[R] Reporting binomial logistic regression from R results

[PIKAL Petr]

Dear Frodo (or Jedi)

The results seems to confirm your assumption that 3 systems are different. How you should present results probably depends on how it is usual to report such results in your environment.

BTW. It seems to me like homework and this list has no homework policy (Sorry, if I am mistaken).

Cheers
Petr
> -----Original Message-----
> From: R-help <r-help-bounces using r-project.org> On Behalf Of Frodo Jedi
> Sent: Monday, November 12, 2018 2:08 AM
> To: r-help using r-project.org
> Subject: [R] Reporting binomial logistic regression from R results
>
> Dear list members,
> I need some help in understanding whether I am doing correctly a binomial
> logistic regression and whether I am interpreting the results in the correct way.
> Also I would need an advice regarding the reporting of the results from the R
> functions.
>
> I want to report the results of a binomial logistic regression where I want to
> assess difference between the 3 levels of a factor (called System) on the
> dependent variable (called Response) taking two values, 0 and 1. My goal is to
> understand if the effect of the 3 systems (A,B,C) in System affect differently
> Response in a significant way. I am basing my analysis on this URL:
> https://stats.idre.ucla.edu/r/dae/logit-regression/
>
> This is the result of my analysis:
>
> > fit <- glm(Response ~ System, data = scrd, family = "binomial")
> > summary(fit)
>
> Call:
> glm(formula = Response ~ System, family = "binomial", data = scrd)
>
> Deviance Residuals:
>     Min       1Q   Median       3Q      Max
> -2.8840   0.1775   0.2712   0.2712   0.5008
>
> Coefficients:
>              Estimate Std. Error z value Pr(>|z|)
> (Intercept)    3.2844     0.2825  11.626  < 2e-16 ***
> SystemB  -1.2715     0.3379  -3.763 0.000168 ***
> SystemC    0.8588     0.4990   1.721 0.085266 .
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> (Dispersion parameter for binomial family taken to be 1)
>
>     Null deviance: 411.26  on 1023  degrees of freedom Residual deviance:
> 376.76  on 1021  degrees of freedom
> AIC: 382.76
>
> Number of Fisher Scoring iterations: 6
> Following this analysis I perform the wald test in order to understand whether
> there is an overall effect of System:
>
> library(aod)
>
> > wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 354.6, df = 3, P(> X2) = 0.0
> The chi-squared test statistic of 354.6, with 3 degrees of freedom is associated
> with a p-value < 0.001 indicating that the overall effect of System is statistically
> significant.
>
> Now I check whether there are differences between the coefficients using again
> the wald test:
>
> # Here difference between system B and C:
>
> > l <- cbind(0, 1, -1)
> > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 22.3, df = 1, P(> X2) = 2.3e-06
>
>
>
> # Here difference between system A and C:
>
> > l <- cbind(1, 0, -1)
> > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 12.0, df = 1, P(> X2) = 0.00052
>
>
>
> # Here difference between system A and B:
>
> > l <- cbind(1, -1, 0)
> > wald.test(b = coef(fit), Sigma = vcov(fit), L = l)
> Wald test:
> ----------
>
> Chi-squared test:
> X2 = 58.7, df = 1, P(> X2) = 1.8e-14
>
> My understanding is that from this analysis I can state that the three systems
> lead to a significantly different Response. Am I right? If so, how should I report
> the results of this analysis? What is the correct way?
>
>
> Thanks in advance
>
> Best wishes
>
> FJ
>
> [[alternative HTML version deleted]]
>
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