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sp <- c("Abelia biflora",
"Abelia uniflora","Acer davidii","Acer davidii subsp. grosseri","Acer griseum",
"Acer pictum subsp. mono","Acer truncatum","Ailanthus altissima","Aralia elata",
"Berberis circumserrata","Berberis mitifolia","Betula chinensis","Betula luminifera",
"Betula platyphylla","Buckleya graebneriana","Carpinus cordata","Carpinus turczaninowii",
"Cerasus clarofolia","Cerasus polytricha","Cerasus serrulata","Cercidiphyllum japonicum",
"Cornus controversa","Cornus kousa subsp. chinensis","Cornus macrophylla",
"Cornus schindleri subsp. poliophylla","Cornus walteri","Corylus chinensis",
"Corylus heterophylla","Cotoneaster acutifolius","Cotoneaster multiflorus",
"Crataegus hupehensis","Crataegus pinnatifida","Deutzia parviflora",
"Diospyros lotus","Elaeagnus umbellata","Euonymus maackii",
"Euonymus phellomanus","Euonymus schensianus","Euptelea pleiosperma",
"Evodia daniellii","Evodia fargesii","Forsythia suspensa","Fraxinus chinensis",
"Fraxinus mandshurica","Fraxinus paxiana","Juglans cathayensis",
"Koelreuteria paniculata","Lespedeza bicolor","Lespedeza buergeri",
"Lindera obtusiloba","Litsea tsinlingensis","Lonicera hispida",
"Lonicera microphylla","Lonicera tatarinowii","Maddenia hypoleuca",
"Malus baccata","Malus honanensis","Malus hupehensis","Malus kansuensis",
"Meliosma flexuosa","Meliosma veitchiorum","Ostrya japonica","Padus buergeriana",
"Philadelphus incanus","Pinus armandii","Pinus tabuliformis","Populus davidiana",
"Quercus aliena var. acutiserrata","Quercus serrata var. brevipetiolata",
"Quercus variabilis","Rhododendron micranthum","Ribes pachysandroides",
"Salix floderusii","Sambucus williamsii","Sinowilsonia henryi",
"Sorbaria sorbifolia","Sorbus alnifolia","Sorbus hupehensis","Spiraea hirsuta",
"Stachyurus chinensis","Styrax hemsleyanus","Styrax obassis","Symplocos paniculata",
"Tilia japonica","Tilia paucicostata","Toxicodendron vernicifluum","Ulmus davidiana",
"Viburnum betulifolium","Viburnum opulus var. sargentii","Yulania denudata")
set.seed(12345)
library(sads)
library(stringr)
library(vegan)
library(coefplot)
library(knitr)
library(lme4)
library(broom)
spcode <- toupper(make.cepnames(sp))
nsp <- length(spcode)
nind <- 12700
temp <- rls(n = nsp, N = 10000, alpha = 6)
sppool <- rep(spcode, temp)
species <- sample(sppool, nind, replace = TRUE)
table(species)
sum(table(species))
gx <- round(runif(nind)*250, digits = 2)
gy <- round(runif(nind)*200, digits = 2)
xlab20 <- cut(gx, breaks = seq(0, 260, by = 20), labels = 1:13)
ylab20 <- cut(gy, breaks = seq(0, 220, by = 20), labels = 1:11)
sort(unique(ylab20))
sort(unique(xlab20))
quadrat20 <- paste(str_pad(xlab20, 2, pad ="0"),
str_pad(ylab20, 2, pad ="0"), sep = "")
dbh = dbh <-round(rexp(nind,rate =2)*50+1, 1)
tag <- paste("BYS", str_pad(1:nind, 6, pad ="0"), sep ="")
status <- rbinom(nind, size = 1, prob = 0.01)
bys <- data.frame(tag, species, gx, gy, quadrat20, dbh, status)
nindividual <- table(bys$species)
sp_to_select <- names(nindividual[nindividual >= 30])
sub_bys <- subset(bys, subset = species %in% sp_to_select)
sub_bys_test <- sub_bys[1:2000,]
compute_neighbour_area <- function (dat, r = NULL)
{
if(any(is.na(dat))){
dat <- na.omit(dat)
warning("NAs detected in the data and the entries
containing NAs have been omitted.")
}
dat$ID <- 1:nrow(dat)
if(is.null(dat$gx)){
stop("Column gx is missing")
}
if(is.null(dat$gy)){
stop("Column gy is missing")
}
if(is.null(dat$species)){
stop("Column species is missing")
}
if(is.null(dat$dbh)){
stop("Column species is missing")
}
if(is.null(r)){
stop("r is not specified")
}
gx <- dat$gx
gy <- dat$gy
dat$species <- as.character(dat$species)
species_each_id <- dat$species
area_of_neighbours_same_species <- rep(NA, nrow(dat))
area_of_neighbours_diff_species <- rep(NA, nrow(dat))
print(paste(nrow(dat), "rows to process."))
for (i in 1:nrow(dat)){
if(i %% 100 == 0 | i == 1){
print(paste("Computing neighbourhood area for row:", i))
}
x <- gx[i]
y <- gy[i]
dat_sub <- dat[(gx < x + r) & (gx > x - r) & (gy < y + r) & (gy > y - r), ]
dat_sub <- dat_sub[dat_sub$ID != i, ]
if(nrow(dat_sub) > 0){
sp <- rep(NA, nrow(dat_sub))
for(j in 1:nrow(dat_sub)){
mat <- data.frame(c(x, dat_sub$gx[j]), c(y, dat_sub$gy[j]))
sp[j] <- (dist(mat) <= r)
}
area_of_neighbours <- pi*(dat_sub$dbh[sp]/2)^2
if(length(area_of_neighbours[dat_sub$species[sp] %in%
species_each_id[i]]) != 0 ){
area_of_neighbours_same_species[i] <-
sum(area_of_neighbours[dat_sub$species[sp] %in% species_each_id[i]])
}
if(length(area_of_neighbours[!dat_sub$species[sp] %in%
species_each_id[i]]) != 0){
area_of_neighbours_diff_species[i] <-
sum(area_of_neighbours[!dat_sub$species[sp] %in%
species_each_id[i]])
}
} else {
area_of_neighbours_same_species[i] <- NA
area_of_neighbours_diff_species[i] <- NA
}
}
res <- data.frame(area_of_neighbours_same_species,
area_of_neighbours_diff_species)
colnames(res) <- paste(c("area_of_neighbours_same_species",
"area_of_neighbours_diff_species"),
"_r_", r, sep = "")
return(res)
}
neighbour_area_res10 <- compute_neighbour_area(sub_bys_test, r = 10)
sub_bys_test <- cbind(sub_bys_test, neighbour_area_res10)
head(sub_bys_test)
sub_bys_test$dbh <- scale(sub_bys_test$dbh)
sub_bys_test$area_of_neighbours_same_species_r_10 <-
scale( sub_bys_test$area_of_neighbours_same_species_r_10)
sub_bys_test$area_of_neighbours_diff_species_r_10 <-
scale (sub_bys_test$area_of_neighbours_diff_species_r_10)
glmm_model_10m = glmer(status ~ dbh +
area_of_neighbours_same_species_r_10 +
area_of_neighbours_diff_species_r_10 +
(1+dbh|species)+(1|quadrat20),
data=sub_bys_test,
family = binomial(link="cloglog"))
tidy(glmm_model_10m)
glance(glmm_model_10m)
coefplot(glmm_model_10m)
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