Extracting Beta, SE and P-Value from GLM model
1
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Entering edit mode
9 weeks ago
Agamemnon ▴ 40

Hi there,

I want to extract beta from a glm model analysis, my variables are as follows:

test.data

ID  Phenotype           snp
1442211 0.947595614262281   2
1501431 0.828960485290687   2
1676115 1.43030605455755    2
1700393 0.77901178220667    2
2028223 0.981493321966468   2
2075852 0.734295870666147   2
2601302 0.829567302722706   2
2764237 0.771672795944737   2
2945830 0.883541977719076   2
3298196 0.943926904715075   2
3526834 0.732810576113019   2
3631754 0.801726287522778   2
3658601 1.04733798012754    2
3935118 0.765959341809356   2
3957833 1.05461197940193    2
3975539 1.17690566037736    2
4170014 0.80678640233671    2
5016911 1.18594231307757    2
5140477 0.770128136053784   2
5255187 1.34567744394334    2
5437481 0.790346873573711   2
5659818 1.00715236762145    2
1002613 0.769225441532956   1
1002726 0.784628007708894   1
1006494 0.859776904196516   1
1006695 0.723499071590675   1
1006712 0.978525656704006   1
1011518 1.67284614392017    1
1013652 0.970961197058078   1
1013817 0.793608698534774   1

My glm formula looks as follows which gives the following results:

Call:
glm(formula = phenotype ~ snp, family = gaussian(), data = test.data)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.22063  -0.15915  -0.09593   0.06366   0.72871  

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.951188   0.171248   5.554 6.11e-06 ***
snp         -0.007054   0.095730  -0.074    0.942    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.05376381)

    Null deviance: 1.5057  on 29  degrees of freedom
Residual deviance: 1.5054  on 28  degrees of freedom
AIC: 1.3719

Number of Fisher Scoring iterations: 2

I would like to extract the beta, and double check which value it is?

I am assuming it is -0.007054?

Thanks

beta glm r • 211 views
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1
Entering edit mode
9 weeks ago
LChart 840

I use summary(model)$coefficients to get the coefficient table.

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