wsValidate: Methods to assess length-bias in a proposed standard weight...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/wsValidate.R

Description

The Willis and empirical quantiles (EmpQ) methods to assess length-bias in a proposed standard weight equation.

Usage

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wsValidate(object, df, pops, len, wt, min, max, w = 10, type = c("EmpQ",
  "Willis"), n.cutoff = 3, cutoff.tail = TRUE, qtype = 8, probs = 0.75,
  use.means = FALSE, quadratic = TRUE, weighted = FALSE, alpha = 0.05)

## S3 method for class 'willis'
print(x, ...)

## S3 method for class 'willis'
summary(object, ...)

## S3 method for class 'empq'
summary(object, ...)

## S3 method for class 'empq'
anova(object, ...)

## S3 method for class 'empq'
coef(object, ...)

## S3 method for class 'empq'
predict(object, ...)

## S3 method for class 'empq'
plot(x, pch = 16, col.pt = "black",
  xlab = "Midpoint Length Category", ylab = paste("Standardized", 100 *
  x$probs, "Percentile Mean Weight"), ...)

## S3 method for class 'empq'
fitPlot(object, pch = 16, col.pt = "black",
  col.mdl = "red", lwd.mdl = 3, lty.mdl = 1,
  xlab = "Midpoint Length Category", ylab = paste("Standardized", 100 *
  object$probs, "Percentile Mean Weight"), main = "EmpQ Method", ...)

Arguments

object

An object of class RLP or EMP returned from calling rlp() or emp() in the main function and an object of class class empq or willis (saved from the wsValidate) in the generic functions.

df

A data frame that contains the length-weight data for each population.

pops

A string or numeric that indicates which column in df contains the variable that identifies the different populations.

len

A string or numeric that indicates which column in df contains the variable with the length data.

wt

A string or numeric that indicates which column in df contains the variable with the weight data.

min

A number that indicates the midpoint value of the smallest X-mm length category.

max

A number that indicates the midpoint value of the largest X-mm length category.

w

A number that indicates the widths for which to create length categories.

type

A string that indicates which type of bias detection method should be used.

n.cutoff

A numeric that indicates the minimum sample size in a length category that should be included in the EmpQ regression. Ignored if type="Willis".

cutoff.tail

A logical that indicates if all length categories larger than the lowest length category with a sample size below n.cutoff should be excluded =TRUE or just those length categories with sample sizes lower than n.cutoff. Ignored if type="Willis".

qtype

Type of quantile method to use. See details. Ignored if use.means=TRUE.

probs

A number that indicates the probability of the quantile. Must be between 0 and 1. Ignored if use.means=TRUE.

use.means

A logical that indicates whether mean mean weight rather than a quantile mean weight should be used in the EmpQ method.

quadratic

A logical that indicates whether a quadratic regression should be fit in the EmpQ method. Ignored if type="Willis".

weighted

A logical that indicates whether the regression in the EmpQ method should be weighted by the number of populations present in each length category. Ignored if type="Willis".

alpha

A numeric that indicates the rejection criterion to be used in the Willis method. Ignored if type="EmpQ".

x

An object saved from the wsValidate call (i.e., of class empq or willis).

pch

A single numeric that indicates what plotting characther codes should be used for the points in plot or fitPlot.

col.pt

A string used to indicate the color of the plotted points.

xlab

A label for the x-axis of plot or fitPlot.

ylab

A label for the y-axis of plot or fitPlot.

col.mdl

A string that indicates the type of color to use for the standard length-weight regression line.

lwd.mdl

A numeric that indicates the width of the line to use for the standard length-weight regression line.

lty.mdl

A numeric that indicates the type of line to use for the standard length-weight regression line.

main

A label for the main title of fitPlot.

...

Additional arguments for methods.

Details

The main function can be used to assess length-bias in a proposed standard weight equation using either the method of Willis et al. (1991) (i.e., type="Willis") or the empricial quantiles method of Gerow et al. (2004) (i.e., type="EmpQ"). The Willis method begins by regressing the relative weight computed from the candidate standard weight equation (supplied in object) for each individual in a population in the df data frame against length. This is repeated for each population in df. The number of positive and negative slopes from this regression that are statistically significant are counted and a chi-square test is used to determine if there is a statistically equal number of each. If there is a statistically equal number of positive and negative significant slopes then the standard weight equation is said not to exhibit a length bias.

The EmpQ method is performed by (1) computing the mean actual weight per w-mm length category for each population, (2) computing the given quartile (default is third) of mean actual weight per length category across all populations, (3) standardizing the quartile mean weights by dividing each by the standard weight for the midpoint of the length categories using the proposed standard weight equation, and (4) regressing the standardized quartile mean weights against the length category midpoints. The regression can either be quadratic (i.e., quadratic=TRUE) as proposed by Gerow et al. (2004) or n-weighted (i.e., weighted=TRUE). In addition, length categories with fewer than ncutoff are eliminated (see cutoff.tail description above). A slope of zero for the relationship between standardized quartile mean weights and length category midpoints indicates that no length-based biases exist with the proposed standard weight equation.

Types of quantile calculation methods are discussed in the details of quantile.

Value

If type="Willis" then a list is returned with the following three items.

If type="EmpQ" then a list is returned with the following five items:

Author(s)

Derek H. Ogle, [email protected]

References

Gerow, K.G., W.A. Hubert, R.C. Anderson-Sprecher. 2004. An alternative approach to detection of length-related biases in standard weight equations. North American Journal of Fisheries Management 24:903-910.

Willis, D.W., C.S. Guy, and B.R. Murphy. 1991. Development and evaluation of the standard weight (Ws) equation for yellow perch. North American Journal of Fisheries Management, 11:374-380.

See Also

rlp, emp, and FroeseWs; and quantile in stats

Examples

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## See examples in rlp(), emp(), and FroeseWs()

droglenc/FSAWs documentation built on July 8, 2018, 7:01 a.m.