FSA (version 0.8.20)

lwCompPreds: Constructs plots of predicted weights at given lengths among different groups.

Description

Constructs plots of predicted weights at given lengths among different groups. These plots allow the user to explore differences in predicted weights at a variety of lengths when the weight-length relationship is not the same across a variety of groups.

Usage

lwCompPreds(object, lens = NULL, qlens = c(0.05, 0.25, 0.5, 0.75, 0.95),
  qlens.dec = 1, base = exp(1), interval = c("confidence", "prediction",
  "both"), center.value = 0, lwd = 1, connect.preds = TRUE,
  show.preds = FALSE, col.connect = "gray50", ylim = NULL,
  main.pre = "Length==", cex.main = 0.8, xlab = "Groups",
  ylab = "Predicted Weight", yaxs = "r", rows = round(sqrt(num)),
  cols = ceiling(sqrt(num)))

Arguments

object

An lm object (i.e., returned from fitting a model with lm). This model should have log(weight) as the response and log(length) as the explanatory covariate and an explanatory factor variable that describes the different groups.

lens

A numeric vector that indicates the lengths at which the weights should be predicted.

qlens

A numeric vector that indicates the quantiles of lengths at which weights should be predicted. This is ignored if lens is non-null.

qlens.dec

A single numeric that identifies the decimal place that the lengths derived from qlens should be rounded to (Default is 1).

base

A single positive numeric value that indicates the base of the logarithm used in the lm object in object. The default is exp(1), or the value e.

interval

A single string that indicates whether to plot confidence (="confidence"), prediction (="prediction"), or both (="both") intervals.

center.value

A single numeric value that indicates the log length used if the log length data was centered when constructing object.

lwd

A single numeric that indicates the line width to be used for the confidence and prediction interval lines (if not interval="both") and the prediction connections line. If interval="both" then the width of the prediction interval will be one less than this value so that the CI and PI appear different.

connect.preds

A logical that indicates whether the predicted values should be connected with a line across groups or not.

show.preds

A logical that indicates whether the predicted values should be plotted with a point for each group or not.

col.connect

A color to use for the line that connects the predicted values (if connect.preds=TRUE).

ylim

A numeric vector of length two that indicates the limits of the y-axis to be used for each plot. If null then limits will be chosen for each graph individually.

main.pre

A character string to be used as a prefix for the main title. See details.

cex.main

A numeric value for the character expansion of the main title. See details.

xlab

A single string for labeling the x-axis.

ylab

A single string for labeling the y-axis.

yaxs

A single string that indicates how the y-axis is formed. See par for more details.

rows

A single numeric that contains the number of rows to use on the graphic.

cols

A single numeric that contains the number of columns to use on the graphic.

…

Other arguments to pass through to the plot function.

Value

None. However, a plot is produced.

IFAR Chapter

7-Weight-Length.

References

Ogle, D.H. 2016. Introductory Fisheries Analyses with R. Chapman & Hall/CRC, Boca Raton, FL.

Examples

# NOT RUN {
# load weight-length data
data(ChinookArg)
# add log length and weight data
ChinookArg$logtl <- log(ChinookArg$tl)
ChinookArg$logwt <- log(ChinookArg$w)
# fit model to assess equality of slopes
lm1 <- lm(logwt~logtl*loc,data=ChinookArg)
anova(lm1)

# set par so that the plots will look decent
par(mar=c(3.5,3.5,1,1),mgp=c(1.8,0.4,0),tcl=-0.2)
# show predicted weights (w/ CI) at the default quantile lengths for each year
lwCompPreds(lm1,xlab="Location")
# show predicted weights (w/ CI) at the quartile lengths for each year
lwCompPreds(lm1,xlab="Location",qlens=c(0.25,0.5,0.75))
# show predicted weights (w/ CI) at certain lengths for each year
lwCompPreds(lm1,xlab="Location",lens=c(60,90,120,150))
# show predicted weights (w/ just PI) at certain lengths for each year
lwCompPreds(lm1,xlab="Location",lens=c(60,90,120,150),interval="prediction")
# show predicted weights (w/ CI and PI) at certain lengths for each year
lwCompPreds(lm1,xlab="Location",lens=c(60,90,120,150),interval="both")
# show predicted weights (w/ CI and points at the prediction) at certain lengths for each year
lwCompPreds(lm1,xlab="Location",lens=c(60,90,120,150),show.preds=TRUE)
# show predicted weights (w/ CI but don't connect means) at certain lengths for each year
lwCompPreds(lm1,xlab="Location",lens=c(60,90,120,150),connect.preds=FALSE,show.preds=TRUE)

# fit model with centered data
mn.logtl <- mean(ChinookArg$logtl,na.rm=TRUE)
ChinookArg$clogtl <- ChinookArg$logtl-mn.logtl
lm2 <- lm(logwt~clogtl*loc,data=ChinookArg)
lwCompPreds(lm2,xlab="Location",center.value=mn.logtl)
lwCompPreds(lm2,xlab="Location",lens=c(60,90,120,150),center.value=mn.logtl)

# fit model with a different base (plot should be the same as the first example)
ChinookArg$logtl <- log10(ChinookArg$tl)
ChinookArg$logwt <- log10(ChinookArg$w)
lm1 <- lm(logwt~logtl*loc,data=ChinookArg)
lwCompPreds(lm1,base=10,xlab="Location")

if (interactive()) {
  # should give error, does not work for only a simple linear regression
  lm2 <- lm(logwt~logtl,data=ChinookArg)
  lwCompPreds(lm2)
  # or a one-way ANOVA
  lm3 <- lm(logwt~loc,data=ChinookArg)
  lwCompPreds(lm3)   
}

# }