nlsTracePlot {FSA} | R Documentation |

Adds model fits from iterations of the `nls`

algorithm as returned when `trace=TRUE`

. Useful for diagnosing model fitting problems or issues associated with starting values.

nlsTracePlot(object, fun, from = NULL, to = NULL, n = 199, lwd = 2, pal = paletteChoices(), rev.col = FALSE, legend = "topright", cex.leg = 0.9, box.lty.leg = 0, add = TRUE)

`object` |
An object saved from |

`fun` |
A function that represents the model being fit in |

`from, to` |
The range over which the function will be plotted. Defaults to range of the x-axis of the active plot. |

`n` |
The number of value at which to evaluate the function for plotting (i.e., the number of values from |

`lwd` |
A numeric used to indicate the line width of the fitted line. |

`pal` |
A character that is the name of a palette. Must be one of “rich”, “cm”, “default”, “grey”, “gray”, “heat”, “jet”, “rainbow”, “topo”, or “terrain”, which are given in |

`rev.col` |
A logical that indicates that the order of colors for plotting the lines should be reversed. |

`legend` |
Controls use and placement of the legend. See details. |

`cex.leg` |
A single numeric value that represents the character expansion value for the legend. Ignored if |

`box.lty.leg` |
A single numeric values that indicates the type of line to use for the box around the legend. The default is to not plot a box. |

`add` |
A logical indicating whether the lines should be added to the existing plot (defaults to |

Nonlinear models fit with the `nls`

function start with starting values for model parameters and iteratively search for other model parameters that continuously reduce the residual sum-of-squares (RSS) until some pre-determined criterion suggest that the RSS cannot be (substantially) further reduced. With good starting values and well-behaved data, the minimum RSS may be found in a few (<10) iterations. However, poor starting values or poorly behaved data may lead to a prolonged and possibly failed search. An understanding of the iterations in a prolonged or failed search may help identify the failure and lead to choices that may result in a successful search. The `trace=TRUE`

argument of `nls`

allows one to see the values at each iterative step. The function documented here plots the “trace” results at each iteration on a previously existing plot of the data. This creates a visual of the iterative process.

The `object`

argument may be an object saved from a successful run of `nls`

. See the examples with `SpotVA1`

and `CodNorwegion`

.

However, if `nls`

fails to converge to a solution then no useful object is returned. In this case, `trace=TRUE`

must be added to the failed `nls`

call. The call is then wrapped in `try`

to work-around the failed convergence error. This is also wrapped in `capture.output`

to capture the “trace” results. This is then saved to an object that which can then be the `object`

of the function documented here. This process is illustrated with the example using `BSkateGB`

.

The function in `fun`

is used to make predictions given the model parameter values at each step of the iteration. This function must accept the explanatory/independent variable as its first argument and values for all model parameters in a vector as its second argument. These types of functions are returned by `vbFuns`

, `GompertzFuns`

, `logisticFuns`

, and `RichardsFuns`

for common growth models and `srFuns`

for common stock-recruitment models. See the examples.

A matrix with the residual sum-of-squares in the first column and parameter estimates in the remaining columns for each iteration (rows) of `nls`

as provided when `trace=TRUE`

.

The position of the “legend” can be controlled in three ways. First, if `legend=TRUE`

, then the R console is suspended until the user places the legend on the plot by clicking on the point where the upper-left corner of the legend should appear. Second, `legend=`

can be set to one of `"bottomright"`

, `"bottom"`

, `"bottomleft"`

, `"left"`

, `"topleft"`

, `"top"`

, `"topright"`

, `"right"`

and `"center"`

. In this case, the legend will be placed inside the plot frame at the given location. Finally, `legend=`

can be set to a vector of length two which identifies the plot coordinates for the upper-left corner of where the legend should be placed. A legend will not be drawn if `legend=FALSE`

or `legend=NULL`

.

Derek H. Ogle, derek@derekogle.com

## Examples following a successful fit vb1 <- vbFuns() fit1 <- nls(tl~vb1(age,Linf,K,t0),data=SpotVA1,start=list(Linf=12,K=0.3,t0=0)) plot(tl~age,data=SpotVA1,pch=21,bg="gray40") nlsTracePlot(fit1,vb1,legend="bottomright") r1 <- srFuns("Ricker") fitSR1 <- nls(log(recruits)~log(r1(stock,a,b)),data=CodNorwegian,start=list(a=3,b=0.03)) plot(recruits~stock,data=CodNorwegian,pch=21,bg="gray40",xlim=c(0,200)) nlsTracePlot(fitSR1,r1) # no plot, but returns trace results as a matrix ( tmp <- nlsTracePlot(fitSR1,r1,add=FALSE) ) ## Not run: if (require(FSAdata)) { data(BSkateGB,package="FSAdata") wtr <- filterD(BSkateGB,season=="winter") bh1 <- srFuns() trc <- capture.output(try( fitSR1 <- nls(recruits~bh1(spawners,a,b),wtr, start=srStarts(recruits~spawners,data=wtr),trace=TRUE) )) plot(recruits~spawners,data=wtr,pch=21,bg="gray40") nlsTracePlot(trc,bh1) # zoom in on y-axis plot(recruits~spawners,data=wtr,pch=21,bg="gray40",ylim=c(0.02,0.05)) nlsTracePlot(trc,bh1,legend="top") # return just the trace results ( tmp <- nlsTracePlot(trc,bh1,add=FALSE) ) } ## End(Not run)

[Package *FSA* version 0.8.25.9000 Index]