## ---- echo=FALSE--------------------------------------------------------------
knitr::opts_chunk$set(cache = FALSE,
                        fig.width = 9,
                        message = FALSE,
                        warning = FALSE)

## ----install-bioc,eval=FALSE--------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly = TRUE))
#      install.packages("BiocManager")
#  

## -----------------------------------------------------------------------------
library(miaSim)
A_normal <- powerlawA(n_species = 12, alpha = 3)

## -----------------------------------------------------------------------------
A_uniform <- randomA(n_species = 10, d = -0.4, min_strength = -0.8,
                        max_strength = 0.8, connectance = 0.5)

## -----------------------------------------------------------------------------

ExampleGLV <- simulateGLV(n_species = 12, A_normal, t_start = 0, 
    t_store = 1000, stochastic = FALSE, norm = FALSE)

ExampleRicker <- simulateRicker(n_species=12, A_normal, tend=100, norm = FALSE)

## -----------------------------------------------------------------------------
Time <- simulationTimes(t_start = 0, t_end = 100, t_step = 0.5, 
                           t_store = 100)
Time$t.index 

## -----------------------------------------------------------------------------
ExampleHubbell <- simulateHubbell(n_species = 8, M = 10, I = 1000, d = 50,
                                    m = 0.02, tend = 100)

ExampleHubbellRates <- simulateHubbellRates(community_initial = c(0,5,10),
    migration_p = 0.1, metacommunity_p = NULL, k_events = 1, 
    growth_rates = NULL, norm = FALSE, t_end=1000)

## -----------------------------------------------------------------------------
ExampleSOI <- simulateSOI(n_species = 12, I = 1000, A_normal, k=5, com = NULL,
                                            tend = 150, norm = TRUE)

## -----------------------------------------------------------------------------
ExampleLogistic <- simulateStochasticLogistic(n_species = 5)

## -----------------------------------------------------------------------------
ExampleConsumerResource <- simulateConsumerResource(n_species = 2,
    n_resources = 4, eff = randomE(n_species = 2, n_resources = 4))

# visualize the dynamics of the model
Consumer_plot <- matplot(ExampleConsumerResource, type = "l")

## -----------------------------------------------------------------------------
ExampleHubbellRates <- simulateHubbellRates(community_initial = c(0,5,10),
    migration_p = 0.1, metacommunity_p = NULL, k_events = 1, 
    growth_rates = NULL, norm = FALSE, t_end=1000)

HubbellSE <- convertToSE(assay = ExampleHubbellRates$counts,
                        colData = ExampleHubbellRates$time,
                        metadata = ExampleHubbellRates$metadata)

## ----eval=FALSE, include=FALSE------------------------------------------------
#  library(TreeSummarizedExperiment)
#  help("TreeSummarizedExperiment-constructor", package = TreeSummarizedExperiment)

## ----eval=FALSE, include=FALSE------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly = TRUE))
#      install.packages("BiocManager")
#  
#  BiocManager::install("miaViz")

## ----eval=FALSE, include=FALSE------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly = TRUE))
#      install.packages("BiocManager")
#  
#  BiocManager::install("mia")

## ----eval=FALSE, include=FALSE------------------------------------------------
#  library(miaViz)
#  
#  HubbellDensityPlot <- plotAbundanceDensity(HubbellSE, abund_values = "counts")

## -----------------------------------------------------------------------------

ExampleGLV <- simulateGLV(n_species = 12, A_normal, t_start = 0, 
    t_store = 1000, stochastic = FALSE, norm = FALSE)

rownames(ExampleGLV) <- c(paste("Species", rownames(ExampleGLV), sep = "_"))
colnames(ExampleGLV) <- c(paste("Sample", seq_len(ncol(ExampleGLV)), sep = "_"))

df <- DataFrame(sampleID = colnames(ExampleGLV),
            Time = seq(1, 1000, 1),
            SubjectID = rep(1:4, 250),
            row.names = colnames(ExampleGLV))

SE_GLV <- convertToSE(assay = ExampleGLV,
                      colData = df)

## -----------------------------------------------------------------------------
sessionInfo()