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#
# Simulate data
#
a <- 2
x <- seq(0, 1, length.out = 100)
n <- length(x)
sigma <- 0.5
set.seed(42)
lny <- rnorm(n, mean = a*x, sd = sigma)
y <- exp(lny)

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#Visualise priors for a variance component selected using the method of Fong et al. 2010 (see page 400 of that paper)
R <- log(10) #+-range. Use log for GLMs that have log-link functions(e.g. poisson) or no logs for a gaussian model with identity link
d <- 1 #Degrees of freedom for student t
quant <-0.95 #quantile you want random effect estimates to vary between

#Parameters of Gamma
a1 <- d/2 
qtemp <- 1-(1-quant)/2
a2 <- (d/2) * (R^2)/(qt(qtemp, df = 1)^2)

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#Semivariance function for over water distance

 semivariance <- function(xdists, yresp, ncats = NA){
	#Function to calculate semivariance using a distance matrix
	#
	#Where:
	#xdists is the distance matrix
	#yresp is the response and rows of yresp correspond to sites in rows and columns of dists
	#ncats is the resolution. Defaults to sturges rule for histogram classes (as per Legendre book)
		

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library(INLA)
nprec <- 100000
prec <- seq(0.05, 1000, length.out = nprec) #precisions
sd <- 1/sqrt(prec)
U <- 1
alpha <- 0.025
lambda <- -log(alpha)/U #rate for exponential on sd
pr_prec <- inla.pc.dprec(prec, U, alpha) #Density for the precision. 
pr_sd <- dexp(sd, rate = lambda) #Density for sd
#nb see inla.pc.dprec for how to get density for the precision. 

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#Written by Chris Brown at Griffith Uni

#Tips for setting up R packages on windows 10

I recommend installing packages to your local drive, not the shared network drive. Though R will default to the network drive (which is "\\\\staff\..."), so some knowledge is required to overcome this.
You could also ask IT to bind a letter to your network drive. I've read that works (see here: https://github.com/r-lib/devtools/issues/353)

Here's the steps I followed to install packages:
Create a shortcut to R on desktop.
Right click it

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#Takes as an input a stars raster layer
#CJ Brown 2020-05-18

st_as_raster <- function(rstars){
  rext <- st_bbox(rstars)
  raster(t(rstars[[1]]), xmn = rext[1], xmx = rext[3],
                   ymn = rext[2], ymx=rext[4],
                   crs = st_crs(rstars)$proj4string)
}

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# How to ggplot2 efficiently
# CJ Brown 2020-10-27


library(ggplot2)

#
# Make some data 
#
n <- 20 #Sample size per group

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# Why you should plot data before doing statistical tests 
# CJ Brown 2020-11-06
#More at www.conservationhackers.org

library(ggplot2)

#
# Make some data 
#
n <- 50 #Sample size per group

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#Parameterize gamma distribution with mode and SD

#Choose summary stats for the gamma
xmode <- 1 #mode of distribution
xsd <- 0.5 # SD 

#Calculate gamma parameters
beta <- (xmode + sqrt(xmode^2 + 4*xsd^2 ) ) / ( 2 * xsd^2 )
alpha <- 1 + xmode * beta

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#Function that converts dataframe of cluster identities
# to distance matrix representation of clusters (where rows/cols are samples/IDs and
# 0 indicates that pair are not in same cluster, 1 indicates they are in same cluster
as_dist_mat <- function(dtemp){
  # dtemp is dataframe with two columns:
 #ID is a unique cell ID
 #Cluster is a variable giving cluster a cell belongs to 
 #It should be arranged so we are certain
  #cells are in ascending order