Interpretation of binomial regression in R I'm running a regression to see if there is a positive or negative relationship between cvdrslt and smkcigst, but I'm not sure how to interpret this regression. Also why is the data for smkcigst NA, even though the dataset has no NAs for it and instead has 1 for all observations?
Does this regression show that there is a positive or a negative relationship between the two variables? I'm also having trouble creating plots because the plot only shows 2 points, please help me write a code to plot this regression.




[...] why is the data for smkcigst NA, even though the dataset has no NAs for it and instead has 1 for all observations?

The answer lies in your question: smkcigst just consists of ones, so there is no variation in this variable. If e.g. this variable measures the amount of smoked cigarettes, your sample only consists of smokers and you have no non-smokers to compare them to.
A more technical answer was already given in @fmtcs comment: a linear regression estimated via OLS does not allow that a variable is a "linear combination of some others", and since the intercept already consists of only ones, smkcigst is such a linear combination. This is why glmfunction automatically drops it.

Does this regression show that there is a positive or a negative relationship between the two variables?

No, because there is no variation in smkcigst

I'm also having trouble creating plots because the plot only shows 2 points, please help me write a code to plot this regression.

If both variables are binary, the plot is correct. Consider this: smkcigst only has value $1$, while cvdrslt can take values $1$ and $0$. Then, there are only two possible combinations: $(1, 0)$ and $(1, 1)$. Since observations overlay each other, you could use some jitter to improve plotting, e.g. by using geom_jitter() from the package ggplot2.
