Size Ratio

Usage

size_null_model(speciesData, algo = "size_uniform", metric = "var_ratio", nReps = 1000, rowNames = TRUE, saveSeed = FALSE, algoOpts = list(), metricOpts = list())

Arguments

speciesData

a dataframe

algo

the algorithm to use, must be "size_uniform", "size_uniform_user", "size_source_pool", "size_gamma"

metric

the metric used to caluclate the null model: choices are "min_diff", "min_ratio", "var_diff", "var_ratio"; default is Var.Ratio

nReps

the number of replicates to run the null model.

rowNames

Does your dataframe have row names? If yes, they are stripped, otherwise FALSE for data that has no row names

saveSeed

TRUE or FALSE. If TRUE the current seed is saved so the simulation can be repeated

algoOpts

a list containing all the options for the specific algorithm you want to use. Must match the algorithm given in the `algo` argument

metricOpts

a list containing all the options for the specific metric you want to use. Must match the metric given in the `metric` argument

Examples

## <strong>Not run</strong>: 
# ## Run the null model
# rodentMod <- size_null_model(dataRodents)
# ## Summary and plot info
# summary(rodentMod)
# plot(rodentMod,type="hist")
# plot(rodentMod,type="size")
# 
# ##  Uniform Size model with user inputs
# rodentMod2 <- size_null_model(dataRodents,algo="size_uniform_user",
# algoOpts = list(userLow = 3,userHigh=15))
# summary(rodentMod2)
# plot(rodentMod2,type="hist")
# plot(rodentMod2,type="size")
# 
# ### Source pool model
# 
# rodentMod_sp <- size_null_model(dataRodents,algo="size_source_pool",
# algoOpts = list(sourcePool = runif(dim(dataRodents)[1],1,15)))
# 
# summary(rodentMod_sp)
# plot(rodentMod_sp,type="hist")
# plot(rodentMod_sp,type="size")
# 
# ## <strong>End(Not run)</strong>