rBatt

#Test case for calculating metabolism
library(LakeMetabolizer)

#set up test example where DO starts and ends at same level, is at saturation (= no flux)
DOobs = c(3, 2, 3, 4, 3, 2)
DOsat = c(3, 2, 3, 4, 3, 2)
kGas = c(.2, .2, .2, .2, .2, .2)
zMix = c(1, 1, 1, 1, 1, 1)
irr = c(0, 1, 1, 0, 0, 0)

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x = arima.sim(model=list(ar=0.9), n=200)

acf(x)

pacf(x

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#' Scatter plot with certain groups of points getting own regression and fitted line
#' 
#' @param Data a data.table
#' @param x,y,lineBy character, name of column in \code{Data}
#' @param ptCol,lineCol character or quoted expression (call) for point or line color, repsectively
#' @param ... arguments to be passed to \code{\link{plot}} and \code{link{lines}}
#' 
#' @examples
#' dt <- data.table( 
#' 	x=x<-rnorm(9, sd=2), # why <- is better than =

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# ==============================
# = Simulating Spreading Range =
# ==============================
# ---- setup ----
nspp <- 25
nsite <- 25
nr <- nsite
nc <- nspp
n_iter <- 25

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nspp <- 10
nsites <- 10

Y_1sppPer <- diag(x=1, nrow=nspp, ncol=nsites)
Y_allSpp1Site <- rbind(rep(1,nspp), matrix(rep(0,nspp*nsites-nspp),nrow=nsites-1))

# euclidean distance
euc <- function(x){
	sum(scale(x,scale=FALSE)^2)/(nrow(x)-1)
}

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library(reshape2)
hasDT <- library(data.table, logical.return=TRUE) # load package, if not installed return FALSE

# ==================
# = Make Fake Data =
# ==================
# ---- Options affecting size of 'big' ----
nScaff <- 50 # number of scaffolds (or 'pages')
nIndex <- 100 # number of loci or indices (or 'words')

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la <- c(Okanagan=348, Seneca=173, Cayuga=172, Ballston=1.10884, Reindeer=5650, Somyn=NA, Thetis=8.336524, Manitoba=4624, Cold=373, Winnipegosis=5370, Crescent=18.401866, Simcoe=722, Muskrat=12.02, Superior=127700, Ontario=7340, Timiskaming=295, Memphremagog=110, Tahoe=490, Tarpon=10, Fulks=NA, Flathead=510, Hopatcong=10, Champlain=1331, Willoughby=1687, Erie=25667, Bear=280, Raystown=33.6, GreatSalt=4400, Arenal=85, NahuelHuapi=530, Dilolo=12.35, MamfePool=NA, DiloloSwamps=NA, Edward=2325, MaikaMarshes=NA, Victoria=68800, Makele=NA, Tangankyika=32900, Bangweulu=15100, KafueFlats=NA, Mweru=5120, IshibaNgandu=NA, Kariba=5580, Bassenthwaite=5.1, Ness=56, Morar=26.7, Maree=28.6, Lagarfljot=53, LoughLeane=19, Muckross=2.7, UpperLake=1.7, Como=146, Seljordsvatnet=16.7, Storsjon=464, Gryttjen=NA, Brosno=7.55, Labynkyr=61, LakeVan=3755, Kanas=45.73, TianChiHeaven=9.82, Ikeda=11, Kussharo=79.3, Pinatubo=1.83, Modewarre=NA, Utopia=14) # area in km^2; Dilolo I measured on google maps and I excluded the NE corner that lo

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# dev.new(width=3.5, height=3.5)
png("~/Desktop/test2.png", width=3.5, height=3.5, res=150, units='in')

par(mfrow=c(2,1), mar=c(2.5,2.5,0.25,2.5), ps=8, cex=1, mgp=c(1,0.25,0), tcl=-0.15)

plot(1:10, xlab="x", ylab="y", type='o')

par(new=TRUE)

plot(x=1:10, y=cumsum(rnorm(10)), xlab="", ylab="", type='o', col='red', xaxt="n", yaxt="n")

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### Get a list of all your installed packages (so that you can reinstall them after updating R)

a <- installed.packages()

my_pkgs <- dimnames(a)[[1]]

save(my_pkgs, file="my_pkgs.RData")

# exit R, reopen R, then continue to following
load("my_pkgs.RData")

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set.seed(1337)

mean_true_sppA <- 0.25
mean_true_sppB <- 0.75
grid_w <- 5
grid_h <- 5
environment <- seq(0.8, 1.2, length.out=grid_w*grid_h) # makes it so the 2 distributions are not independent
n_reps <- 20

probs_true_sppA <- plogis(rnorm(grid_w*grid_h, qlogis(mean_true_sppA*environment))) # the GAM model estimates these values