Message-ID: <5ABB77E7-36F2-4E0B-886B-F4DBEDEA197D@u.washington.edu>
Date: 2017-06-15T19:51:15Z
From: Don McKenzie
Subject: about fitting a regression line
In-Reply-To: <CAN5afy9Quwt66UVjz+uML7gSf=vDye1rki+W8YJzR8R9KrGg8Q@mail.gmail.com>
Did you perhaps accidentally include your response as a predictor?
> On Jun 15, 2017, at 12:48 PM, lily li <chocold12 at gmail.com> wrote:
>
> 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.
>>>>
>>>> [[alternative HTML version deleted]]
>>>>
>>>> ______________________________________________
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>>>> posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>
>>>>
>>>>
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
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>>
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> ______________________________________________
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