search for: seagrass

Hi, Assuming that your dataset is similar to the one below: set.seed(25) dat1<- data.frame(Algae.Mass=sample(40:50,10,replace=TRUE),Seagrass.Mass=sample(30:70,10,replace=TRUE),Terrestrial.Mass=sample(80:100,10,replace=TRUE),Other.Mass=sample(40:60,10,replace=TRUE),Site.X.Treatment=rep(c("ALA1A","ALA1U"),each=5),stringsAsFactors=FALSE) library(reshape2) dat2<-melt(dat1,id.var="Site.X.Treatment") sapply(s...

Is there an optimal / minimum sample size for attempting to construct a classification tree using /rpart/? I have 27 seagrass disturbance sites (boat groundings) that have been monitored for a number of years. The monitoring protocol for each site is identical. From the monitoring data, I am able to determine the level of recovery that each site has experienced. Recovery is our categorical dependent variable with v...

Hi, I am currently trying to do a GLMM on a dataset with percent cover of seagrass (dep. var) and a suite of explanatory variables including algal (AC) and epiphyte cover (EC), rainfall, temperature and sunshine hours. M2=glmer(SG~AC+EC+TP+SS+RF+(1|Location/fSi/fTr), family=binomial,data=data,nAGQ=1) As the dependent variable is percent cover, I used a binomial error structure....

...tors,margin=0.08,cp=0,xv="1se",xval=nrow(abundance),xvmult=100,which=4) where abundance is 4th root transformed fish abundance (103 species x 168 samples), and factors is the relief (high, medium, low profile, sand inundated reef, flat), benthos (coral, sessile inverts, kelp, macroalgae, seagrass, sand), depth (continuous in meters), latitude, and longitude of each sample. To try and incorporate spatial autocorrelation (as a covariate) into this I have been trying the command - ABUNDTMRT <- mvpart(abundance ~ environ + spatial, data.frame,margin=0.08,cp=0,xv="1se",xval=nrow(a...

I'm running Debian stable (etch) on a server, and have the following Xapian packages installed. root@seagrass:/etc/php5/apache# dpkg -l | grep xap ii libxapian-dev 0.9.9-1 Development files for Xapian search engine l ii libxapian13 0.9.9-1 Search engine library ii php4-xapian 0.9.9-1.1...

...block in Bayesian analysis is model selection. Chapter 14 provides an easy-to-understand overview of various Bayesian model selection tools. Chapter 15 contains an example of Bayesian model selection using butterfly data. In Chapter 16 we discuss methods for the analysis of proportional data (seagrass coverage time series) with a large number of zeros. We use a zero-altered beta model with nested random effects. Finally, in Chapters 17 and 18 we discuss various topics, including multivariate GLMMs and generalised Poisson models (these can be used for underdispersion). We also discuss zero-in...

Hi, I'm trying to customize a window with 2 graphs. I'm able to do the first one with something like this general example par(mfrow=c(1,2),cex.axis=0.85,cex.lab=0.80,mai=c(1.3,1,0.5,0),las=3) bplot<-barplot(bar.values,names.arg=cf.names,width=0.5,ylab="% Area held") abline(h=0.3,lty=3,col="red") abline(h=0.1,lty=3,col="blue")