library(flextable)
dat <- attitude
dat$high.rating <- (dat$rating > 70)
probit.model <- glm(high.rating ~ learning + critical +
advance, data=dat, family = binomial(link = "probit"))
ft <- as_flextable(probit.model)
ft
Estimate | Standard Error | z value | Pr(>|z|) | ||
(Intercept) | -7.476 | 3.570 | -2.094 | 0.0362 | * |
learning | 0.164 | 0.053 | 3.079 | 0.0021 | ** |
critical | -0.001 | 0.044 | -0.013 | 0.9896 |
|
advance | -0.062 | 0.042 | -1.472 | 0.1410 |
|
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 | |||||
| |||||
(Dispersion parameter for binomial family taken to be 1) | |||||
Null deviance: 38.19 on 29 degrees of freedom | |||||
Residual deviance: 18.17 on 26 degrees of freedom |