synthetic(x, y, z, delta, plot = FALSE, percen, upmargin = 0.1, widths = c(1, 4), steps = 0.05)
dispersion.all
.
bcrossv.all
and akaike.all
could be used as z
.
predict.loess
for details.
par
function, mai
parameter.
layout
parameter.
plot=TRUE
, this value is the average pollen percentage used from each sample in the construction of the synthetic assemblages.plot=TRUE
, a figure containing the synthetic assemblages is produced. Given differences in pollen representativity among species, individual taxa are standardized to facilitate illustration.
avg.info
returns the proportion of pollen data that was used from each sample. If no taxa are excluded from the pollen sum, such proportion will coincide with the sum of the percentages of the taxa included. In this case, the argument percent
can be filled by repeating 100 as many times as number of of modern samples (percent = rep(100, nrow(y))
). When one or more taxa are excluded from the pollen sum, samples might have total percentages that are larger than 100. Therefore, the percent
has to be calculated by adding all the percentages in each sample (percent = apply (Y, 1, sum)
, where Y
is the original percentage matrix including all taxa).
par
and layout
for details on graphic parameters.
data(modernq)
# Calculate percentages
perq<-percenta(modernq,first=2,last=39)[,2:55]
# filter data set to include only samples with at least 0.5
# percent in 20 percent of the samples
perq1<-filter.p(perq,presen=0.5,persist=0.2)$filtered
# calculate alpha and degree for each taxon through AIC
a.d<-akaike.all(modernq[,1],perq1)
# select taxa that have acceptable dispersion and normally
# distributed residuals in percentages and AIC matrices
perq2<-perq1[,-c(3:5,8,17,19)]
a.d1<-a.d[-c(3:5,8,17,19),]
#build synthetic assemblages
syas.q<-synthetic(modernq[,1],perq2,a.d1,delta=25,plot=TRUE,
percen=rep(100,53))
# predic percentages of Cyperacea for an elevation sequence from
# 100 to 550 in 25-m increments
predict(syas.q[[1]]$Cyperaceae,seq(100,550,25))
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