Ken Ervin wrote:
I have a data set of 6 or so ordered pairs, and I've been able to graph
them and have decided to use a high-order polynomial regression. I've
used the following piece of code:
regression <- function(x,y) {
x <- c(insert_numbers_here)
y <- c(insert_other_numbers_here)
fit <- lm(y ~ x + I(x^2) + I(x^3) + I(x^4) + I(x^5) + I(x^6) + I(x^7)
+ I(x^8) + I(x^9))
summary(fit)
This gives me the coefficients for the regression very nicely, but I
would like to plot both the data and the regression curve together. How
do I plot that regression curve as a function, and can I put it on the
same set of axes as my data scatter plot?
Are you sure that fitting such a high-degree polynomial makes
sense? Is there any theory to support the model? If you really
want to do this, then use predict.lm():
## with xmin, xmax as the limits of your scatterplot
xx <- seq(xmin, xmax, length=51)
yy <- predict(fit, newdata=list(x=xx))
lines(xx, yy) ## add to scatterplot
-Peter Ehlers
Thanks in advance for your help!
-KE
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