Description Usage Arguments Value Examples
Convenient function for adding curves to an existing plot, or to plot the data with the fitted curve.
For non-linear regression plotting (plot_nls), works for simple non-linear regression models fit with nls, grouped non-linear regression (with nlsList), and non-linear quantile regression fit with nlrq from the quantreg package.
When plotting an nlsList object, plot_nls plots the fitted curve for each of the groups specified in the model, and sets colours with lines.col and points.col arguments.
For local regression models fitted with loess, use the plot_loess function, which additionally adds a confidence interval around the fitted curve (unless band=FALSE).
1 2 3 4 5 6 |
object |
The object returned by |
col |
Colour to be used for the data symbols and the fitted line, unless |
band |
For |
plotdata |
Logical. Whether to add the data points to the plot. |
extrapolate |
Logical (default FALSE). If TRUE, extends the fitted curve beyond the data, based on the x-axis limits specified. |
lines.col |
Colour(s) for the fitted lines. When plotting a |
points.col |
Colour(s) for the data symbols. When plotting a |
ci.col |
Colour of the confidence band, if plotted. Defaults to a transparent grey colour. |
lwd |
Thickness of the line (see |
lty |
Line type (see |
add |
Logical. Whether to add to current plot (default FALSE). |
xlab |
Label for x-axis |
ylab |
Label for y-axis |
coverage |
If confidence band to be plotted, the coverage (e.g. for 95% confidence interval, use 0.95) |
xlim |
X-axis limits (optional). |
... |
Further arguments passed to |
Returns the predicted values used in plotting (invisibly), as a dataframe with columns 'predvar' (regularly spaced predictor values), and 'fit' (fitted values). For plot_loess also returns confidence intervals, standard error, and df of the residual.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # Plot an nls object
chick <- as.data.frame(ChickWeight)
fit0 <- nls(weight ~ a*Time^b, data=chick, start=list(a=10, b=1.1))
plot_nls(fit0)
# Plot a grouped nls object
library(nlme)
fit1 <- nlsList(weight ~ a*Time^b|Diet, data=chick, start=list(a=10, b=1.1))
plot_nls(fit1)
# Plot a local regression object, with confidence interval
l <- loess(wt ~ disp, data=mtcars)
plot_loess(l)
# To plot behind the data:
with(mtcars, plot(disp, wt, pch=19,
panel.first=plot_loess(l, plotdata=FALSE)))
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