RuffeWs: Raw length-weight data that can be used to compute the Ws for...
In droglenc/FSAWs: Construct and Validate Standard Weight (Ws) Equations for Fish

RuffeWs

R Documentation

Raw length-weight data that can be used to compute the Ws for Ruffe.

Description

Raw length-weight data from a variety of populations used for computing the standard weight (Ws) equation for Ruffe (Gymnocephalus cernuus). Data are from Ogle and Winfield (2009).

Format

A data frame with 20005 observations on the following 13 variables:

regrnum: A unique numeric identifier for each separate regression – should match one-to-one with locShort.
fishID: A numeric identifier for each fish that is not unique.
country: Country of data set.
locShort: Name (and possibly year) of data set.
watertype: Waterbody type descriptor.
year: Year of collection.
month: Month of collection.
day: Day of collection.
tl: Total length (mm) of fish.
fl: Fork length (mm) of fish.
sl: Standard length (mm) of fish.
wt: Weight (g) of fish.
use: Use of data set – either develop or validate.

Source

From Ogle, D.H. and I.J. Winfield. 2009. Ruffe length-weight relationships with a proposed standard weight equation. North American Journal of Fisheries Management 29:850-85.

Examples

## Create log10 TL and Wt
RuffeWs$logtl <- log10(RuffeWs$tl)
RuffeWs$logwt <- log10(RuffeWs$wt)

## Isolate development and validation data sets
rWs.d <- droplevels(subset(RuffeWs,use=="develop"))
str(rWs.d)
rWs.v <- droplevels(subset(RuffeWs,use=="validate"))
str(rWs.v)

## Loop through all regressions (for use with rlp())
### First make a function that performs a regression on one regrnum
###   and creates a data.frame of desired results
indivreg <- function(d,alpha=0.05) {
  tmp.lm <- lm(logwt~logtl,data=d)
  tmp.smry <- summary(tmp.lm)
  tmp.cf <- tmp.smry$coefficients[,"Estimate"]
  tmp.bt3 <- abs((tmp.cf[[2]]-3)/tmp.smry$coefficients["logtl","Std. Error"])
  tmp.bp3 <- 2*stats::pt(tmp.bt3,df=tmp.smry$df[2],lower.tail=FALSE)
  data.frame(regrnum=unique(d$regrnum),reg.lbls=unique(d$loc),
             minTL=min(d$tl,na.rm=TRUE),maxTL=max(d$tl,na.rm=TRUE),
             minWT=min(d$wt,na.rm=TRUE),maxWT=max(d$wt,na.rm=TRUE),
             n=dim(tmp.lm$model)[1],r2=tmp.smry$r.squared,
             loga=tmp.cf[[1]],b=tmp.cf[[2]],sigb3=tmp.bp3<alpha)
}

### Second, split the data frame into a list where each item is one regrnum
tmp <- split(rWs.d,as.factor(rWs.d$regrnum))
### Third, apply the indivreg function to each item in the list
reg.d <- as.data.frame(do.call(rbind,lapply(tmp,FUN=indivreg)))
### View (partially) the results
head(reg.d)


## Ruffe Ws equation from Ogle and Winfield
# Summarize TL to set min and max to emp()
summary(rWs.d$tl)
# Find the EmP Ws equation
emp.res.d <- emp(rWs.d,pop="regrnum",len="tl",wt="wt",min=60,max=220,w=10,
  n.cutoff=4,cutoff.tail=TRUE)
# EmP model results
anova(emp.res.d)
summary(emp.res.d)
# Fitted model and residual plots
fitPlot(emp.res.d)
plot(emp.res.d$Ws$residuals~emp.res.d$Ws$fitted.values,pch=19)
abline(h=0,lty=3)
# Sample sizes
emp.res.d$pop.by.len
emp.res.d$ind.by.len
# Fitted model relative to mean weights by length categories
plot(emp.res.d)

# Validate (explore length-related biases)
# Perform validation calculations -- Willis method
emp.v.w <- wsValidate(emp.res.d,rWs.v,"regrnum","tl","wt",min=60,max=210,w=10,type="Willis")
# Observed validation results
summary(emp.v.w)
emp.v.w
# See individual regressions -- NOT RUN

# Perform validation calculations -- weighted EmpQ method
emp.v.q <- wsValidate(emp.res.d,rWs.v,"regrnum","tl","wt",min=60,max=220,w=10,
  type="EmpQ",weighted=TRUE)
anova(emp.v.q)
fitPlot(emp.v.q)


## Fit the RLP model
rlp.res.d <- rlp(reg.d$loga,reg.d$b,min=60,max=210,w=10)
# RLP model results
summary(rlp.res.d)
# Fitted model and residual plot
fitPlot(rlp.res.d)
plot(rlp.res.d$Ws$residuals~rlp.res.d$Ws$fitted.values,pch=19)
abline(h=0,lty=3)
# Show rlp model relative to all regression lines
plot(rlp.res.d)

# Validate (explore length-related biases)
# Perform validation calculations -- Willis method
rlp.v.w <- wsValidate(rlp.res.d,rWs.v,"regrnum","tl","wt",min=60,max=210,w=10,type="Willis")
summary(rlp.v.w)
rlp.v.w


# Perform validation calculations -- weighted EmpQ method
rlp.v.q <- wsValidate(rlp.res.d,rWs.v,"regrnum","tl","wt",min=60,max=210,w=10,
  type="EmpQ",weighted=TRUE)
anova(rlp.v.q)
fitPlot(rlp.v.q)


## "Fit" the Froese model
Froese.res.d <- FroeseWs(reg.d$loga,reg.d$b)
# Observe results
coef(Froese.res.d)
plot(Froese.res.d,min=55,max=200)

# Validate (explore length-related biases)
# Perform validation calculations -- Willis method
Froese.v.w <- wsValidate(Froese.res.d,rWs.v,"regrnum","tl","wt",min=60,max=190,w=10,type="Willis")
summary(Froese.v.w)
Froese.v.w

# Perform validation calculations -- EmpQ method
Froese.v.q <- wsValidate(Froese.res.d,rWs.v,"regrnum","tl","wt",min=60,max=190,
  w=10,type="EmpQ",use.means=TRUE,weighted=TRUE)
anova(Froese.v.q)
fitPlot(Froese.v.q,ylab="Standarded Mean Mean Weight")

droglenc/FSAWs documentation built on Feb. 3, 2023, 8:48 a.m.