[R] Comparing linear regression coefficients to a slope of 1

Albyn Jones jones at reed.edu
Sun Nov 25 05:52:31 CET 2012


Dear Cat

My apologies for presuming...

Here's a "primitive" solution:  compute a t-statistic or CI.

t = (beta-hat - 1)/SE(beta-hat), compare to qt(.975, res.df)

Or Better, compute the 95% confidence interval

   beta-hat + c(-1,1)*qt(.975, res.df)*SE(beta-hat)

albyn

On 2012-11-24 18:05, Catriona Hendry wrote:
> Hi,
>
> @ Albyn, David.. No, its not homework. Its basic groundwork for 
> testing
> allometric relationships for a graduate project I am working on. I 
> read the
> guide before posting, I spent half the day trying to understand how I 
> am
> going wrong based on the advice given to others.
>
> @Bert, David... I apologise for the lack of code, I wasn't sure how 
> to
> explain my problem and I guess I went about it the wrong way.
>
> I do think this is what I need to be doing, I am testing allometric
> relationships of body size against a predicted isometric (1:1)
> relationship. So I would like to know if the relationship between my
> variables deviates from that.
>
> Hopefully the information below will be what is needed.
>
> Here is the part of the code relevant to the regression and plot:
>
>
>>plot(Contrast_log_MTL_ALL, Contrast_log_FTL_ALL)
>
>>Regression_PhyloContrasts_ALL <- lm(Contrast_log_FTL_ALL ~
> Contrast_log_MTL_ALL, offset=1*Contrast_log_MTL_ALL)
> abline(Regression_PhyloContrasts_ALL)
>
> the plot that resulted is attached as an image file.
>
>
> Below are the vectors of my variables. The are converted from other 
> values
> imported and indexed from a csv file, so unfortunately I don't have 
> matrix
> set up for them.
>
>   Contrast_log_FTL_ALL Contrast_Log_MTL_ALL  83 0.226593 0.284521  84
> 0.165517 0.084462  85 -0.1902 -0.0055  86 0.585176 0.639916  87 
> -0.01078
> 0.118011  88 0.161142 0.073762  89 -0.08566 -0.04788  90 -0.13818 
> -0.0524
> 91 -0.02504 -0.21099  92 -0.05027 -0.07594  93 -0.11399 -0.07251  94
> -0.07299 -0.08247  95 -0.09507 -0.04817  96 0.207591 0.151695  97 
> -0.14224
> -0.05097  98 0.06375 -0.0229  99 0.04607 0.06246  100 0.257389 
> 0.190531  101
> -0.0612 -0.10902  102 -0.1981 -0.24698  103 -0.12328 -0.36942  104 
> 0.269877
> 0.341989  105 0.125377 0.227183  106 0.087038 -0.05962  107 0.114929
> 0.096112  108 0.252807 0.305583  109 -0.0895 -0.08586  110 -0.38483 
> -0.20671
> 111 -0.72506 -0.63785  112 -0.37212 -0.21458  113 0.010348 0.117577  
> 114
> -0.09625 -0.0059  115 -0.26291 -0.25986  116 0.056922 0.064041  117 
> 0.051472
> -0.09747  118 -0.05691 0.075005  119 0.117095 -0.15497  120 -0.01329
> -0.12473  121 0.098725 0.020522  122 -0.0019 -0.01998  123 -0.12446 
> -0.02312
> 124 0.019234 0.031391  125 0.385366 0.391766  126 0.495518 0.468946  
> 127
> -0.09251 -0.08045  128 0.147965 0.139117  129 -0.03143 -0.02319  130
> -0.19801 -0.14924  131 0.014104 -0.01917  132 0.031872 -0.01381  133
> -0.01412 -0.04381  134 -0.12864 -0.08527  135 -0.07179 -0.03525  136 
> 0.31003
> 0.29553  137 -0.09347 -0.11903  138 -0.10706 -0.16654  139 0.078655 
> 0.065509
> 140 0.08279 -0.00766  141 0.181885 0.001414  142 0.345818 0.496323  
> 143
> 0.235044 0.095073  144 -0.03022 0.039918  145 0.042577 0.136586  146
> 0.064208 0.001379  147 -0.02237 -0.03009  148 -3.55E-05 0.040197  149
> 0.011168 0.087116  150 0.019964 0.071822  151 -0.04602 -0.06616  152
> 0.083087 0.038592  153 0.032078 0.107237  154 -0.21108 -0.22347  155
> 0.122959 0.297917  156 -0.05898 0.012547  157 -0.07584 -0.21588  158
> -0.00929 -0.06864  159 -0.01211 -0.04559  160 0.090948 0.136582  161
> 0.016974 0.018259  162 -0.04083 0.016245  163 -0.20328 -0.31678
>
>
>
>
>
>
> On Sat, Nov 24, 2012 at 8:22 PM, Bert Gunter <gunter.berton at gene.com> 
> wrote:
>
>> 1. The model is correct :  lm( y~ x + offset(x))
>> ( AFAICS)
>>
>> 2. Read the posting guide, please: Code? I do not know what you mean 
>> by:
>>
>> " this resulted in a regression line that was plotted perpendicular 
>> to
>> the data when added with the abline function."
>>
>> Of course, maybe someone else will groc this.
>>
>> 3. I wonder if you really want to do what you are doing, anyway. For
>> example, in comparing two assays to see whether they give "similar"
>> results, you would **not** do what you are doing. If you care to 
>> follow up
>> on this, I suggest you post complete context to a statistical 
>> mailing list,
>> not here, like stats.stackexchange .com.  Also, feel free to ignore 
>> me, of
>> course. I'm just guessing.
>>
>> Cheers,
>> Bert
>>
>> Cheers,
>> Bert
>>
>>
>> On Sat, Nov 24, 2012 at 4:27 PM, Catriona Hendry 
>> <hendry at gwmail.gwu.edu>wrote:
>>
>>> Hi!
>>>
>>> I have a question that is probably very basic, but I cannot figure 
>>> out how
>>> to do it. I simply need to compare the significance of a regression 
>>> slope
>>> against a slope of 1, instead of the default of zero.
>>>
>>> I know this topic has been posted before, and I have tried to use 
>>> the
>>> advice given to others to fix my problem. I tried the offset 
>>> command based
>>> on one of these advice threads as follows:
>>>
>>> Regression <- lm(y~x+offset(1*x))
>>>
>>> but this resulted in a regression line that was plotted 
>>> perpendicular to
>>> the data when added with the abline function.
>>>
>>> I would be extremely grateful for your help!!
>>>
>>> Thanks!!
>>>
>>> Cat
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help at 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.
>>>
>>
>>
>>
>> --
>>
>> Bert Gunter
>> Genentech Nonclinical Biostatistics
>>
>> Internal Contact Info:
>> Phone: 467-7374
>> Website:
>>
>> 
>> http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
>>
>>
>>




More information about the R-help mailing list