Prediction data frame for nls
Get predictions with standard errors into data frame for use with geom_smooth
## S3 method for class 'nls' predictdf(model, xseq, se, level)
model |
nls object |
xseq |
newdata |
se |
Display confidence interval around smooth? |
level |
Level of confidence interval to use |
ggplot2::geom_smooth produces confidence intervals by silently calling functions
of the form predictdf.method, where method is "loess", "lm", "glm" etc.
depending on what method is specified in the call to geom_smooth.
Currently ggplot2 does not define a predictdf.nls function for method of type "nls",
and thus confidence intervals cannot be automatically generated by geom_smooth
for method = "nls". Here we define predictdf.nls for calculating the confidence
intervals of an object of type nls. geom_smooth will silently call this function
whenever method = "nls", and produce the appropriate confidence intervals.
predictdf.nls calculates CI for a model fit of class nls based on the "delta-method"
http://sia.webpopix.org/nonlinearRegression.html#confidence-intervals-and-prediction-intervals)
CI = [ f(x0, beta) + qt_(alpha/2, n - d) * se(f(x0, beta)), f(x0, beta) + qt_(1 - alpha/2, n - d) * se(f(x0, beta))]
where: beta = vector of parameter estimates x = independent variable se(f(x0, beta)) = sqrt( delta(f)(x0, beta) * Var(beta) * (delta(f)(x0, beta))' ) delta(f) is the gradient of f
dataframe with x and y values, if se is TRUE dataframe also includes ymin and ymax
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