fitCDM: Fit a CDM Model In phenoCDM: Continuous Development Models for Incremental Time-Series Analysis

Description

This function fits a CDM model on the input data as it is described by the phenoSim function.

Usage

 ```1 2``` ```fitCDM(x, z, connect = NULL, nGibbs = 1000, nBurnin = 1, n.adapt = 100, n.chains = 4, quiet = FALSE, calcLatentGibbs = FALSE, trend = +1) ```

Arguments

 `x` Matrix of predictors [N x p]. `z` Vector of response values [N x 1]. `connect` The connectivity matrix for the z vector [n x 2]. Each row contains the last and next elements of the time-series. NA values indicate not connected. `nGibbs` Number of MCMC itterations `nBurnin` Number of burn-in itterations. `n.adapt` Number of itterations for adaptive sampling `n.chains` Number of MCMC chains `quiet` logical value indicating whether to report the progress `calcLatentGibbs` logical value indicating whether to calculate the latent states `trend` time-series expected trend as -1:decreasing, +1:increasing, 0: not constrained

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```#Summarize CDM Model Ouput ssSim <- phenoSim(nSites = 2, #number of sites nTSet = 30, #number of Time steps beta = c(1, 2), #beta coefficients sig = .01, #process error tau = .1, #observation error plotFlag = TRUE, #whether plot the data or not miss = 0.05, #fraction of missing data ymax = c(6, 3) #maximum of saturation trajectory ) ssOut <- fitCDM(x = ssSim\$x, #predictors nGibbs = 200, nBurnin = 100, z = ssSim\$z,#response connect = ssSim\$connect, #connectivity of time data quiet=TRUE) summ <- getGibbsSummary(ssOut, burnin = 100, sigmaPerSeason = FALSE) colMeans(summ\$ymax) colMeans(summ\$betas) colMeans(summ\$tau) colMeans(summ\$sigma) ```

phenoCDM documentation built on May 2, 2018, 5:04 p.m.