Fis with MLGsim method
# #pvalues
# psex_Fis(zostera, vecpop = popvec, nbrepeat = 100) #with p-values
# psex_Fis(zostera, vecpop = popvec, nbrepeat = 1000, bar = TRUE)
# #with p-values and a progression bar
## ---- eval = FALSE------------------------------------------------------------
# res <- psex_Fis(zostera, vecpop = popvec, RR = TRUE, nbrepeat = 1000)
# res$Arcouest[[1]]
# #if nbrepeat != 0, res contains a table of psex values and a vector of sim-psex Fis values
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(resvigncont2$res2_PS4, align = "c")
## ---- eval = FALSE------------------------------------------------------------
# res$Arcouest[[2]] #a part of sim psex Fis values
## ---- echo = FALSE------------------------------------------------------------
resvigncont2$res2_PS3$Arcouest[[2]][1:10]
## -----------------------------------------------------------------------------
data(popsim)
vecsim <- c(rep(1,50), rep(2,50))
## ---- eval = FALSE------------------------------------------------------------
# #genetic distances computation, distance on allele differences:
# respop <- genet_dist(popsim, vecpop = vecsim)
# ressim <- genet_dist_sim(popsim, vecpop = vecsim , nbrepeat = 1000)
# #theoretical distribution: sexual reproduction
# ressimWS <- genet_dist_sim(popsim, vecpop = vecsim , genet = TRUE, nbrepeat = 1000)
# #idem, without selfing
## ---- echo = FALSE------------------------------------------------------------
respop <- resvigncont2$respop
ressim <- resvigncont2$ressim
ressimWS <- resvigncont2$ressimWS
## ---- fig.width = 10, fig.height = 8------------------------------------------
#graph prep.:
#first pop:
p1 <- hist(respop[[1]]$distance_matrix, freq = FALSE, col = rgb(0,0.4,1,1),
breaks = seq(0, max(respop[[1]]$distance_matrix)+1, 1),
main = "pop_1_sim", xlab = "")
p2 <- hist(ressim[[1]]$distance_matrix, freq = FALSE, col = rgb(0.7,0.9,1,0.5),
breaks = seq(0, max(ressim[[1]]$distance_matrix)+1, 1),
main = "pop_1_SR", xlab = "")
p3 <- hist(ressimWS[[1]]$distance_matrix, freq = FALSE, col = rgb(0.9,0.5,1,0.3),
breaks = seq(0, max(ressimWS[[1]]$distance_matrix)+1, 1),
main = "pop_1_SRWS", xlab = "")
limx <- max(max(respop[[1]]$distance_matrix), max(ressim[[1]]$distance_matrix),
max(ressimWS[[1]]$distance_matrix))
#graph superposition:
plot(p1, col = rgb(0,0.4,1,1), freq = FALSE, xlim = c(0,limx),
main = paste("pop", unique(vecsim)[[1]], sep = "_"),
xlab = "Genetic distances")
plot(p2, col = rgb(0.7,0.9,1,0.5), freq = FALSE, add = TRUE)
plot(p3, col = rgb(0.9,0.5,1,0.3), freq = FALSE, add = TRUE)
#adding a legend:
leg.txt <- c("original data","simulated data", "without selfing")
col <- c(rgb(0,0.4,1,1), rgb(0.7,0.9,1,0.5), rgb(0.9,0.5,1,0.3))
legend("top", fill = col, leg.txt, plot = TRUE, bty = "o", box.lwd = 1.5,
bg = "white")
#second pop:
p <- 2 #useful if several populations: just change *p* and run lines
p1 <- hist(respop[[p]]$distance_matrix, freq = FALSE, col = rgb(0,0.4,1,1),
breaks = seq(0, max(respop[[p]]$distance_matrix)+1, 1),
main = paste("pop", p, sep = "_"), xlab = "")
p2 <- hist(ressim[[p]]$distance_matrix, freq = FALSE, col = rgb(0.7,0.9,1,0.5),
breaks = seq(0, max(ressim[[p]]$distance_matrix)+1, 1),
main = paste("pop", p, sep = "_"), xlab = "")
p3 <- hist(ressimWS[[p]]$distance_matrix, freq = FALSE, col = rgb(0.9,0.5,1,0.3),
breaks = seq(0, max(ressimWS[[p]]$distance_matrix)+1, 1),
main = paste("pop", p, sep = "_"), xlab = "")
limx <- max(max(respop[[p]]$distance_matrix), max(ressim[[p]]$distance_matrix),
max(ressimWS[[p]]$distance_matrix))
#graph superposition:
plot(p1, col = rgb(0,0.4,1,1), freq = FALSE, xlim = c(0,limx),
main = paste("pop", unique(vecsim)[[p]], sep = "_"),
xlab = "Genetic distances")
plot(p2, col = rgb(0.7,0.9,1,0.5), freq = FALSE, add = TRUE)
plot(p3, col = rgb(0.9,0.5,1,0.3), freq = FALSE, add = TRUE)
#adding a legend:
leg.txt <- c("original data","simulated data", "without selfing")
col <- c(rgb(0,0.4,1,1), rgb(0.7,0.9,1,0.5), rgb(0.9,0.5,1,0.3))
legend("top", fill = col, leg.txt, plot = TRUE, bty = "o", box.lwd = 1.5,
bg = "white")
## -----------------------------------------------------------------------------
#determining alpha2
table(respop[[1]]$distance_matrix)
#alpha2 = 3
## -----------------------------------------------------------------------------
#creating MLL list:
MLLlist <- MLL_generator(popsim, vecpop = vecsim, alpha2 = c(3,0))
##This will create a list of MLL (alpha2 = 3) and MLG (alpha2 = 0) !
#or
res <- genet_dist(popsim, vecpop = vecsim, alpha2 = c(3,0))
MLLlist <- MLL_generator2(list(res[[1]]$potential_clones,
res[[2]]$potential_clones), MLG_list(popsim, vecpop = vecsim), vecpop = vecsim)
## ---- eval = FALSE------------------------------------------------------------
# respop <- genet_dist(haplodata, haploid = TRUE, vecpop = vechaplo)
# ressim <- genet_dist_sim(haplodata, haploid = TRUE, vecpop = vechaplo,
# nbrepeat = 1000)
# MLLlist <- MLL_generator(haplodata, haploid = TRUE, vecpop = vechaplo,
# alpha2 = c(3,0))
# #or
# res <- genet_dist(haplodata, haploid = TRUE, vecpop = vechaplo, alpha2 = c(3,0))
# MLLlist <- MLL_generator2(list(res[[1]]$potential_clones, res[[2]]$potential_clones),
# haploid = TRUE, MLG_list(haplodata, vecpop = vechaplo), vecpop = vechaplo)
## ---- eval = FALSE------------------------------------------------------------
# clonal_index(zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# clonal_index(popsim, vecpop = vecsim, listMLL = MLLlist)
## ---- eval = FALSE------------------------------------------------------------
# clonal_index(haplodata, vecpop = vechaplo)
## ---- eval = FALSE------------------------------------------------------------
# clonal_index(zostera, vecpop = popvec)
## ---- echo = FALSE, results = 'asis'------------------------------------------
knitr::kable(resvigncont2$res2_ci, align = "c")
## ---- eval = FALSE------------------------------------------------------------
# Pareto_index(zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# Pareto_index(popsim, vecpop = vecsim, listMLL = MLLlist)
## ---- eval = FALSE------------------------------------------------------------
# Pareto_index(haplodata, vecpop = vechaplo)
## ---- eval = FALSE------------------------------------------------------------
# Pareto_index(zostera, vecpop = popvec, graph = TRUE) #classic graphic
# Pareto_index(zostera, vecpop = popvec, legends = 2, export = TRUE)
# #export option
# Pareto_index(zostera, vecpop = popvec, full = TRUE) #all results
## ---- eval = FALSE------------------------------------------------------------
# res <- Pareto_index(zostera, vecpop = popvec, full = TRUE, graph = TRUE, legends = 2)
## ---- echo = FALSE------------------------------------------------------------
require(RClone)
resz <- split(zostera, popvec)
res1 <- Pareto_index(resz[[1]], full = TRUE, graph = TRUE, legends = 2)
res2 <- Pareto_index(resz[[2]], full = TRUE, graph = TRUE, legends = 2)
## ---- eval = FALSE------------------------------------------------------------
# names(res$SaintMalo)
## ---- echo = FALSE------------------------------------------------------------
names(res1)
## ---- eval = FALSE------------------------------------------------------------
# res$SaintMalo$Pareto
## ---- echo = FALSE------------------------------------------------------------
res1$Pareto
## ---- eval = FALSE------------------------------------------------------------
# res$SaintMalo$c_Pareto
## ---- echo = FALSE------------------------------------------------------------
res1$c_Pareto
## ---- eval = FALSE------------------------------------------------------------
# #res$SaintMalo$regression_results
# #res$SaintMalo$coords_Pareto #points coordinates
## ---- eval = FALSE------------------------------------------------------------
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE)
## ---- eval = FALSE------------------------------------------------------------
# autocorrelation(popsim, coords = coord_sim, Loiselle = TRUE, listMLL = MLLlist)
## ---- eval = FALSE------------------------------------------------------------
# autocorrelation(haplodata, haploid = TRUE, coords = coord_haplo, Loiselle = TRUE)
## ---- eval = FALSE------------------------------------------------------------
# #kinship distances:
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE)
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Ritland = TRUE)
#
# #ramets/genets methods:
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE)
# #ramets
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec,, Loiselle = TRUE,
# genet = TRUE, central_coords = TRUE)
# #genets, central coordinates of each MLG
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE,
# genet = TRUE, random_unit = TRUE)
# #genets, one random unit per MLG
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE,
# genet = TRUE, weighted = TRUE)
# #genets, with weighted matrix on kinships
#
# #distance classes construction:
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE)
# #10 equidistant classes
# distvec <- c(0,10,15,20,30,50,70,76.0411074)
# #with 0, min distance and 76.0411074, max distance
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE,
# vecdist = distvec) #custom distance vector
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE,
# class1 = TRUE, d = 7) #7 equidistant classes
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Loiselle = TRUE,
# class2 = TRUE, d = 7)
# #7 distance classes with the same number of units in each
#
# #graph options:
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Ritland = TRUE,
# graph = TRUE) #displays graph
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Ritland = TRUE,
# export = TRUE) #export graph
#
# #pvalues computation
# autocorrelation(zostera, coords = coord_zostera, vecpop = popvec, Ritland = TRUE,
# nbrepeat = 1000)
## ---- eval = FALSE------------------------------------------------------------
# res <- autocorrelation(zostera, coords = coord_zostera, vecpop = popvec,
# Ritland = TRUE, nbrepeat = 1000, graph = TRUE)
## ---- echo = FALSE------------------------------------------------------------
plot(resvigncont2$res2auto1$Main_results[,3], resvigncont2$res2auto1$Main_results[,6], main = "Spatial aucorrelation analysis",
ylim = c(-0.2,0.2), type = "l", xlab = "Spatial distance", ylab = "Coancestry (Fij)")
points(resvigncont2$res2auto1$Main_results[,3], resvigncont2$res2auto1$Main_results[,6], pch = 20)
abline(h = 0, lty = 3)
plot(resvigncont2$res2auto2$Main_results[,3], resvigncont2$res2auto2$Main_results[,6], main = "Spatial aucorrelation analysis",
ylim = c(-0.2,0.2), type = "l", xlab = "Spatial distance", ylab = "Coancestry (Fij)")
points(resvigncont2$res2auto2$Main_results[,3], resvigncont2$res2auto2$Main_results[,6], pch = 20)
abline(h = 0, lty = 3)
## ---- eval = FALSE------------------------------------------------------------
# names(res$Arcouest)
## ---- echo = FALSE------------------------------------------------------------
names(resvigncont2$res2auto2)
## ---- eval = FALSE------------------------------------------------------------
# res$Arcouest$Main_results #enables graph reproduction
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(resvigncont2$res2auto2$Main_results, align = "c")
## ---- eval = FALSE------------------------------------------------------------
# apply(res$Arcouest$Main_results, 2, mean)[6] #mean Fij
## ---- echo = FALSE------------------------------------------------------------
apply(resvigncont2$res2auto2$Main_results, 2, mean)[6]
## ---- eval = FALSE------------------------------------------------------------
# res$Arcouest$Slope_and_Sp_index #gives b and Sp indices
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(resvigncont2$res2auto2$Slope_and_Sp_index, align = "c")
## -----------------------------------------------------------------------------
#raw data:
#res$Arcouest$Slope_resample
#res$Arcouest$Kinship_resample
#res$Arcouest$Matrix_kinship_results
#res$Arcouest$Class_kinship_results
#res$Arcouest$Class_distance_results
## ---- eval = FALSE------------------------------------------------------------
# clonal_sub(zostera, coords = coord_zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# clonal_sub(popsim, coords = coord_sim, listMLL = MLLlist)
## ---- eval = FALSE------------------------------------------------------------
# clonal_sub(haplodata, haploid = TRUE, coords = coord_haplo)
## ---- eval = FALSE------------------------------------------------------------
# clonal_sub(posidonia, coords = coord_posidonia) #basic, with 10 equidistant classes
# distvec <- c(0,10,15,20,30,50,70,76.0411074)
# #with 0, min distance and 76.0411074, max distance
# clonal_sub(zostera, coords = coord_zostera, vecpop = popvec, vecdist = distvec)
# #custom distance classes
# clonal_sub(zostera, coords = coord_zostera, vecpop = popvec, class1 = TRUE, d = 7)
# #7 equidistant classes
# clonal_sub(zostera, coords = coord_zostera, vecpop = popvec, class1 = TRUE, d = 7)
# #7 distance classes with the same number of units in each
## -----------------------------------------------------------------------------
res <- clonal_sub(zostera, coords = coord_zostera, vecpop = popvec)
res$Arcouest[[1]] #Global clonal subrange
## ---- eval = FALSE------------------------------------------------------------
# res$Arcouest$clonal_sub_tab #details per class
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(res$Arcouest$clonal_sub_tab, align ="c")
## ---- eval = FALSE------------------------------------------------------------
# agg_index(zostera, coords = coord_zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# agg_index(popsim, coords = coord_sim, listMLL = MLLlist)
## ---- eval = FALSE------------------------------------------------------------
# agg_index(haplodata, coords = coord_haplo)
## ---- eval = FALSE------------------------------------------------------------
# agg_index(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 100)
# #pvalue computation
# agg_index(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 1000,
# bar = TRUE) #could be time consuming
## ---- eval = FALSE------------------------------------------------------------
# res <- agg_index(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 1000)
## ---- eval = FALSE------------------------------------------------------------
# res$SaintMalo$results #Aggregation index
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(resvigncont2$res2_agg$SaintMalo$results, align = "c")
## -----------------------------------------------------------------------------
#res$SaintMalo$simulation #vector of sim aggregation index
## ---- eval = FALSE------------------------------------------------------------
# #for zostera, centers of quadra is at 15,10
# edge_effect(zostera, coords = coord_zostera, vecpop = popvec,
# center = rep(c(15,10),2))
## ---- eval = FALSE------------------------------------------------------------
# edge_effect(popsim, coords = coord_sim, center = rep(c(15,10),2), listMLL = MLLlist)
## ---- eval = FALSE------------------------------------------------------------
# edge_effect(haplodata, coords = coord_haplo, center = rep(c(15,10),2))
## ---- eval = FALSE------------------------------------------------------------
# edge_effect(zostera, coords = coord_zostera, vecpop = popvec, center = rep(c(15,10),2),
# nbrepeat = 100) #pvalue computation
# edge_effect(zostera, coords = coord_zostera, vecpop = popvec, center = rep(c(15,10),2),
# nbrepeat = 1000, bar = TRUE) #could be time consuming
## ---- eval = FALSE------------------------------------------------------------
# res <- edge_effect(zostera, coords = coord_zostera, vecpop = popvec,
# center = rep(c(15,10),2), nbrepeat = 100) #better put 1000 nbrepeat at least
## ---- eval = FALSE------------------------------------------------------------
# res$SaintMalo$results #Aggregation index
## ---- echo = FALSE------------------------------------------------------------
knitr::kable(resvigncont2$res2_ee$SaintMalo$results, align = "c")
## -----------------------------------------------------------------------------
#res$SaintMalo$simulation #vector of sim aggregation index
## ---- eval = FALSE------------------------------------------------------------
# GenClone(zostera, coords = coord_zostera, vecpop = popvec)
## ---- eval = FALSE------------------------------------------------------------
# GenClone(popsim, coords = coord_sim, listMLL = MLLlist)
## ---- eval = FALSE------------------------------------------------------------
# GenClone(haplodata, haploid = TRUE, coords = coord_haplo)
## ---- eval = FALSE------------------------------------------------------------
# GenClone(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 100) #pvalues
# GenClone(zostera, coords = coord_zostera, vecpop = popvec, nbrepeat = 1000, bar = TRUE)
# #could be time consuming
## ---- eval = FALSE------------------------------------------------------------
# GenClone(zostera, coords = coord_zostera, vecpop = popvec)
## ---- echo = FALSE, results = 'asis'------------------------------------------
knitr::kable(resvigncont2$res2_gen[,1:9], longtable = TRUE, align = "c")
knitr::kable(resvigncont2$res2_gen[,10:16], longtable = TRUE, align = "c")
knitr::kable(resvigncont2$res2_gen[,17:24], longtable = TRUE, align = "c")
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