validate {secrdesign}

Reject Implausible Statistics

Description

Simulation output may contain rogue values due to idiosyncracies of model fitting. For example, nonidentifiability due to inadequate data can result in spurious extreme ‘estimates’ of the sampling variance. Undue influence of rogue replicates can be reduced by using the median as a summary field rather than the mean. This function is another way to deal with the problem, by setting to NA selected statistics from replicates for which some ‘test’ statistic is out-of-range.

Usage

validate(x, test, validrange = c(0, Inf), targets = test, quietly = FALSE)

Arguments

x

object that inherits from ‘selectedstatistics’

test

character; name of statistic to check

validrange

numeric vector comprising the minimum and maximum permitted values of ‘test’, or a matrix (see details)

targets

character vector with names of one or more statistics to set to missing (NA) when test is out-of-range

quietly

logical; if TRUE messages are suppressed

Details

Values of ‘test’ and ‘targets’ should be columns in each component ‘replicate x statistic’ matrix (i.e., scenario) of x$output. You can check for these with find.stats.

If validrange is a matrix its first and second columns are interpreted as scenario-specific bounds (minima and maxima), and the number of rows must match the number of scenarios.

If all non-missing values of ‘test’ are in the valid range, the effect is to force the target statistics to NA wherever ‘test’ is NA.

The default is to change only the test field itself. If the value of ‘test’ does not appear in ‘targets’ then the test field is unchanged.

If targets = "all" then all columns are set to NA when the test fails.

Value

An object of class c(‘selectedstatistics’, secrdesign', ‘list’) with the same structure and header information as the input, but possibly with some values in the ‘output’ component converted to NA.

See Also

select.stats, find.stats

Examples

## Not run: 

## generate some data
scen1 <- make.scenarios(D = c(5,10), sigma = 25, g0 = 0.2)
traps1 <- make.grid()
tmp1 <- run.scenarios(nrepl = 5, trapset = traps1, scenarios = scen1,
    fit = TRUE, extractfn = trim)
tmp2 <- predict(tmp1)
tmp3 <- select.stats(tmp2, 'D', c('estimate','RB','RSE','COV'))

## just for demonstration --
## apply scenario-specific +/- 20% bounds for estimated density
## set RB, RSE and COV to NA when estimate is outside this range
permitted <- outer(tmp3$scenarios$D, c(0.8,1.2)) 
permitted   ## a 2 x 2 matrix
tmp4 <- validate(tmp3, 'estimate', permitted, c('RB', 'RSE','COV'))

## what have we done?!
tmp4$output
summary(tmp4)


## End(Not run)