lwCompPreds {FSA} | R Documentation |

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.

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)))

`object` |
An |

`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 |

`qlens.dec` |
A single numeric that identifies the decimal place that the lengths derived from |

`base` |
A single positive numeric value that indicates the base of the logarithm used in the |

`interval` |
A single string that indicates whether to plot confidence ( |

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

`lwd` |
A single numeric that indicates the line width to be used for the confidence and prediction interval lines (if not |

`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 |

`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 |

`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 |

None. However, a plot is produced.

7-Weight-Length.

Derek H. Ogle, derek@derekogle.com

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

# add log length and weight data to ChinookArg 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 graphing parameters so that the plots will look decent op <- 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) } ## return graphing parameters to original state par(op)

[Package *FSA* version 0.8.25.9000 Index]