about fitting a regression line

d <- data.frame(x=1:7, y=c(7,4,2,3,6,8,7))
fit <- lm( y~cos(x), data=d)
coef(fit)

sfit <- summary(fit)
sfit$r.squared

with(sfit, pf(fstatistic[1], fstatistic[2], fstatistic[3], lower=FALSE))

Thanks for your replies. I tried the regression, but then got a NA value
for the slope. And here is the error message:
Coefficients: (1 not defined because of singularities)


On Thu, Jun 15, 2017 at 12:20 AM, PIKAL Petr <petr.pikal at precheza.cz>
wrote:

Hi

But X can be some function like - sin, cos, log, exp...

Cheers
Petr

-----Original Message-----
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of lily

li

Sent: Thursday, June 15, 2017 1:28 AM
To: MacQueen, Don <macqueen1 at llnl.gov>
Cc: R mailing list <r-help at r-project.org>
Subject: Re: [R] about fitting a regression line

Thanks. I thought lm() function is for linear model, such as the

correlation

below:
Y= aX + b

On Wed, Jun 14, 2017 at 5:25 PM, MacQueen, Don <macqueen1 at llnl.gov>
wrote:

Start with the lm() function; i.e., see

  ?lm

-Don

--
Don MacQueen

Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062


On 6/14/17, 3:40 PM, "R-help on behalf of lily li" <
r-help-bounces at r-project.org on behalf of chocold12 at gmail.com>

wrote:

    Hi R users,

    I have some data points (Xi, Yi), and they may follow such a
pattern Yi =
    cCOS(Xi) + d, how to find the c and d in R? which function to

use?

Also,
    how to get the R2 and p value for this correlation? Thanks for

any

kind of
    help.

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see

code.

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