I am writing a research paper commenting theresults of the following regression, which is a GLM quasibionomial regression with a logit link (the outcome variable capfactor ranges between 0 and 1).
formula<-capfactor ~ log(input) myglm1<-glm(formula,data=daily2, family = quasibinomial('logit')) coeftest(myglm1, vcov.=vcovHC(myglm1, type="HC0"))
Here is the summary of the results:
z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.976206 0.104157 9.3724 < 2.2e-16 *** log(input) -0.067847 0.024697 -2.7472 0.006011 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
I am strugguling to interpret the coefficient in human-understandable terms. I am aware of this answer Logit-link GLM Summary Interpretation, but still I am not able to formulate a satisfactory result statement. How can I derive something like an 'average marginal effect' interpretation from the coefficient?
Something along the lines of:
'On average a 1% increase in the
inputvariable yields to a x percentage points decline in the