summary.secrdesign: Generic Methods for secrdesign Objects

Description

Methods to summarize simulated datasets.

Usage

# S3 method for secrdesign
summary(object, ...)
# S3 method for rawdata
summary(object, ...)
# S3 method for estimatetables
summary(object, ...)
# S3 method for selectedstatistics
summary(object, fields = c('n', 'mean',
'se'), dec = 5, alpha = 0.05, type = c('list','dataframe','array'), ...)
# S3 method for selectedstatistics
plot(x, scenarios, statistic, type =
c('hist', 'CI'), refline, xlab = NULL, ...)
header(object)

Value

List with components `header'

call: original function call
starttime: from object
proctime: from object
constants: small dataframe with values of non-varying inputs
varying: small dataframe with values of varying inputs
fit.args: small dataframe with values arguments for secr.fit, if specified

and `OUTPUT', a list with one component for each field. Each component may be a list or an array.

Arguments

object: object of class simulations from run.scenarios
dec: number of decimal places in output
fields: character vector; names of required summary statistics (see Details)
alpha: alpha level for confidence intervals and quantiles
type: character code for type of output (see Details)
...: other arguments -- not currently used by summary but passed to hist by the plot method
x: object of class `selectedstatistics' from run.scenarios
scenarios: integer indices of scenarios to plot (all plotted if not specified)
statistic: integer or character indices of the statistics in x for which histograms are requested
refline: logical; if TRUE a reference line is plotted at the true value of a parameter
xlab: character; optional label for x-axis

Details

If object inherits from `selectedstatistics' then the numeric results from replicate simulations are summarized using the chosen `fields' (by default, the number of non-missing values, mean and standard error), along with header information describing the simulations. Otherwise the header alone is returned.

fields is a vector of any selection from c(`n', `mean', `sd', `se', `min', `max', `lcl', `ucl', `median', `q', `rms', `var'), or the character value `all'.

Field `q' provides 1000 alpha/2 and 1000[1 - alpha/2] quantiles qxxx and qyyy.

`lcl' and `ucl' refer to the upper and lower limits of a 100(1 - alpha)% confidence interval for the statistic, across replicates.

`rms' gives the root-mean-square of the statistic - most useful for the statistic `ERR' (see select.stats) when it represents the overall accuracy or RMSE.

The plot method plots either (i) histograms of the selected statistics (type = `hist') or (ii) the estimate and confidence interval for each replicate (type = `CI'). The default for type = `hist' is to plot the first statistic - this is usually `n' (number of detected animals) when fit = FALSE, and `estimate' (parameter estimate) when fit = TRUE. If length(statistic) > 1 then more than one plot will be produced, so a multi-column or multi-row layout should be prepared with par arguments `mfcol' or `mfrow'.

For type = `CI' the statistics must include `estimate', `lcl' and `ucl' (or `beta', `lcl' and `ucl' if outputtype = `coef').

estimateSummary is a simpler approach that provides full output for models with groups or multiple sessions simulated in run.scenarios with extractfn predict or coef).

Examples

Run this code


## collect raw counts
scen1 <- make.scenarios(D = c(5,10), sigma = 25, g0 = 0.2)
traps1 <- make.grid()
tmp1 <- run.scenarios(nrepl = 50, trapset = traps1, scenarios = scen1,
    fit = FALSE)

opar <- par(mfrow=c(2,3))
plot(tmp1, statistic = 1:3)
par(opar)

summary(tmp1)

summary(tmp1, field=c('q025', 'median', 'q975'))