inst/doc/getting-started.R

In phenoCDM: Continuous Development Models for Incremental Time-Series Analysis

## ---- echo=TRUE, eval = FALSE--------------------------------------------
#  
#  install.packages("phenoCDM")
#  

## ---- echo=TRUE, eval = FALSE--------------------------------------------
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#  if(!require(devtools)) install.packages("devtools")
#  
#  install_github(repo = 'bnasr/phenoCDM')
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## ---- echo=TRUE, eval = FALSE--------------------------------------------
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#  library(phenoCDM)
#  

## ---- echo=TRUE, eval = FALSE--------------------------------------------
#  
#  set.seed(2)
#  
#  ssSim <- phenoSim(nSites = 3, #number of sites
#                    nTSet = 30, #number of time steps
#                    beta = c(1, 2), #beta coefficients
#                    sig = 0.05, #process error
#                    tau = 0.25, #observation error
#                    plotFlag = F, #whether plot the data or not
#                    miss = 0.1, #portion of missing data
#                    ymax = c(9,5, 3) #maximum of saturation trajectory
#  )
#  
#  

## ---- echo=TRUE, eval = FALSE--------------------------------------------
#  
#  
#  ww1 <- which(is.na( ssSim$connect[,1]))
#  ww2 <- which(is.na( ssSim$connect[,2]))
#  
#  png('fig1.png', width = 6, height = 3, units = 'in', res = 300)
#  
#  par(mfrow = c(1,3), oma = c(4,4,4,3), mar=c(0,1,0,0))
#  for(i in 1:length(ww1))  {
#    z <- ssSim$z[ww1[i]:ww2[i]]
#    ymax <- ssSim$ymax[i]
#    plot(z, xlab = 'Index', ylab = '', type = 'b', ylim = range(c(0, ymax, ssSim$z), na.rm = T), yaxt= switch(i, '1'='s', '2'='n', '3' = 'n'))
#    mtext(paste('Set', i), side = 3, line = .3, col = 'blue', font=1)
#    abline(h = ymax, col='red')
#  }
#  
#  mtext(text = 'Response (z)', side = 2, line = 2, outer = T, font = 2)
#  mtext(text = 'Index', side = 1, line = 2.5, outer = T, font = 2)
#  mtext('Simulated time-series data', side = 3, outer = T, line = 1.5, font = 2)
#  legend('bottomright', legend = c('z', 'ymax'), col = c('black', 'red'), lty = 1, bty = 'n', cex=1.5, lwd =2)
#  
#  dev.off()
#  

## ---- echo=TRUE, eval = FALSE--------------------------------------------
#  
#  ssOut <- fitCDM(x = ssSim$x, #predictors
#                  nGibbs = 2000,
#                  nBurnin = 1000,
#                  z = ssSim$z,#response
#                  connect = ssSim$connect, #connectivity of time data
#                  quiet=T,
#                  calcLatentGibbs = T)

## ---- echo=TRUE, eval = FALSE--------------------------------------------
#  
#  summ <- getGibbsSummary(ssOut, burnin = 1000, sigmaPerSeason = F)
#  
#  colMeans(summ$ymax)
#  colMeans(summ$betas)
#  colMeans(summ$tau)
#  colMeans(summ$sigma)
#  

## ---- echo=TRUE, eval = FALSE--------------------------------------------
#  
#  png('fig2.png', width = 8, height = 3, units = 'in', res = 300)
#  par(mfrow = c(1,3), oma = c(1,1,3,1), mar=c(2,2,0,1), font.axis=2)
#  
#  plotPost(chains = ssOut$chains[,c("beta.1", "beta.2")], trueValues = ssSim$beta)
#  plotPost(chains = ssOut$chains[,c("ymax.1", "ymax.2", "ymax.3")], trueValues = ssSim$ymax)
#  plotPost(chains = ssOut$chains[,c("sigma", "tau")], trueValues = c(ssSim$sig, ssSim$tau))
#  mtext('Posterior distributions of the parameters', side = 3, outer = T, line = 1, font = 2)
#  legend('topleft', legend = c('posterior', 'true value'), col = c('black', 'red'), lty = 1, bty = 'n', cex=1.5, lwd =2)
#  
#  dev.off()
#  

## ---- echo=TRUE, eval = FALSE--------------------------------------------
#  
#  
#  yGibbs <- ssOut$latentGibbs
#  zGibbs <- ssOut$zpred
#  o <- ssOut$data$z
#  p <- apply(ssOut$rawsamples$y, 1, mean)
#  R2 <- cor(na.omit(cbind(o, p)))[1,2]^2
#  
#  png('fig3.png', width = 5, height = 5, units = 'in', res = 300)
#  par( mar=c(4,4,1,1), font.axis=2)
#  plotPOGibbs(o = o , p = zGibbs,
#              xlim = c(0,10), ylim=c(0,10),
#              cex = .7, nburnin = 1000)
#  points(o, p, pch = 3)
#  mtext(paste0('R² = ', signif(R2, 3)), line = -1, cex = 2, font = 2, side = 1, adj = .9)
#  legend('topleft', legend = c('mean', '95th percentile', '1:1 line', 'latent states'),
#         col = c('#fb8072','#80b1d3','black', 'black'),
#         bty = 'n', cex=1.5,
#         lty = c(NA, 1, 2, NA), lwd =c(NA, 2, 2, 2), pch = c(16, NA, NA, 3))
#  dev.off()
#  
#  

## ---- echo=TRUE, eval = FALSE--------------------------------------------
#  
#  ?phenoCDM::fitCDM
#