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I have a dataset of counts of four different metadata factors associated with a gene and two experimental groups, FGT and free, with 52 and 40 unique genes respectively. The first 100 rows can be found here: https://pastebin.com/PAG5pCDh (I can provide more)

Having performed a poisson distributed glm on count data and identifying the variable origin as a significant predictor, as originfree is significant (I think I am under standing that correctly?), how do I determine if origin free is associated with a higher or lower count.

A truncated output of coefficients for the glm looks like this:

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -7.100e-01  5.827e-01  -1.218 0.223062    
originfree           -2.921e-01  8.830e-02  -3.308 0.000939 ***
variableDuplication   1.427e-01  1.116e-01   1.279 0.201013    
variableKnown_target -1.609e+00  2.000e-01  -8.047 8.47e-16 ***
variablePhylogeny     1.310e-01  1.119e-01   1.171 0.241491    
geneGrpE              1.792e+00  6.236e-01   2.873 0.004063 ** 
genePGK              -4.455e-15  8.165e-01   0.000 1.000000    
geneRibosomal_S14     6.931e-01  7.071e-01   0.980 0.326959    
geneSHMT              2.079e+00  6.124e-01   3.396 0.000684 ***
geneTIGR00009         9.758e-15  8.165e-01   0.000 1.000000    
geneTIGR00057         6.931e-01  7.071e-01   0.980 0.326959    
geneTIGR00069        -6.149e-15  8.165e-01   0.000 1.000000    
geneTIGR00079         1.386e+00  6.455e-01   2.148 0.031743 *  
geneTIGR00105         1.386e+00  6.455e-01   2.148 0.031743 * 

I see that originfree is significant, which I understand to mean it the fact of something being originfree or not significantly affects the models ability to predict count )please correct me if I am wrong)

Now how do I find out if originfree is associated with an increase or decrease in the count of the four metadata factors? Would I have to run separate glms on subset dataframe for each metadata factor in order to work this out?

My alternative hypothesis is that it would lead to a decrease

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    $\begingroup$ Read the value of the estimated coefficient. $\endgroup$
    – whuber
    Jul 9, 2020 at 13:50
  • $\begingroup$ I believe your question is answered here. $\endgroup$
    – Stephen G
    Jul 9, 2020 at 13:56
  • $\begingroup$ Thank you @StephenG That is very helpful! $\endgroup$
    – Lamma
    Jul 9, 2020 at 14:11
  • $\begingroup$ @whuber So would that for example mean ´originfree` results in a -2.921 decrease with even 1 unit increase of the intercept? And then how do i identify what the intercept is made of? Is it one of each origin, variable and gene ? $\endgroup$
    – Lamma
    Jul 29, 2020 at 14:56
  • $\begingroup$ I cannot connect that comment with the question, but permit me to remark that (1) the coefficient of originfree is $-0.292$ and (2) because it's negative, higher values of originfree are associated with lower values of the response variable. $\endgroup$
    – whuber
    Jul 29, 2020 at 15:10

1 Answer 1

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You exponentiate the estimation of the coefficient and this gives you a multiplicative factor by which you can see the effect of the coefficient.

In R this can be done by:

exp(estimation)

As highlighted by @Whuber and directed to an answer by @Stephen G and also a very good answer can be found here

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