It causes inefficient estimators and we cannot trust our p-values.Ĭlick to learn more about checking for and correcting for heteroskedasticity in OLS. We suffer heteroskedasticity when the variance of errors in our model vary (i.e are not consistently random) across observations. a linear model) to show the impact of changing the robust argument. So I will run a “gaussian” general linear model (i.e. The assumption of homoskedasticity is does not need to be met in order to run a logistic regression. One additional thing we can specify in the summ() function is the robust argument, which we can use to specify the type of standard errors that we want to correct for. stargazer(model_bin, model_bin_2, type = "text") Additionally, both the Pseudo R 2 scores are larger! So we can say that the model with the additional judicial corruption variable is a better fitting model.Ĭlick here to learn more about the AIC and choosing model variables with a stepwise algorithm function. The AIC of the second model is smaller, so this model is considered better. summary(model_bin_2 <- glm(coup_binary ~Īnd run the summ() function like above: summ(model_bin_2, vifs = TRUE) If we add another continuous variable – judicial corruption score – we can see how this affects the overall fit of the model. So don’t be disheartened if your Pseudo scores seems to be always low. However, a McFadden’s Pseudo R 2 ranging from 0.3 to 0.4 can loosely indicate a good model fit. an R 2 over 0.7 is considered a very good model), comparisons between Pseudo R 2 are restricted to the same measure within the same data set in order to be at all meaningful to us. Compared to OLS R 2, which has a general rule of thumb (e.g. Unfortunately, there is no broad consensus on which one is the best metric for a well-fitting model so we can only look at the trends of both scores relative to similar models. These two Pseudo measures are just two of the many ways to calculate a Pseudo R 2 for logistic regression. There is no agreed equivalent to R 2 when we run a logistic regression (or other generalized linear models). The Cragg-Uhler is just a modification of the Cox and Snell R 2. In the above MODEL FIT section, we can see both the Cragg-Uhler (also known as Nagelkerke) and the McFadden Pseudo R 2 scores give a measure for the relative model fit. summ(model_bin_1, vifs = TRUE)Īnd we can see there is no problem with multicollinearity with the model the VIF scores for the two independent variables in this model are well below 5.Ĭlick here to read more about the Variance Inflation Factor and dealing with pesky multicollinearity. Set the vifs argument to TRUE for a multicollineary check. This function also works with regular OLS lm() type models. The summ() function can give us more information about the fit of the binomial model. It was created by Jacob Long from the University of South Carolina to help create simple summary tables that we can modify in the function. This is where the jtools package comes in. Labs(title = "Did a regime end with a coup in 1994?", Ggplot(aes(x = coup_binary, fill = as.factor(coup_binary))) + vdem_2 %ĭplyr::mutate(regime_end = as.numeric(v2regendtype)) %>%ĭplyr::mutate(coup_binary = ifelse(regime_end = 0 |regime_end =1 | regime_end = 2, 1, 0))įirst we can quickly graph the distribution of coups across different regions in this year: palette %ĭplyr::group_by(as.factor(coup_binary), as.factor(region)) %>%ĭplyr::mutate(count_conflict = length(coup_binary)) %>% Click here to read the VDEM codebook on this variable. We can extract all the regimes that end due to a coup d’etat in 1994.
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