在 Zelig relogit 的输出中访问 z 值和其他统计信息

Question

I want to compute a logit regression for rare events. I decided to use the Zelig package ( relogit function) to do so.

Usually, I use stargazer to extract and save regression results. However, there seem to be compatibility issues with these two packages ( Using stargazer with Zelig ).

I now want to extract the following information from the Zelig relogit output:

Coefficients, z values, p values, number of observations, log likelihood, AIC

I have managed to extract the p-values and coefficients. However, I failed at the rest. But I am sure these values must be accessible somehow, because they are reported in the summary() output (however, I did not manage to store the summary output as an R object). The summary cannot be processed in the same way as a regular glm summary ( https://stats.stackexchange.com/questions/176821/relogit-model-from-zelig-package-in-r-how-to-get-the-estimated-coefficients )

A reproducible example:

##Initiate package, model and data

require(Zelig)

data(mid)

z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
                data = mid, model = "relogit")

##Call summary on output (reports in console most of the needed information)

summary(z.out1)

##Storing the summary fails and only produces a useless object

summary(z.out1) -> z.out1.sum

##Some of the output I can access as follows

z.out1$get_coef() -> z.out1.coeff
z.out1$get_pvalue() -> z.out1.p
z.out1$get_se() -> z.out1.se

However, I did not find similar commands for other elements, such as z values , AIC etc. However, as they are shown in the summary() call, they should be accessible somehow.

The summary call result:

Model: 

Call:
z5$zelig(formula = conflict ~ major + contig + power + maxdem + 
    mindem + years, data = mid)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0742  -0.4444  -0.2772   0.3295   3.1556  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.535496   0.179685 -14.111  < 2e-16
major        2.432525   0.157561  15.439  < 2e-16
contig       4.121869   0.157650  26.146  < 2e-16
power        1.053351   0.217243   4.849 1.24e-06
maxdem       0.048164   0.010065   4.785 1.71e-06
mindem      -0.064825   0.012802  -5.064 4.11e-07
years       -0.063197   0.005705 -11.078  < 2e-16

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 3979.5  on 3125  degrees of freedom
Residual deviance: 1868.5  on 3119  degrees of freedom
AIC: 1882.5

Number of Fisher Scoring iterations: 6

Next step: Use 'setx' method

Answer 1

Use from_zelig_model for deviance, AIC.

m <- from_zelig_model(z.out1)
m$aic
...

Z-values are coefficient / sd.

z.out1$get_coef()[[1]]/z.out1$get_se()[[1]]