Description Usage Arguments Details Value Author(s) References See Also Examples
The Willis and empirical quantiles (EmpQ) methods to assess lengthbias in a proposed standard weight equation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  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", ...)

object 
An object of class 
df 
A data frame that contains the lengthweight data for each population. 
pops 
A string or numeric that indicates which column in 
len 
A string or numeric that indicates which column in 
wt 
A string or numeric that indicates which column in 
min 
A number that indicates the midpoint value of the smallest Xmm length category. 
max 
A number that indicates the midpoint value of the largest Xmm 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 
cutoff.tail 
A logical that indicates if all length categories larger than the lowest length category with a sample size below 
qtype 
Type of quantile method to use. See details. Ignored if 
probs 
A number that indicates the probability of the quantile. Must be between 0 and 1. Ignored if 
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 
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 
alpha 
A numeric that indicates the rejection criterion to be used in the Willis method. Ignored if 
x 
An object saved from the 
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 xaxis of plot or fitPlot. 
ylab 
A label for the yaxis of plot or fitPlot. 
col.mdl 
A string that indicates the type of color to use for the standard lengthweight regression line. 
lwd.mdl 
A numeric that indicates the width of the line to use for the standard lengthweight regression line. 
lty.mdl 
A numeric that indicates the type of line to use for the standard lengthweight regression line. 
main 
A label for the main title of fitPlot. 
... 
Additional arguments for methods. 
The main function can be used to assess lengthbias 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 chisquare 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 nweighted (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 lengthbased biases exist with the proposed standard weight equation.
Types of quantile calculation methods are discussed in the details of quantile
.
If type="Willis"
then a list is returned with the following three items.
res.ind
is a data frame that contains the results of the individual regressions.
res.tbl
) is the table summarizing the number of positive and negative significant slopes.
res.test
) contains the results for the chisquare test.
If type="EmpQ"
then a list is returned with the following five items:
n.by.pop
) is a table of the number of populations represented in each length category.
regdata
) is a dataframe used for the EmpQ regression.
quadratic
) is a logical that indicates whether the quadratic regression was used.
weighted
) is a logical that indicates whether a weighted regression was used.
lm.v
is the EmpQ regression model results.
Derek H. Ogle, [email protected]
Gerow, K.G., W.A. Hubert, R.C. AndersonSprecher. 2004. An alternative approach to detection of lengthrelated biases in standard weight equations. North American Journal of Fisheries Management 24:903910.
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, 11:374380.
rlp
, emp
, and FroeseWs
; and quantile
in stats
1  ## See examples in rlp(), emp(), and FroeseWs()

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