I've just realised the couple of graphs I put on here have been stripped
off. If anyone has to see them and can't see my problem from code, I can
send them directly to anyone who thinks they can help but wants to see them.
Thanks,
Ben W.
On 18/02/2011 23:29, Ben Ward wrote:
Hi, I wonder if anyone could advise me with this:
I've been trying to make a standard curve in R with lm() of some
standards from a spectrophotometer, so as I can express the curve as a
formula, and so obtain values from my treated samples by plugging in
readings into the formula, instead of trying to judge things by eye,
with a curve drawn by hand.
It is a curve and so I used the following formula:
model <- lm(Approximate.Counts~X..Light.Transmission +
I(Approximate.Counts^2), data=Standards)
It gives me a pretty decent graph:
xyplot(Approximate.Counts + fitted(model) ~ X..Light.Transmission,
data=Standards)
I'm pretty happy with it, and looking at the model summary, to my
inexperienced eyes it seems pretty good:
lm(formula = Approximate.Counts ~ X..Light.Transmission +
I(Approximate.Counts^2),
data = Standards)
Residuals:
Min 1Q Median 3Q Max
-91.75 -51.04 27.33 37.28 49.72
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.868e+02 2.614e+01 37.75 <2e-16 ***
X..Light.Transmission -1.539e+01 8.116e-01 -18.96 <2e-16 ***
I(Approximate.Counts^2) 2.580e-04 6.182e-06 41.73 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 48.06 on 37 degrees of freedom
Multiple R-squared: 0.9956, Adjusted R-squared: 0.9954
F-statistic: 4190 on 2 and 37 DF, p-value: < 2.2e-16
I tried to put some 95% confidence interval lines on a plot, as
advised by my tutor, to see how they looked, and I used a function I
found in "The R Book":
se.lines <- function(model){
b1<-coef(model)[2]+ summary(model)[[4]][4]
b2<-coef(model)[2]- summary(model)[[4]][4]
xm<-mean(model[[12]][2])
ym<-mean(model[[12]][1])
a1<-ym-b1*xm
a2<-ym-b2*xm
abline(a1,b1,lty=2)
abline(a2,b2,lty=2)
}
se.lines(model)
but when I do this on a plot I get an odd result:
They looks to me, to lie in the same kind of area, that my regression
line did, before I used polynomial regression, by squaring
"Approximate.Counts":
lm(formula = Approximate.Counts ~ X..Light.Transmission +
I(Approximate.Counts^2), data = Standards)
Is there something else I should be doing? I've seen several ways of
dealing with non-linear relationships, from log's of certain
variables, and quadratic regression, and using sin and other
mathematical devices. I'm not completely sure if I'm "allowed" to
square the y variable, the book only squared the x variable in
quadratic regression, which I did first, and it fit quite well, but
not as good squaring Approximate Counts does:
model <- lm(Approximate.Counts~X..Light.Transmission +
I(X..Light.Transmission^2), data=Standards)
Any advice is greatly appreciated, it's the first time I've really had
to look at regression with data in my coursework that isn't a straight
line.
Thanks,
Ben Ward.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
