# Censor gjam response data Returns a `list` with censored values, intervals, and censored response `matrix y`. ```r gjamCensorY(values, intervals, y, type='CA', whichcol = c(1:ncol(y))) ``` ## Arguments - `values`: Values in `y` that are censored, specified by `intervals` - `intervals`: `matrix` having two rows and one column for each value in `values`. The first row holds lower bounds. The second row holds upper bounds. See Examples . - `y`: Response `matrix`, `n` rows by `S` columns. All values within `intervals` will be replaced with `values` - `type`: Response type, see `typeNames` in `gjam` - `whichcol`: Columns in `y` that are censored (often not all responses are censored) ## Returns Returns a list containing two elements. - **`y`**: n by S matrix updated with censored values substituted for those falling within `intervals`. - **`censor`**: `list` containing `$columns` that are censored and `$partition`, a matrix with 3 rows used in `gjam` and `gjamPlot`, one column per censor interval. Rows are values, followed by lower and upper bounds. ## Details Any values in `y` that fall within censored `intervals` are replaced with censored `values`. The example below simulates data collected on an 'octave scale': 0, 1, 2, 4, 8, ..., an approach to accelerate data collection with approximate bins. ## Examples ```r ## Not run: # data in octaves v <- up <- c(0, 2^c(0:4), Inf) dn <- c(-Inf, v[-length(v)]) i <- rbind( dn, up ) # intervals f <- gjamSimData(n = 2000, S = 15, Q = 3, typeNames='CA') y <- f$y cc <- c(3:6) # censored columns g <- gjamCensorY(values = v, intervals = i, y = y, whichcol = cc) y[,cc] <- g$y # replace columns ml <- list(ng = 500, burnin = 100, censor = g$censor, typeNames = f$typeNames) output <- gjam(f$formula, xdata = f$xdata, ydata = y, modelList = ml) #repeat with ng = 2000, burnin = 500, then: pl <- list(trueValues = f$trueValues, width = 3, height = 3) gjamPlot(output, pl) # upper detection limit up <- 5 v <- up i <- matrix(c(up,Inf),2) rownames(i) <- c('down','up') f <- gjamSimData(typeNames='CA') g <- gjamCensorY(values = v, intervals = i, y = f$y) ml <- list(ng = 500, burnin = 100, censor = g$censor, typeNames = f$typeNames) out <- gjam(f$formula, xdata = f$xdata, ydata = g$y, modelList = ml) #repeat with ng = 2000, burnin = 500, then: pl <- list(trueValues = f$trueValues, width = 3, height = 3) gjamPlot(out, pl) # lower detection limit lo <- .001 values <- upper <- lo intervals <- matrix(c(-Inf,lo),2) rownames(intervals) <- c('lower','upper') ## End(Not run) ``` ## Author(s) James S Clark, jimclark@duke.edu ## References Clark, J.S., D. Nemergut, B. Seyednasrollah, P. Turner, and S. Zhang. 2017. Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecological Monographs 87, 34-56. ## See Also `gjamSimData` simulates data `gjam` analyzes data A more detailed vignette is can be obtained with: `browseVignettes('gjam')` website 'http://sites.nicholas.duke.edu/clarklab/code/'.

gjamCensorY function