# Simulated data for gjam analysis Simulates data for analysis by `gjam`. ```r gjamSimData(n = 1000, S = 10, Q = 5, x = NULL, nmiss = 0, typeNames, effort = NULL) ``` ## Arguments - `n`: Sample size - `S`: Number of response variables (columns) in `y`, typically less than `n` - `Q`: Number of predictors (columns) in design matrix `x << n` - `x`: design `matrix`, if supplied `n` and `Q` will be set to `nrow(x)` and `ncol(x)`, respectively - `nmiss`: Number of missing values to in `x << n` - `typeNames`: Character vector of data types, see Details - `effort`: List containing '`columns`' specifying columns to which effort applies, and '`values`', a length-`n` vector of effort per observation. ## Returns - **formula**: R formula for model, e.g., `~ x1 + x2` - **xdata**: `data.frame` includes columns for predictors in the design matrix - **ydata**: `data.frame` for the simulated response - **y**: response as a `n` by `S` `matrix` as assembled in `gjam`. - **w**: `n` by `S` latent states - **typeY**: vector of data types corresponding to columns in `y`, see Details - **typeNames**: vector of data types corresponding to columns in `ydata` - **trueValues**: list containing true parameter values `beta` (regression coefficients), `sigma` (covariance matrix), `corSpec` (correlation matrix corresponding to `sigma`), and `cuts` (partition matrix for ordinal data). - **effort**: see Arguments . ## Details Generates simulated data and parameters for analysis by `gjam`. Because both parameters and data are stochastic, not all simulations will give good results. `typeNames` can be '`PA`' (presenceAbsence), '`CA`' (continuous), '`DA`' (discrete), '`FC`' (fractional composition), '`CC`' (count composition), '`OC`' (ordinal counts), and '`CAT`' (categorical levels). If more than one `'CAT'` is included, each defines a multilevel categorical reponse. One additional type, '`CON`' (continuous), is not censored at zero by default. If defined as a single `character` value `typeNames` applies to all columns in `y`. If not, `typeNames` is length-`S` `character vector`, identifying each response by column in `y`. If a column `'CAT'` is included, a random number of levels will be generated, `a, b, c, ...`. A more detailed vignette is can be obtained with: `browseVignettes('gjam')` website 'http://sites.nicholas.duke.edu/clarklab/code/'. ## Author(s) James S Clark, jimclark@duke.edu ## References Clark, J.S., D. Nemergut, B. Seyednasrollah, P. Turner, and S. Zhang. 2016. Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecological Monographs 87, 34-56. ## See Also `gjam` ## Examples ```r ## Not run: ## ordinal data, show true parameter values sim <- gjamSimData(S = 5, typeNames = 'OC') sim$ydata[1:5,] # example data sim$trueValues$cuts # simulated partition sim$trueValues$beta # coefficient matrix ## continuous data censored at zero, note latent w for obs y = 0 sim <- gjamSimData(n = 5, S = 5, typeNames = 'CA') sim$w sim$y ## continuous and discrete data types <- c(rep('DA',5), rep('CA',4)) sim <- gjamSimData(n = 10, S = length(types), Q = 4, typeNames = types) sim$typeNames sim$ydata ## composition count data sim <- gjamSimData(n = 10, S = 8, typeNames = 'CC') totalCount <- rowSums(sim$ydata) cbind(sim$ydata, totalCount) # data with sample effort ## multiple categorical responses - compare matrix y and data.frqme ydata types <- rep('CAT',2) sim <- gjamSimData(S = length(types), typeNames = types) head(sim$ydata) head(sim$y) ## discrete abundance, heterogeneous effort S <- 5 n <- 1000 ef <- list( columns = 1:S, values = round(runif(n,.5,5),1) ) sim <- gjamSimData(n, S, typeNames = 'DA', effort = ef) sim$effort$values[1:20] ## combinations of scales, partition only for 'OC' columns types <- c('OC','OC','OC','CC','CC','CC','CC','CC','CA','CA','PA','PA') sim <- gjamSimData(S = length(types), typeNames = types) sim$typeNames head(sim$ydata) sim$trueValues$cuts ## End(Not run) ```

gjamSimData function