learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_phytr$contributions
(macrichfinal_phytr$self.statistics$mean.null-macrichfinal_phytr$cv.statistics$deviance.mean)/macrichfinal_phytr$self.statistics$mean.null
macrichfinal_cladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_cladr$contributions
(macrichfinal_cladr$self.statistics$mean.null-macrichfinal_cladr$cv.statistics$deviance.mean)/macrichfinal_cladr$self.statistics$mean.null
macrichfinal_cladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_cladr$contributions
(macrichfinal_cladr$self.statistics$mean.null-macrichfinal_cladr$cv.statistics$deviance.mean)/macrichfinal_cladr$self.statistics$mean.null
macrichfinal_cladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_cladr$contributions
(macrichfinal_cladr$self.statistics$mean.null-macrichfinal_cladr$cv.statistics$deviance.mean)/macrichfinal_cladr$self.statistics$mean.null
macrichfinal_cladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_cladr$contributions
(macrichfinal_cladr$self.statistics$mean.null-macrichfinal_cladr$cv.statistics$deviance.mean)/macrichfinal_cladr$self.statistics$mean.null
acrichfinal_phytcladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17,3),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_phytcladr$contributions
(macrichfinal_phytcladr$self.statistics$mean.null-macrichfinal_phytcladr$cv.statistics$deviance.mean)/macrichfinal_phytcladr$self.statistics$mean.null
rm(acrichfinal_phytcladr)
macrichfinal_phytcladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17,3),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_phytcladr$contributions
(macrichfinal_phytcladr$self.statistics$mean.null-macrichfinal_phytcladr$cv.statistics$deviance.mean)/macrichfinal_phytcladr$self.statistics$mean.null
macrichfinal_phytcladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17,3),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_phytcladr$contributions
(macrichfinal_phytcladr$self.statistics$mean.null-macrichfinal_phytcladr$cv.statistics$deviance.mean)/macrichfinal_phytcladr$self.statistics$mean.null
macrichfinal_phytcladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17,3),
gbm.y = 7, family = "poisson",
tree.complexity = 3,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_phytcladr$contributions
(macrichfinal_phytcladr$self.statistics$mean.null-macrichfinal_phytcladr$cv.statistics$deviance.mean)/macrichfinal_phytcladr$self.statistics$mean.null
macrichfinal_phytcladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17,3),
gbm.y = 7, family = "poisson",
tree.complexity = 3,
learning.rate = 0.001,
bag.fraction = 0.75)
macrichfinal_phytcladr$contributions
(macrichfinal_phytcladr$self.statistics$mean.null-macrichfinal_phytcladr$cv.statistics$deviance.mean)/macrichfinal_phytcladr$self.statistics$mean.null
macrichfinal_phytcladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17,3),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_phytcladr$contributions
(macrichfinal_phytcladr$self.statistics$mean.null-macrichfinal_phytcladr$cv.statistics$deviance.mean)/macrichfinal_phytcladr$self.statistics$mean.null
save.image("C:/Users/thd.AD/Dropbox/docs/CIRCE/BIOMAN/data/macrotree.RData")
load("C:/Users/thd.AD/Dropbox/docs/CIRCE/BIOMAN/data/phytotree.RData")
library(gbm)
prich.tc2.lr005 <- gbm.step(data=tmodel.data,
gbm.x = c(2,24,27:29,32:38),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.75)
(prich.tc2.lr005$self.statistics$mean.null-prich.tc2.lr005 $cv.statistics$deviance.mean)/prich.tc2.lr005 $self.statistics$mean.null
prich.tc2.lr005$contributions
gbm.plot(prich.tc2.lr005, n.plots=12, write.title = F)
names(tmodel.data)
#### tree for phytoplankton with only env data
prich.tc2.lr005 <- gbm.step(data=tmodel.data,
gbm.x = c(2,23,24,27:29,32:38),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.75)
(prich.tc2.lr005$self.statistics$mean.null-prich.tc2.lr005 $cv.statistics$deviance.mean)/prich.tc2.lr005 $self.statistics$mean.null
prich.tc2.lr005$contributions
prich.tc2.lr005 <- gbm.step(data=tmodel.data,
gbm.x = c(2,24,27:29,32:38),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.75)
(prich.tc2.lr005$self.statistics$mean.null-prich.tc2.lr005 $cv.statistics$deviance.mean)/prich.tc2.lr005 $self.statistics$mean.null
prich.tc2.lr005$contributions
prich.simp <- gbm.simplify(prich.tc2.lr005, n.drops =10)
prich.simp$pred.list[[7]]
envmodel <- gbm.step(data=tmodel.data,
gbm.x = c( 2, 28, 29, 36, 37),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
(envmodel$self.statistics$mean.null-envmodel$cv.statistics$deviance.mean)/envmodel$self.statistics$mean.null
envmodel$contributions
gbm.plot(envmodel, n.plots=5, write.title = F)
envmodel <- gbm.step(data=tmodel.data,
gbm.x = c( 2, 28, 29, 36, 37),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
## to calculate variance explained
(envmodel$self.statistics$mean.null-envmodel$cv.statistics$deviance.mean)/envmodel$self.statistics$mean.null
tmodel.data$TotZoopRich<-model.data$COPRICHGEN+model.data$ROTRICHMIT+model.data$CLADRICHMIT+model.data$CILRICHSPEC
names(tmodel.data)
envmodel <- gbm.step(data=tmodel.data,
gbm.x = c( 2, 28, 29, 36, 37),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
## to calculate variance explained
(envmodel$self.statistics$mean.null-envmodel$cv.statistics$deviance.mean)/envmodel$self.statistics$mean.null
envmodel$contributions
gbm.plot(envmodel, n.plots=5, write.title = F)
penv_zoop <- gbm.step(data=model.data,
gbm.x = c(2, 28, 29, 36, 37,74),
gbm.y = 2,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
penv_zoop <- gbm.step(data=model.data,
gbm.x = c(2, 28, 29, 36, 37,74),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
penv_zoop <- gbm.step(data=tmodel.data,
gbm.x = c(2, 28, 29, 36, 37,74),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
(penv_zoop$self.statistics$mean.null-penv_zoop$cv.statistics$deviance.mean)/penv_zoop$self.statistics$mean.null
penv_zoop$contributions
tmodel.data$TotZoopRich<-tmodel.data$COPRICHGEN+model.data$ROTRICHMIT+model.data$CLADRICHMIT+model.data$CILRICHSPEC
names(tmodel.data)
penv_zoop <- gbm.step(data=tmodel.data,
gbm.x = c(2, 28, 29, 36, 37,74),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
(penv_zoop$self.statistics$mean.null-penv_zoop$cv.statistics$deviance.mean)/penv_zoop$self.statistics$mean.null
penv_zoop$contributions
penv_zoop.simp <- gbm.simplify(penv_zoop, n.drops =5)
names(tmodel.data)
names(tmodel.data)
penv_mac <- gbm.step(data=model.data,
gbm.x = c(19,29,22,35,7),
gbm.y = 3,
family = "poisson",
tree.complexity =3,
learning.rate = 0.001,
bag.fraction = 0.7)
## to calculate variance explained
(penv_mac$self.statistics$mean.null-penv_mac$cv.statistics$deviance.mean)/penv_mac$self.statistics$mean.null
penv_mac$contributions
penv_mac <- gbm.step(data=tmodel.data,
gbm.x = c(19,29,22,35,7),
gbm.y = 3,
family = "poisson",
tree.complexity =3,
learning.rate = 0.001,
bag.fraction = 0.7)
## to calculate variance explained
(penv_mac$self.statistics$mean.null-penv_mac$cv.statistics$deviance.mean)/penv_mac$self.statistics$mean.null
penv_mac$contributions
penv_mac <- gbm.step(data=tmodel.data,
gbm.x = c(2, 28, 29, 36, 37,7),
gbm.y = 3,
family = "poisson",
tree.complexity =3,
learning.rate = 0.001,
bag.fraction = 0.7)
## to calculate variance explained
(penv_mac$self.statistics$mean.null-penv_mac$cv.statistics$deviance.mean)/penv_mac$self.statistics$mean.null
penv_mac$contributions
penv_mac.simp <- gbm.simplify(penv_mac, n.drops =5)
penv_fish <- gbm.step(data=tmodel.data,
gbm.x = c(2, 28, 29, 36, 37,19),
gbm.y = 3,
family = "poisson",
tree.complexity =3,
learning.rate = 0.001,
bag.fraction = 0.7)
(penv_fish$self.statistics$mean.null-penv_fish$cv.statistics$deviance.mean)/penv_fish$self.statistics$mean.null
penv_fish$contributions
penv_fish.simp <- gbm.simplify(penv_fish, n.drops =4)
save.image("C:/Users/thd.AD/Dropbox/docs/CIRCE/BIOMAN/data/phytotree.RData")
load("C:/Users/thd.AD/Dropbox/docs/CIRCE/BIOMAN/data/macrotree.RData")
macrichfinal_cladr.simp <- gbm.simplify(macrichfinal_cladr, n.drops =4)
macrichfinal_cladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_cladr.simp <- gbm.simplify(macrichfinal_cladr, n.drops =4)
macrichfinal_phytr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,3),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_phytr$contributions
(macrichfinal_phytr$self.statistics$mean.null-macrichfinal_phytr$cv.statistics$deviance.mean)/macrichfinal_phytr$self.statistics$mean.null
macrichfinal_phytr.simp <- gbm.simplify(macrichfinal_phytr, n.drops =4)
macrichfinal_phytr.simp <- gbm.simplify(macrichfinal_phytr, n.drops =4)
macrichfinal_copr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33, 37,15),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.7)
macrichfinal_copr$contributions
(macrichfinal_copr$self.statistics$mean.null-macrichfinal_copr$cv.statistics$deviance.mean)/macrichfinal_copr$self.statistics$mean.null
macrichfinal_phytcladr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33,37,17,3),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.75)
macrichfinal_phytcladr$contributions
(macrichfinal_phytcladr$self.statistics$mean.null-macrichfinal_phytcladr$cv.statistics$deviance.mean)/macrichfinal_phytcladr$self.statistics$mean.null
## no improvement
macrichfinal_phytcladr.simp <- gbm.simplify(macrichfinal_phytcladr, n.drops =4)
(macrichfinal_phytcladr$self.statistics$mean.null-macrichfinal_phytcladr$cv.statistics$deviance.mean)/macrichfinal_phytcladr$self.statistics$mean.null
macrichfinal_phytcladr$contributions
gbm.plot(macrichfinal_phytrcladr, n.plots=6, write.title = F)
gbm.plot(macrichfinal_phytcladr, n.plots=6, write.title = F)
macrichfinal_cilr.simp <- gbm.simplify(macrichfinal_cilr, n.drops =4)
macrichfinal_cilr.simp <- gbm.simplify(macrichfinal_cilr, n.drops =3)
macrichfinal_cilr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33, 37,18),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.7)
macrichfinal_cilr$contributions
(macrichfinal_cilr$self.statistics$mean.null-macrichfinal_cilr$cv.statistics$deviance.mean)/macrichfinal_cilr$self.statistics$mean.null
macrichfinal_rotr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33, 37,16),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.7)
macrichfinal_rotr$contributions
(macrichfinal_rotr$self.statistics$mean.null-macrichfinal_rotr$cv.statistics$deviance.mean)/macrichfinal_rotr$self.statistics$mean.null
macrichfinal_rotr.simp <- gbm.simplify(macrichfinal_rotr, n.drops =4)
macrichfinal_fishr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33, 37,19),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.7)
macrichfinal_fishr$contributions
(macrichfinal_fishr$self.statistics$mean.null-macrichfinal_fishr$cv.statistics$deviance.mean)/macrichfinal_fishr$self.statistics$mean.null
names(tmodel.data)
macrichfinal_fishr<- gbm.step(tmodel.data,
gbm.x = c(27, 28, 33, 37,19),
gbm.y = 7, family = "poisson",
tree.complexity = 2,
learning.rate = 0.005,
bag.fraction = 0.7)
macrichfinal_fishr$contributions
(macrichfinal_fishr$self.statistics$mean.null-macrichfinal_fishr$cv.statistics$deviance.mean)/macrichfinal_fishr$self.statistics$mean.null
gbm.plot(macrichfinal_fishr, n.plots=5, write.title = F)
macrichfinal_fishr.simp <- gbm.simplify(macrichfinal_fishr, n.drops =4)
gbm.plot(macrichfinal_phytr, n.plots=5, write.title = F)
gbm.plot(macrichfinal_copr, n.plots=5, write.title = F)
save.image("C:/Users/thd.AD/Dropbox/docs/CIRCE/BIOMAN/data/macrotree.RData")
load("C:/Users/thd.AD/Dropbox/docs/CIRCE/BIOMAN/data/phytotree.RData")
penv_zoop <- gbm.step(data=tmodel.data,
gbm.x = c(2, 28, 29, 36, 37,74),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
(penv_zoop$self.statistics$mean.null-penv_zoop$cv.statistics$deviance.mean)/penv_zoop$self.statistics$mean.null
penv_zoop$contributions
penv_zoop.simp <- gbm.simplify(penv_zoop, n.drops =5)
penv_zoop$contributions
gbm.plot(penv_zoop, n.plots=5, write.title = F)
gbm.plot(penv_zoop, n.plots=6, write.title = F)
penv_mac <- gbm.step(data=tmodel.data,
gbm.x = c(2, 28, 29, 36, 37,7),
gbm.y = 3,
family = "poisson",
tree.complexity =3,
learning.rate = 0.001,
bag.fraction = 0.7)
## to calculate variance explained
(penv_mac$self.statistics$mean.null-penv_mac$cv.statistics$deviance.mean)/penv_mac$self.statistics$mean.null
penv_mac$contributions
penv_mac.simp <- gbm.simplify(penv_mac, n.drops =5)
penv_zoop.simp <- gbm.simplify(penv_zoop, n.drops =5)
names(tmodel.data)
prich.tc2.lr005 <- gbm.step(data=tmodel.data,
gbm.x = c(2,24,25,26,27:29,32:38),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.75)
(prich.tc2.lr005$self.statistics$mean.null-prich.tc2.lr005 $cv.statistics$deviance.mean)/prich.tc2.lr005 $self.statistics$mean.null
prich.tc2.lr005$contributions
gbm.plot(prich.tc2.lr005, n.plots=12, write.title = F)
prich.simp <- gbm.simplify(prich.tc2.lr005, n.drops =10)
(prich.tc2.lr005$self.statistics$mean.null-prich.tc2.lr005 $cv.statistics$deviance.mean)/prich.tc2.lr005 $self.statistics$mean.null
prich.tc2.lr005 <- gbm.step(data=tmodel.data,
gbm.x = c(2,24,25,26,27:29,32:38),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.75)
(prich.tc2.lr005$self.statistics$mean.null-prich.tc2.lr005 $cv.statistics$deviance.mean)/prich.tc2.lr005 $self.statistics$mean.null
prich.tc2.lr005$contributions
gbm.plot(prich.tc2.lr005, n.plots=12, write.title = F)
prich.simp <- gbm.simplify(prich.tc2.lr005, n.drops =10)
prich.simp <- gbm.simplify(prich.tc2.lr005, n.drops =13)
prich.simp$pred.list[[7]]
envmodel <- gbm.step(data=tmodel.data,
gbm.x = c( 2,25, 28, 29,34, 36, 37),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
## to calculate variance explained
(envmodel$self.statistics$mean.null-envmodel$cv.statistics$deviance.mean)/envmodel$self.statistics$mean.null
envmodel$contributions
gbm.plot(envmodel, n.plots=6, write.title = F)
gbm.plot(envmodel, n.plots=7, write.title = F)
penv_zoop <- gbm.step(data=tmodel.data,
gbm.x = c(2,25, 28, 29,34, 36, 37,74),
gbm.y = 3,
family = "poisson",
tree.complexity = 2,
learning.rate = 0.001,
bag.fraction = 0.7)
(penv_zoop$self.statistics$mean.null-penv_zoop$cv.statistics$deviance.mean)/penv_zoop$self.statistics$mean.null
penv_zoop$contributions
gbm.plot(penv_zoop, n.plots=6, write.title = F)
gbm.plot(penv_zoop, n.plots=8, write.title = F)
penv_zoop.simp <- gbm.simplify(penv_zoop, n.drops =5)
save.image("C:/Users/thd.AD/Dropbox/docs/CIRCE/BIOMAN/data/phytotree.RData")
install.packages(c("ape", "BRugs", "class", "evaluate", "foreign", "formatR", "knitr", "lattice", "maptools", "markdown", "MASS", "mgcv", "nlme", "nnet", "R2jags", "rjags", "rpart", "sp", "spatial", "spdep"))
install.packages("PCNM")
install.packages("pcnm")
install.packages("C:/Users/thd.AD/Downloads/PCNM_2.1-2.zip", repos = NULL)
library("PCNM", lib.loc="C:/Program Files/R/R-3.0.0/library")
install.packages("C:/Users/thd.AD/Downloads/packfor_0.0-8.zip", repos = NULL)
install.packages("C:/Users/thd.AD/Downloads/spacemakeR_0.0-5.zip", repos = NULL)
data(mite)
library(vegan)
data(mite)
data(mite.xy)
ls()
str(mite.xy)
mite.xy
mite
?decostand
library("spacemakeR", lib.loc="C:/Program Files/R/R-3.0.0/library")
library("PCNM", lib.loc="C:/Program Files/R/R-3.0.0/library")
install.packages("AEM")
library("PCNM", lib.loc="C:/Program Files/R/R-3.0.0/library")
library("pcurve", lib.loc="C:/Program Files/R/R-3.0.0/library")
?pcnm
?pcnm
?rda
require(pcnm)
require(PCNM)
library(PCNM)
setwd("C:/Users/thd.AD/Dropbox/course/pracdata")
data.org1<-read.csv("org1.csv", row.names=1)
str(data.org1)
library(vegan)
data.org1.hel<-decostand(data.org1, "hellinger")
str(data.org1.hel)
data.env<-read.csv("tenv.csv", row.names=1)
str(data.env)
data.xy<-read.csv("xy.csv", row.names=1)
str(data.xy)
library(PCNM)
distance <- dist(data.xy, method="euclidean")
data.pcnm<-PCNM(distance)
library(PCNM)
library(vegan)
data.pcnm<-PCNM(distance)
data.pcnm<-pcnm(distance)
op <- par(mfrow=c(1,3))
ordisurf(mite.xy, pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
ordisurf(mite.xy, pcnm$vectors[,2], bubble = 4, main = "PCNM 2")
ordisurf(mite.xy, pcnm$vectors[,3], bubble = 4, main = "PCNM 3")
par(op)
op <- par(mfrow=c(1,3))
ordisurf(data.xy, pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
ordisurf(data.xy, pcnm$vectors[,2], bubble = 4, main = "PCNM 2")
ordisurf(data.xy, pcnm$vectors[,3], bubble = 4, main = "PCNM 3")
par(op)
ordisurf(data.xy, pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
pcnm$vectors[,1]
op <- par(mfrow=c(1,3))
ordisurf(data.xy, data.pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
ordisurf(data.xy, data.pcnm$vectors[,2], bubble = 4, main = "PCNM 2")
ordisurf(data.xy, data.pcnm$vectors[,3], bubble = 4, main = "PCNM 3")
par(op)
library(PCNM)
library(packfor)
distance <- dist(data.xy, method="euclidean")
data.pcnm<-pcnm(distance)
data.PCNM<-pcnm(distance)
data.pcnm<-PCNM(distance)
??boot
library("boot", lib.loc="C:/Program Files/R/R-3.0.1/library")
data.pcnm<-PCNM(distance)
op <- par(mfrow=c(1,3))
ordisurf(data.xy, data.pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
ordisurf(data.xy, data.pcnm$vectors[,2], bubble = 4, main = "PCNM 2")
ordisurf(data.xy, data.pcnm$vectors[,3], bubble = 4, main = "PCNM 3")
par(op)
data.xy<-read.csv("xy.csv", row.names=1)
str(data.xy)
library(PCNM)
## now we create a distance matrix for between site differences
distance <- dist(data.xy, method="euclidean")
data.pcnm<-pcnm(distance)
op <- par(mfrow=c(1,3))
ordisurf(data.xy, data.pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
ordisurf(data.xy, data.pcnm$vectors[,2], bubble = 4, main = "PCNM 2")
ordisurf(data.xy, data.pcnm$vectors[,3], bubble = 4, main = "PCNM 3")
par(op)
data.xy<-read.csv("xy.csv", row.names=1)
str(data.xy)
library(PCNM)
distance <- dist(data.xy, method="euclidean")
data.pcnm<-pcnm(distance)
op <- par(mfrow=c(1,3))
ordisurf(data.xy, data.pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
ordisurf(data.xy, data.pcnm$vectors[,2], bubble = 4, main = "PCNM 2")
ordisurf(data.xy, data.pcnm$vectors[,3], bubble = 4, main = "PCNM 3")
par(op)
setwd("C:/Users/thd.AD/Dropbox/course/pracdata")
data.xy<-read.csv("xy.csv", row.names=1)
str(data.xy)
library(PCNM)
distance <- dist(data.xy, method="euclidean")
data.pcnm<-pcnm(distance)
op <- par(mfrow=c(1,3))
ordisurf(data.xy, data.pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
ordisurf(data.xy, data.pcnm$vectors[,2], bubble = 4, main = "PCNM 2")
ordisurf(data.xy, data.pcnm$vectors[,3], bubble = 4, main = "PCNM 3")
par(op)
op <- par(mfrow=c(1,3))
ordisurf(data.xy, data.pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
ordisurf(data.xy, data.pcnm$vectors[,2], bubble = 4, main = "PCNM 2")
ordisurf(data.xy, data.pcnm$vectors[,3], bubble = 4, main = "PCNM 3")
par(op)
data.xy<-read.csv("xy.csv", row.names=1)
str(data.xy)
distance <- dist(data.xy, method="euclidean")
data.pcnm<-pcnm(distance)
op <- par(mfrow=c(1,3))
ordisurf(data.xy, data.pcnm$vectors[,1], bubble = 4, main = "PCNM 1")
ordisurf(data.xy, data.pcnm$vectors[,2], bubble = 4, main = "PCNM 2")
ordisurf(data.xy, data.pcnm$vectors[,3], bubble = 4, main = "PCNM 3")
par(op)
?ordisurf
