Calculates a local polynomial regression fit with associated confidence intervals
loess.ci(y, x, p = 0.95, plot = FALSE, ...)
Arguments
| y | Dependent variable, vector |
|---|---|
| x | Independent variable, vector |
| p | Percent confidence intervals (default is 0.95) |
| plot | Plot the fit and confidence intervals |
| ... | Arguments passed to loess |
Value
A list object with:
loess Predicted values
se Estimated standard error for each predicted value
lci Lower confidence interval
uci Upper confidence interval
df Estimated degrees of freedom
rs Residual scale of residuals used in computing the standard errors
References
W. S. Cleveland, E. Grosse and W. M. Shyu (1992) Local regression models. Chapter 8 of Statistical Models in S eds J.M. Chambers and T.J. Hastie, Wadsworth & Brooks/Cole.
Author
Jeffrey S. Evans jeffrey_evans@tnc.org
Examples
x <- seq(-20, 20, 0.1) y <- sin(x)/x + rnorm(length(x), sd=0.03) p <- which(y == "NaN") y <- y[-p] x <- x[-p] opar <- par(no.readonly=TRUE) par(mfrow=c(2,2)) lci <- loess.ci(y, x, plot=TRUE, span=0.10) lci <- loess.ci(y, x, plot=TRUE, span=0.30) lci <- loess.ci(y, x, plot=TRUE, span=0.50) lci <- loess.ci(y, x, plot=TRUE, span=0.80)
