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about fitting a regression line

10 messages · MacQueen, Don, Rolf Turner, PIKAL Petr +4 more

lily li
# 
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.
MacQueen, Don
# 
Start with the lm() function; i.e., see

  ?lm

-Don
lily li
# 
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:

            

      
      
      
    

        

      
      
    

        

  
  


  


      

    

    

      
      

        


  

        

      Rolf Turner
    

    

      
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On 15/06/17 10:40, lily li wrote:

            

      
      
      
    

        
If I understand you correctly (always a dubious conditional):

     fit <- lm(y ~ cos(x))

where y and x are vectors of the y and x values of your data points, 
should answer all of your questions.

I am bewildered that you need to ask.  You have been posting to this 
list for quite a while.  Have you learned nothing about R?  It's time 
that you did.  Read and study carefully "An Introduction to R" from the 
R web site -> Manuals.

cheers,

Rolf Turner

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Rolf Turner
    

    

      
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On 15/06/17 11:28, lily li wrote:

            

      
      
      
    

        
And you don't think that Y = a*cos(X) + b is a linear model?

The mind boggles.

Well, I guess there is a "subtlety" here.  Linear models are *linear in 
the PARAMETERS of the model*, not in the predictors.

First-year knuckle-draggers get confused about this.  People who are 
using R to do research should be a bit beyond such confusion.

cheers,

Rolf Turner

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      PIKAL Petr
    

    

      
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Hi

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

Cheers
Petr

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

        
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      lily li
    

    

      
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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:

        

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

        

  
  


  


      

    

    

      
      

        


  

        

      Don McKenzie
    

    

      
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Did you perhaps accidentally include your response as a predictor?

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

  
  


  


      

    

    

      
      

        


  

        

      Christopher W. Ryan
    

    

      
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Rather than just posting your error message, it helps immensely to post the
code that produced the error--indeed with some small sample data that
reproduces the problem.

x <- rnorm(40)
y <- 0.6 * x + rnorm(40, sd = 0.3)
plot(y ~ x)
model <- lm(y ~ cos(x))
summary(model)
plot(y ~ cos(x))
abline(model, col = "red")

## obviously I am not claiming that this is a meaningful or sensible model
## It's just for illustrative purposes

--Chris Ryan

        
On Thu, Jun 15, 2017 at 3:48 PM, lily li <chocold12 at gmail.com> wrote:

        

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

        

  
  


  


      

    

    

      
      

        


  

        

      William Dunlap
    

    

      
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You really have to show what you did to get the result you showed.  E.g.,
something like the following:

            

      
      
      
    

        
(Intercept)      cos(x)
   5.079190    3.022805

            

      
      
      
    

        
[1] 0.9915548

            

      
      
      
    

        
value
2.232099e-06



Bill Dunlap
TIBCO Software
wdunlap tibco.com

        
On Thu, Jun 15, 2017 at 12:48 PM, lily li <chocold12 at gmail.com> wrote: