This function implements the methodology of Helser and Hayes (1995) for generating quantitative reference points from relative abundance indices based on research surveys

```
surveyref(x = NULL, refpt = 25, compyear = NULL, reffix = FALSE,
refrange = NULL, nboot = 500, allboots = FALSE)
```

x

output object from function `surveyfit`

.

refpt

the lower quantile (percentile) of the fitted time series used as the reference point.

compyear

the index year to compare to the reference point. Multiple years can be included in the comparison using the `c()`

function.

reffix

a logical value specifying whether the lower quantile should be determined from a fixed set of years. Default = FALSE.

refrange

If `reffix`

= TRUE, the beginning and ending year of the time series to include in
determination of the lower quantile. The values should be enclosed within `c()`

(e.g., c(1963,1983)).

nboot

the number of bootstrap replicates.

allboots

a logical value specifying whether the fitted values for the bootstrap replicates should be included in the output. Default = FALSE.

list containing the lower quantile of the original fitted time series and the mean quantile of the
fitted bootstrap replicates (`comp_refpt`

), the original fitted time series values versus the mean of the fitted
bootstrap time series values(`comp_fitted`

), the empirical distribution of the selected index (`emp_dist_index`

),
the empirical distribution of the lower quantile (`emp_dist_refpt`

), the probability that the index
value lies below the reference point for a given decision confidence level (`prob_index`

), and, if argument `allboots`

is TRUE, the fitted values
of the bootstrap replicates (`boot_runs`

).

Using the output object from function `surveyfit`

, the methodology of Helser and Hayes (1995) is applied to
generate the probability distribution that the abundance index value for a given year lies below the value of a
lower quantile (reference point). The procedure is : 1) add to the original fitted time series residuals randomly selected
with replacement from the Pennington model fit, 2) repeat this `nboot`

times to create new time series,
3) fit the Pennington model to each new time series using the original theta estimate to get `nboot`

replicates
of new fitted time series, and 4) determine the lower quantile for each new fitted time
series. A probability profile specifying the probability that the selected index value is less than the quantile reference
point for various decision confidence levels is calculated following Helser et al. (2002).

If comparisons between the current year's index and the reference point will be made year-after-year, Helser and Hayes
(1995) recommend using a fixed set of years to select the lower quantile. This procedure will avoid a change in
reference point over time as a survey time series is updated. Use arguments `reffix`

and `refrange`

to
accomplish this.

Helser, T. E. and D. B. Hayes. 1995. Providing quantitative management advice from stock abundance indices based on research surveys. Fishery Bulletin 93: 290-298.

Hesler, T. E., A. Sharov, and D. M. Kahn. 2002. A stochastic decision-based approach to assessing the status of the Delaware Bay blue crab stock. American Fisheries Society Symposium 27: 63-82.

```
# NOT RUN {
data(wolffish)
out<-surveyfit(year=wolffish$year,index=wolffish$index,logtrans=TRUE)
surveyref(out,refpt=25,compyear=c(1990))
# }
```

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