rlp: Computes the standard weight equation using the...
In droglenc/FSAWs: Functions for constructing and validating standard weight (Ws) equations for fish.

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

Computes the standard weight equation using the regression-line-percentile method when given the log(a) and b values for a population of length-weight regression equations.

rlp(log.a, b, min, max, w = 10, qtype = 8, probs = 0.75, digits = NULL)

## S3 method for class 'rlp'
plot(x, what = c("both", "raw", "log"), col.pop = c("rich",
  "cm", "default", "grey", "gray", "heat", "jet", "rainbow", "topp", "terrain"),
  lwd.pop = 1, lty.pop = 1, order.pop = TRUE, col.Ws = "black",
  lwd.Ws = 3, lty.Ws = 1, ...)

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

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

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

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

## S3 method for class 'rlp'
fitPlot(object, pch = 16, col.pt = "black", col.Ws = "red",
  lwd.Ws = 3, lty.Ws = 1, xlab = "log10(midpt Length)",
  ylab = paste("log10(", 100 * object$prob,
  " Percentile of Predicted Weight)", sep = ""), main = "RLP Equation Fit",
  ...)

## S3 method for class 'rlp'
residPlot(object, ...)

`log.a`	A numeric vector that contains the log_{10}(a) values for the population of length-weight regression equations.
`b`	A numeric vector that contains the b values for the population of length-weight regression equations
`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.
`qtype`	Type of quantile method to use. See details.
`probs`	A number that indicates the probability of the quantile. Must be between 0 and 1.
`digits`	Number of digits to round predicted weights.
`x`	An object saved from the `rlp` call (i.e., of class `rlp`).
`object`	An object saved from `rlp()` or `emp()` (i.e., of class `rlp`) for the `anova`, `coef`, and `summary` functions..
`what`	A string that indicates the type of plot to produce. See details.
`col.pop`	A string that indicates the type of color or palette to use for the population of length-weight regression lines. See details.
`order.pop`	A logical that indicates whether the populations should be plotted from the smallest to largest weight in the initial length category. See details.
`lwd.pop`	A numeric that indicates the width of the line to use for the population of length-weight regression lines.
`lty.pop`	A numeric that indicates the type of line to use for the population of length-weight regression lines.
`col.Ws`	A string that indicates the type of color to use for the standard length-weight regression line.
`lwd.Ws`	A numeric that indicates the width of the line to use for the standard length-weight regression line.
`lty.Ws`	A numeric that indicates the type of line to use for the standard length-weight regression line.
`pch`	A single numeric that indicates what plotting characther codes should be used for the points in the fitPlot.
`col.pt`	A string used to indicate the color of the plotted points.
`xlab`	A label for the x-axis of fitPlot.
`ylab`	A label for the y-axis of fitPlot.
`main`	A label for the main title of fitPlot.
`...`	Additional arguments for methods.

The main function follows the steps of the regression-line-percentile method detailed in Murphy et al. (1990). In summary, a predicted weight is constructed for each 1-cm length class from each population from the given log_{10}(a) and b values, the predicted weight at the probth percentile (wq) is identified, and a linear regression equation is fit to the log_{10}(wq) and log_{10}(midpoint length) data.

Note that log_{10}(a) and b must be from the regression of log_{10}(W) on log_{10}(L) where W is measured in grams and L is the total length measured in mm.

It appears that Murphy et al. (1990) used qtype=6 in their SAS program. Types of quantile calculation methods are discussed in the details of of quantile.

The plot, coef, and summary methods are used to construct a plot (see below), extract the coefficients of the standard weight equation, and find summary results of the lm object returned by the main function. The what argument in the plot method can be set to "both", "log", or "raw". The "raw" plot plots lines on the length-weight scale for each population represented in the log.a and b vectors with the resultant standard weight equation superimposed in red. The "log" plot constructs a similar plot but on the log_{10}(weight)-log_{10}(length) scale. The "both" option produces both plots side-by-side.

If the col.pop argument is set equal to one of these palettes – “rich”, “cm”, “default”, “grey”, “gray”, “heat”, “jet”, “rainbow”, “topo”, or “terrain” – and the order.pop=TRUE then the populations plotted should form a general color gradient from smallest to largest weight in the initial length category. This will make it easier to identify populations that “cross over” other populations.

A list is returned with the following items:

log.a is a numeric vector of the observed log_{10}(a) values sent in the log.a argument.
b is a numeric vector of the observed b values sent in the b argument.
data.pred is a matrix of the predicted weight at length for all populations.
data.reg contains a data frame with the probth quartile of predicted weights and the midpoint lengths.
Ws is an lm object that contains the results of the regression of log_{10}(wq) on log_{10}(midpoint length).

Derek H. Ogle, [email protected]

Murphy, B.R., M.L. Brown, and T.A. Springer. 1990. Evaluation of the relative weight (Wr) index, with new applications to walleye. North American Journal of Fisheries Management, 10:85-97.

emp, FroeseWs, and wsValidate; and quantile in stats

## Recreate Murphy et al. (1990) results for largemouth bass
# min and max lengths were 152 and 816
# compare to log.a=-5.379 and b=3.221
data(LMBassWs)
lmb.rlp <- rlp(LMBassWs$log.a,LMBassWs$b,155,815,qtype=6)
coef(lmb.rlp)
plot(lmb.rlp)
#fitPlot(lmb.rlp)
#residPlot(lmb.rlp)