Fitplot r9 how to#

The y- and x-axes are labelled ( name=), expansion factor for the axis limits is removed ( expand=c(0,0)) so that the point (0,0) is in the corner of the plot, and the axis limits ( limits=) and breaks ( breaks=) are controlled using scale_y_continuous() and scale_x_continuous(). ggplot () + geom_ribbon ( data = preds2, aes ( x = age, ymin = LCI, ymax = UCI ), fill = 'gray90' ) + geom_point ( data = wf14T, aes ( y = tl, x = age ), size = 2, alpha = 0.1 ) + geom_line ( data = preds1, aes ( y = fit, x = age ), size = 1, linetype = 2 ) + geom_line ( data = preds2, aes ( y = fit, x = age ), size = 1 ) The fitted curve for just the observed range of ages (i.e., using preds2) is added using a solid line so that the dashed line for the observed ages is covered. ggplot () + geom_ribbon ( data = preds2, aes ( x = age, ymin = LCI, ymax = UCI ), fill = 'gray90' ) + geom_point ( data = wf14T, aes ( y = tl, x = age ), size = 2, alpha = 0.1 ) + geom_line ( data = preds1, aes ( y = fit, x = age ), size = 1, linetype = 2 ) A slightly thicker than default ( size=) dashed ( linetype=) line was used. The fitted curve over the entire range of ages used above (i.e., using preds1) is added with geom_line(). ggplot () + geom_ribbon ( data = preds2, aes ( x = age, ymin = LCI, ymax = UCI ), fill = 'gray90' ) + geom_point ( data = wf14T, aes ( y = tl, x = age ), size = 2, alpha = 0.1 ) The points are slightly larger than the default (with size=) and also with a fairly low transparency value to handle considerable over-plotting. Observed lengths and ages in the wf14T ame were then added to this plot with geom_point(). 3 ggplot () + geom_ribbon ( data = preds2, aes ( x = age, ymin = LCI, ymax = UCI ), fill = 'gray90' )

The fill color of the polygon is set with fill=. This polygon is constructed with geom_ribbon() using preds2 (the confidence polygon will only cover observed ages) where the x-axis will be age and the minimum part of the y-axis will be LCI and the maximum part of the y-axis will be UCI. The plot begins with a polygon that encases the lower and upper confidence interval values for mean length at each age. Thus, the data and aesthetics will be set within specific geoms. However, the data and aesthetics should not be set in ggplot in this application because information will be drawn from three ames – wf14T, preds, and preds2. ( f.starts = agesum $ minage, age <= agesum $ maxage ) headtail ( preds2 ) # age fit LCI UCIĪ ggplot2 often starts by defining data= and aes()thetic mappings in ggplot(). Reasonable starting values for the optimization algorithm may be obtained with vbStarts(), where the first argument is a formula of the form lengths~ages where lengths and ages are replaced with the actual variable names containing the observed lengths and ages, respectively, and data= is set to the ame containing those variables. Briefly, a function for the typical VBGF is constructed with vbFuns() 1. Methods for fitting a von Bertalannfy growth function (VBGF) are detailed in my Introductory Fisheries Analyses with R book and in Chapter 12 of Age and Growth of Fishes: Principles and Techniques book.

data ( WalleyeErie2 ) wf14T % summarize ( minage = min ( age ), maxage = max ( age )) agesum # A tibble: 1 x 3įitting a von Bertalanffy Growth Function I focus on female Walleye from location “1” captured in 2014 in this example. My primary interest here is in the tl (total length in mm) and age variables ( see here for more details about the data). These data are available in my FSAdata package and formed many of the examples in Chapter 12 of the Age and Growth of Fishes: Principles and Techniques book.

Fitplot r9 how to#

Here I demonstrate how to produce such plots with lengths and ages of Lake Erie Walleye ( Sander vitreus) captured during October-November, 2003-2014. A fitted curve (without a confidence polygon) over a larger range than the observed ages (this often helps identify problematic fits).A fitted curve with a confidence polygon over the range of observed ages.Transparent points to address over-plotting of fish with the same length and age.I often make a plot to illustrate the fit of a von Bertalanffy growth function to data. I am continuing to learn ggplot2 for elegant graphics. Introduction library ( FSAdata ) # for data library ( FSA ) # for vbFuns(), vbStarts(), confint.bootCase() library ( car ) # for Boot() library ( dplyr ) # for filter(), mutate() library ( ggplot2 )