Zelig | A statistical framework that serves as a common interface to a large range of models | Data Visualization library

I have 2 tables:

Users:

It might be a trivial question to many of you.

I have matched the treatment observations with a large pool of control observations using the MatchIt package in r with method nearest. After extracting the matched data, should I use independent sample t-test or paired t-test to compare a continuous outcome variable? My understanding is that matching mimics balanced randomization where treatment and control groups are similar in terms of exogenous confounders. However, I've found some tutorials which are using paired t-test after matching. That made me wonder which treatment observation is matched with which control observation? I couldn't find an index variable that may answer this question. Following is a sample code that is similar to what I have used for my data:

I am using Afrobarometer survey data using 2 rounds of data for 10 countries. My DV is a binary 0-1 variable. I need to use logistic regression, fixed-effects, clustered standard errors (at country), and weighted survey data. A variable for the weights already exists in the dataframe.

I've been looking at help files for the following packages: clogit, glm, pglm, glm2, zelig, bife , etc. Typical errors include: can't add weights, can't do fixed effects, cant do either or etc.

I am trying to complete seemingly unrelated regressions (SUR) using the systemfit package in R. However, it is not straightforward to complete these analyses with multiply imputed data (with mice package).

Upon googling this question, I see that there was a deleted post about the identical question, which seems to have utilized the following example (credit to poster, minor edits)

I am trying to get the total employee count under a manager and trying to add it If IsManager=True from the below nested JSON

I am trying to get the employee count under a manger and trying to add it If Manager=True from the below nested JSON

I am trying to use imputed data created with MICE in Stata.

My understanding of the steps are:

1) converting the mids object to mi in R

I am using a multiply imputed dataset with Amelia and would then like Zelig to calculate predicted values from a regression model. Zelig's documentation states that "When quantities of interest are plotted, such as expected and predicted values and first differenences, these are correctly pooled across those from each of the m imputed datasets". This is true, but I would also like to obtain estimated values pooled across each of the imputed datasets as the output of the "sim" command.

Here is sample code replicating the instructions on the Zelig webiste and generating the same output:

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: