Hi,
Is it just me or it appears the "temperature" and "probability" should be
reversed?
Anyhow it should help you to assign your model to a variable (as Joshua did
with his own suggestion)
yourmodel <-lm(x[,2] ~ x[,1] + I(x[,1]^2)) # again, taking literally the way
you formulated it...
Hi Josh,
This is by no means the fanciest solution ever, but as there are
predict methods for many types of models in R, I thought I would show
it this way.
## fit the model
model <- lm(probability ~ poly(temperature, 2), data = x)
## create line values
dat <- data.frame(temperature = seq(min(x$
Dear Listserv,
Here is my latest in a series of simple-seeming questions that dog me.
Consider the following data:
x <- read.table(textConnection("temperature probability
0.11 9.4
0 2.3
0.38 8.7
0.43 9.2
0.6 15.6
0.47 8.7
0.09 12.8
0.11 9.4
0.01 7.7
0.83 8
0.65 9.3
0.05 7.4
0.34 10.1
0.02 4.8
0.
Hi Ryan,
See the example in ?predict.lm . I think it would be useful in this case.
HTH,
Jorge
On Wed, Dec 31, 1969 at 8:16 PM, Ryan Harrigan <[EMAIL PROTECTED]> wrote:
> I have an equation describing the best-fit model for a set of points (just
> 2
> axes) that is in the form:
>
> y=b+mx+px^2
I have an equation describing the best-fit model for a set of points (just 2
axes) that is in the form:
y=b+mx+px^2
Where b is the intercept, m is the slope describing a linear term, and p is
a slope of the quadratic term.
I would like to plot this equation on a curve (I know the equation is
y=(
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