Hi, Thank you all so much for the help provided here!
@Dennis.. from your work-through I can see where I had gotten lost, I greatly appreciate your time. The question that remains is whether or not I actually need to be doing this it seems, so here is the rationale... The variables cannot be swapped as dependant and independant. The key point is that this is a test of the predictions of a particular allometric pattern, where male size is under consistant directional selection, and female size is inherently linked to male size, but not under the same selection. So changes in male size elicit a change in female size, but not the other way around. As for whether I need to be forcing this through the origin... thats a biological issue I will have to consider. Theoretically if I am testing deviation from an isometric relationship of body size it should I suppose. Thank you all once again. Cat On Sun, Nov 25, 2012 at 7:14 AM, Michael Dewey <i...@aghmed.fsnet.co.uk>wrote: > At 02:05 25/11/2012, 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: >> > > Cat, > What did you think the call to abline was going to do? Did you mean to use > the reg parameter? > > But really Bert's advice dominates any other here, is this really, really > what you want to do? > > > > >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.ber...@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 <hen...@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@r-project.org mailing list >> >> https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help> >> >> PLEASE do read the posting guide >> >> http://www.R-project.org/**posting-guide.html<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<http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm> >> > >> > >> > >> >> >> -- >> ------------------------------**----------- >> *Catriona Hendry* >> *Postgraduate Student* >> *Biological Sciences Department* >> *George Washington University* >> *hen...@gwmail.gwu.edu* >> >> Content-Type: image/png; name="regression error.png" >> Content-Disposition: attachment; filename="regression error.png" >> X-Attachment-Id: f_h9xixz060 >> > > Michael Dewey > i...@aghmed.fsnet.co.uk > http://www.aghmed.fsnet.co.uk/**home.html<http://www.aghmed.fsnet.co.uk/home.html> > > -- ----------------------------------------- *Catriona Hendry* *Postgraduate Student* *Biological Sciences Department* *George Washington University* *hen...@gwmail.gwu.edu* [[alternative HTML version deleted]] ______________________________________________ R-help@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.
