triangleImage: Comparison of the Habitat Use by the Roe Deer Between two...
In ClementCalenge/roedeer3sites: Habitat Selection by the Roe Deer in three French Study Sites
Description
Usage
Arguments
Value
Author(s)
Examples
Description
The functions presented on this help page have been used to compare
the habitat use between two sites (figure 5 of the paper). The
function triangleImage can be used to display an image in the
ecological triangle. The function triangleGrid can be used to
generate such an image. We describe in the Examples section how we
used these two functions to generate the figure 5 of the paper.
Usage
1
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8
Arguments
pred
a vector of N values to be plotted
gr
a N x 2 matrix containing the pairs of Cartesian
coordinates on the ecological triangle, corresponding to the N
values stored in pred.
names
The names of the three habitat types.
cuts
a numeric vector containing the B+1 breaks used to define the colors.
col
a character vector containing the names of the B colors
corresponding to the B classes defined by cuts.
listlim
Optionally, a list with two elements named xlim and
ylim describing the x and y limits of the image to be plotted
on the triangle.
lowerleft
A vector of length 3 describing the habitat proportions
corresponding to the lower left corner of the triangle.
lowerright
A vector of length 3 describing the habitat proportions
corresponding to the lower right corner of the triangle.
top
A vector of length 3 describing the habitat proportions
corresponding to the top corner of the triangle.
ngrid
An integer value controling the resolution of the grid generated on
the ecological triangle.
Value
The function triangleGrid returns a list with two elements
named triangle and proportions containing respectively a
matrix containing the pairs of Cartesian coordinates of the grid in
the triangle and a matrix containing the corresponding vectors of
three habitat proportions.
The function triangleImage returns invisibly an object of class
spatialPixelsDataFrame containing the gridded values within the
limits specified by listlim, lowerleft,
lowerright and top.
Author(s)
William Gaudry, Clement Calenge, Sonia Said, Jean-Michel Gaillard
Examples
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## In these examples, we describe how we built the figure 5.
######################################################
##
## 1. Load the data and fit the model
## We load the data used for the fit:
data(HS3sites)
## We remove the information concerning the meadows
## (negligible, see the paper)
HS3sites$locs$meadows <- NULL
HS3sites$hr$meadows <- NULL
## Calculates the total number of relocations
HS3sites$N <- apply(HS3sites$locs,1,sum)
## stores the number of animals
HS3sites$J <- nrow(HS3sites$locs)
## keeps the home-range information in another object
availhr <- HS3sites$hr
## For a better mixing, we scale the covariates
HS3sites$hr <- scale(HS3sites$hr)
mo <- attr(HS3sites$hr,"scaled:center")
sc <- attr(HS3sites$hr,"scaled:scale")
## We have described in the example section of the help
## page of the dataset coefficientsModel2 how to fit the model.
## We just load the dataset:
data(coefficientsModel2)
## Not run:
####################################################
##
## 2. We generate a grid of values in the ecological triangle
tr <- triangleGrid()
pp <- tr$proportions
gr <- tr$triangle
####################################################
##
## 3. We predict, for each possible habitat availability stored in tr
## and gr, the posterior distribution of the log-ratios between
## habitat use in a site and in another
## Remember that we centered and scaled the availability proportions
## to improve mixing of the MCMC. We center and scale the proportions
## for prediction. Also, pp is designed for representation purposes
## (with scrubs as the horisontal axis), whereas d is for calculation:
d <- pp[,c(2,3,1)]
d <- t(apply(d,1,function(x) (x-mo)/sc))
## We bind the 3 chains
coef2 <- do.call(rbind, coefficientsModel2)
## We work with 1000 random vectors of parameters sampled from the
## posterior.
set.seed(98)
sampledvectors <- sample(1:nrow(cor2), 1000)
## For each parameter vector, we predict the mean of log-ratios between
## pairs of sites for all possible available proportions.
## WARNING!!!! Very slow loop!
lpr <- lapply(1:1000, function(samp) {
## progress bar
cat(round(100*samp/1000),"\r")
## Prediction of the use in site 1
i <- sampledvectors[samp]
site <- rep(1,nrow(d))
logp1 <- coef2[i,paste("a0f[",site,"]",sep="")] +
coef2[i,paste("aff[",site,"]",sep="")]*d[,1] +
coef2[i,paste("afp[",site,"]",sep="")]*d[,2]
logp2 <- coef2[i,paste("a0p[",site,"]",sep="")] +
coef2[i,paste("apf[",site,"]",sep="")]*d[,1] +
coef2[i,paste("app[",site,"]",sep="")]*d[,2]
p1 <- exp(logp1)/(1+exp(logp1)+exp(logp2))
p2 <- exp(logp2)/(1+exp(logp1)+exp(logp2))
p3 <- 1/(1+exp(logp1)+exp(logp2))
p11 <- p1
p21 <- p2
p31 <- p3
## Prediction of the use in site 2
site <- rep(2,nrow(d))
logp1 <- coef2[i,paste("a0f[",site,"]",sep="")] +
coef2[i,paste("aff[",site,"]",sep="")]*d[,1] +
coef2[i,paste("afp[",site,"]",sep="")]*d[,2]
logp2 <- coef2[i,paste("a0p[",site,"]",sep="")] +
coef2[i,paste("apf[",site,"]",sep="")]*d[,1] +
coef2[i,paste("app[",site,"]",sep="")]*d[,2]
p1 <- exp(logp1)/(1+exp(logp1)+exp(logp2))
p2 <- exp(logp2)/(1+exp(logp1)+exp(logp2))
p3 <- 1/(1+exp(logp1)+exp(logp2))
p12 <- p1
p22 <- p2
p32 <- p3
## Prediction of the use in site 3
site <- rep(3,nrow(d))
logp1 <- coef2[i,paste("a0f[",site,"]",sep="")] +
coef2[i,paste("aff[",site,"]",sep="")]*d[,1] +
coef2[i,paste("afp[",site,"]",sep="")]*d[,2]
logp2 <- coef2[i,paste("a0p[",site,"]",sep="")] +
coef2[i,paste("apf[",site,"]",sep="")]*d[,1] +
coef2[i,paste("app[",site,"]",sep="")]*d[,2]
p1 <- exp(logp1)/(1+exp(logp1)+exp(logp2))
p2 <- exp(logp2)/(1+exp(logp1)+exp(logp2))
p3 <- 1/(1+exp(logp1)+exp(logp2))
p13 <- p1
p23 <- p2
p33 <- p3
## return the matrix of the predicted mean log-ratios.
return(cbind(log(p11/p12), log(p21/p22), log(p31/p32),
log(p12/p13), log(p22/p23), log(p32/p33),
log(p13/p11), log(p23/p21), log(p33/p31)))
})
####################################################
##
## 4. We predict, for each possible habitat availability stored in tr
## and gr, the probability that the log-ratios between
## habitat use in a site and in another is greater than 0
gg <- do.call("cbind", lapply(1:ncol(lpr[[1]]), function(i) {
aa <- do.call("cbind",lapply(1:length(lpr), function(k) {
lpr[[k]][,i]
}))
apply(aa,1,function(x) mean(x>0))
}))
####################################################
##
## 5. We plot the results using triangleImage
## Several elements required for the plot
namesa <- c("Chizé","LPP","TF")
nameshab <- c("Scrubs","pole","CWS")
labels1 <- apply(expand.grid(nameshab,
namesa)[,2:1],1,
function(x) paste(x, collapse="-"))
## The range of available conditions available in the two sites of a
## pair
availhr <- availhr[,c(3,1,2)] ## we reordered the columns, as we found
## clearer to present the scrubs as the horizontal side
di <- availhr[HS3sites$site==1,]
predd <- t(apply(di,1,ptoxy))
poly1 <- list(xlim=range(predd[,1]), ylim=range(predd[,2]))
di <- availhr[HS3sites$site==2,]
predd <- t(apply(di,1,ptoxy))
poly2 <- list(xlim=range(predd[,1]), ylim=range(predd[,2]))
poly2$xlim <- poly1$xlim
di <- availhr[HS3sites$site==3,]
predd <- t(apply(di,1,ptoxy))
poly3 <- list(xlim=range(predd[,1]), ylim=range(predd[,2]))
poly3$xlim <- poly1$xlim
## preparation of the image layout
mat <- rbind(c(1,2,2,2,2,3,3,3,3,4,4,4,4),
c(5,6,6,6,6,7,7,7,7,8,8,8,8),
c(5,6,6,6,6,7,7,7,7,8,8,8,8),
c(5,6,6,6,6,7,7,7,7,8,8,8,8),
c(5,6,6,6,6,7,7,7,7,8,8,8,8),
c(9,10,10,10,10,11,11,11,11,12,12,12,12),
c(9,10,10,10,10,11,11,11,11,12,12,12,12),
c(9,10,10,10,10,11,11,11,11,12,12,12,12),
c(9,10,10,10,10,11,11,11,11,12,12,12,12),
c(13,14,14,14,14,15,15,15,15,16,16,16,16),
c(13,14,14,14,14,15,15,15,15,16,16,16,16),
c(13,14,14,14,14,15,15,15,15,16,16,16,16),
c(13,14,14,14,14,15,15,15,15,16,16,16,16))
lay <- layout(mat)
## Top row of graphs = titles
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"Scrubs", cex=1.5, font=2)
box()
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"Pole Stage", cex=1.5, font=2)
box()
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"CWS", cex=1.5, font=2)
box()
## Second row of graphs
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"Chizé/LPP", cex=1.5, font=2, srt=90)
box()
triangleImage(gg[,1], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,2], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,3], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
## Third row of graphs
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"LPP/TF", cex=1.5, font=2, srt=90)
box()
triangleImage(gg[,4], gr, listlim=poly3, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,5], gr, listlim=poly3, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,6], gr, listlim=poly3, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
## Last row of graphs
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"TF/Chizé", cex=1.5, font=2, srt=90)
box()
triangleImage(gg[,7], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,8], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,9], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
## End(Not run)
ClementCalenge/roedeer3sites documentation built on May 16, 2019, 6:58 p.m.
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Description Usage Arguments Value Author(s) Examples
Description
The functions presented on this help page have been used to compare
the habitat use between two sites (figure 5 of the paper). The
function triangleImage can be used to display an image in the
ecological triangle. The function triangleGrid can be used to
generate such an image. We describe in the Examples section how we
used these two functions to generate the figure 5 of the paper.
Usage
1 2 3 4 5 6 7 8 |
Arguments
pred |
a vector of N values to be plotted |
gr |
a N x 2 matrix containing the pairs of Cartesian
coordinates on the ecological triangle, corresponding to the N
values stored in |
names |
The names of the three habitat types. |
cuts |
a numeric vector containing the B+1 breaks used to define the colors. |
col |
a character vector containing the names of the B colors
corresponding to the B classes defined by |
listlim |
Optionally, a list with two elements named |
lowerleft |
A vector of length 3 describing the habitat proportions corresponding to the lower left corner of the triangle. |
lowerright |
A vector of length 3 describing the habitat proportions corresponding to the lower right corner of the triangle. |
top |
A vector of length 3 describing the habitat proportions corresponding to the top corner of the triangle. |
ngrid |
An integer value controling the resolution of the grid generated on the ecological triangle. |
Value
The function triangleGrid returns a list with two elements
named triangle and proportions containing respectively a
matrix containing the pairs of Cartesian coordinates of the grid in
the triangle and a matrix containing the corresponding vectors of
three habitat proportions.
The function triangleImage returns invisibly an object of class
spatialPixelsDataFrame containing the gridded values within the
limits specified by listlim, lowerleft,
lowerright and top.
Author(s)
William Gaudry, Clement Calenge, Sonia Said, Jean-Michel Gaillard
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | ## In these examples, we describe how we built the figure 5.
######################################################
##
## 1. Load the data and fit the model
## We load the data used for the fit:
data(HS3sites)
## We remove the information concerning the meadows
## (negligible, see the paper)
HS3sites$locs$meadows <- NULL
HS3sites$hr$meadows <- NULL
## Calculates the total number of relocations
HS3sites$N <- apply(HS3sites$locs,1,sum)
## stores the number of animals
HS3sites$J <- nrow(HS3sites$locs)
## keeps the home-range information in another object
availhr <- HS3sites$hr
## For a better mixing, we scale the covariates
HS3sites$hr <- scale(HS3sites$hr)
mo <- attr(HS3sites$hr,"scaled:center")
sc <- attr(HS3sites$hr,"scaled:scale")
## We have described in the example section of the help
## page of the dataset coefficientsModel2 how to fit the model.
## We just load the dataset:
data(coefficientsModel2)
## Not run:
####################################################
##
## 2. We generate a grid of values in the ecological triangle
tr <- triangleGrid()
pp <- tr$proportions
gr <- tr$triangle
####################################################
##
## 3. We predict, for each possible habitat availability stored in tr
## and gr, the posterior distribution of the log-ratios between
## habitat use in a site and in another
## Remember that we centered and scaled the availability proportions
## to improve mixing of the MCMC. We center and scale the proportions
## for prediction. Also, pp is designed for representation purposes
## (with scrubs as the horisontal axis), whereas d is for calculation:
d <- pp[,c(2,3,1)]
d <- t(apply(d,1,function(x) (x-mo)/sc))
## We bind the 3 chains
coef2 <- do.call(rbind, coefficientsModel2)
## We work with 1000 random vectors of parameters sampled from the
## posterior.
set.seed(98)
sampledvectors <- sample(1:nrow(cor2), 1000)
## For each parameter vector, we predict the mean of log-ratios between
## pairs of sites for all possible available proportions.
## WARNING!!!! Very slow loop!
lpr <- lapply(1:1000, function(samp) {
## progress bar
cat(round(100*samp/1000),"\r")
## Prediction of the use in site 1
i <- sampledvectors[samp]
site <- rep(1,nrow(d))
logp1 <- coef2[i,paste("a0f[",site,"]",sep="")] +
coef2[i,paste("aff[",site,"]",sep="")]*d[,1] +
coef2[i,paste("afp[",site,"]",sep="")]*d[,2]
logp2 <- coef2[i,paste("a0p[",site,"]",sep="")] +
coef2[i,paste("apf[",site,"]",sep="")]*d[,1] +
coef2[i,paste("app[",site,"]",sep="")]*d[,2]
p1 <- exp(logp1)/(1+exp(logp1)+exp(logp2))
p2 <- exp(logp2)/(1+exp(logp1)+exp(logp2))
p3 <- 1/(1+exp(logp1)+exp(logp2))
p11 <- p1
p21 <- p2
p31 <- p3
## Prediction of the use in site 2
site <- rep(2,nrow(d))
logp1 <- coef2[i,paste("a0f[",site,"]",sep="")] +
coef2[i,paste("aff[",site,"]",sep="")]*d[,1] +
coef2[i,paste("afp[",site,"]",sep="")]*d[,2]
logp2 <- coef2[i,paste("a0p[",site,"]",sep="")] +
coef2[i,paste("apf[",site,"]",sep="")]*d[,1] +
coef2[i,paste("app[",site,"]",sep="")]*d[,2]
p1 <- exp(logp1)/(1+exp(logp1)+exp(logp2))
p2 <- exp(logp2)/(1+exp(logp1)+exp(logp2))
p3 <- 1/(1+exp(logp1)+exp(logp2))
p12 <- p1
p22 <- p2
p32 <- p3
## Prediction of the use in site 3
site <- rep(3,nrow(d))
logp1 <- coef2[i,paste("a0f[",site,"]",sep="")] +
coef2[i,paste("aff[",site,"]",sep="")]*d[,1] +
coef2[i,paste("afp[",site,"]",sep="")]*d[,2]
logp2 <- coef2[i,paste("a0p[",site,"]",sep="")] +
coef2[i,paste("apf[",site,"]",sep="")]*d[,1] +
coef2[i,paste("app[",site,"]",sep="")]*d[,2]
p1 <- exp(logp1)/(1+exp(logp1)+exp(logp2))
p2 <- exp(logp2)/(1+exp(logp1)+exp(logp2))
p3 <- 1/(1+exp(logp1)+exp(logp2))
p13 <- p1
p23 <- p2
p33 <- p3
## return the matrix of the predicted mean log-ratios.
return(cbind(log(p11/p12), log(p21/p22), log(p31/p32),
log(p12/p13), log(p22/p23), log(p32/p33),
log(p13/p11), log(p23/p21), log(p33/p31)))
})
####################################################
##
## 4. We predict, for each possible habitat availability stored in tr
## and gr, the probability that the log-ratios between
## habitat use in a site and in another is greater than 0
gg <- do.call("cbind", lapply(1:ncol(lpr[[1]]), function(i) {
aa <- do.call("cbind",lapply(1:length(lpr), function(k) {
lpr[[k]][,i]
}))
apply(aa,1,function(x) mean(x>0))
}))
####################################################
##
## 5. We plot the results using triangleImage
## Several elements required for the plot
namesa <- c("Chizé","LPP","TF")
nameshab <- c("Scrubs","pole","CWS")
labels1 <- apply(expand.grid(nameshab,
namesa)[,2:1],1,
function(x) paste(x, collapse="-"))
## The range of available conditions available in the two sites of a
## pair
availhr <- availhr[,c(3,1,2)] ## we reordered the columns, as we found
## clearer to present the scrubs as the horizontal side
di <- availhr[HS3sites$site==1,]
predd <- t(apply(di,1,ptoxy))
poly1 <- list(xlim=range(predd[,1]), ylim=range(predd[,2]))
di <- availhr[HS3sites$site==2,]
predd <- t(apply(di,1,ptoxy))
poly2 <- list(xlim=range(predd[,1]), ylim=range(predd[,2]))
poly2$xlim <- poly1$xlim
di <- availhr[HS3sites$site==3,]
predd <- t(apply(di,1,ptoxy))
poly3 <- list(xlim=range(predd[,1]), ylim=range(predd[,2]))
poly3$xlim <- poly1$xlim
## preparation of the image layout
mat <- rbind(c(1,2,2,2,2,3,3,3,3,4,4,4,4),
c(5,6,6,6,6,7,7,7,7,8,8,8,8),
c(5,6,6,6,6,7,7,7,7,8,8,8,8),
c(5,6,6,6,6,7,7,7,7,8,8,8,8),
c(5,6,6,6,6,7,7,7,7,8,8,8,8),
c(9,10,10,10,10,11,11,11,11,12,12,12,12),
c(9,10,10,10,10,11,11,11,11,12,12,12,12),
c(9,10,10,10,10,11,11,11,11,12,12,12,12),
c(9,10,10,10,10,11,11,11,11,12,12,12,12),
c(13,14,14,14,14,15,15,15,15,16,16,16,16),
c(13,14,14,14,14,15,15,15,15,16,16,16,16),
c(13,14,14,14,14,15,15,15,15,16,16,16,16),
c(13,14,14,14,14,15,15,15,15,16,16,16,16))
lay <- layout(mat)
## Top row of graphs = titles
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"Scrubs", cex=1.5, font=2)
box()
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"Pole Stage", cex=1.5, font=2)
box()
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"CWS", cex=1.5, font=2)
box()
## Second row of graphs
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"Chizé/LPP", cex=1.5, font=2, srt=90)
box()
triangleImage(gg[,1], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,2], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,3], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
## Third row of graphs
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"LPP/TF", cex=1.5, font=2, srt=90)
box()
triangleImage(gg[,4], gr, listlim=poly3, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,5], gr, listlim=poly3, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,6], gr, listlim=poly3, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
## Last row of graphs
par(mar = c(0.1,0.1,0.1,0.1))
plot(0,0, asp=1, ty="n", xlim = c(0,1), ylim = c(0,1), axes=FALSE)
text(0.5,0.5,"TF/Chizé", cex=1.5, font=2, srt=90)
box()
triangleImage(gg[,7], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,8], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
triangleImage(gg[,9], gr, listlim=poly1, names=c("CWS","scrubs","pole"),
lowerleft=c(1,0,0), lowerright=c(0.3,0.7,0), top=c(0.3,0,0.7))
## End(Not run)
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