7

You appear to be misunderstanding the output from your model. In your code, the line: abline(fit.mice$coefficients[1:2], col="skyblue3") plots a line with the correct intercept, but the wrong slope. fit.mice$coefficients[1] is the intercept, but fit.mice$coefficients[2] is the estimate for treat in the Mice added group, hence this is an offset to the ...


3

I'm afraid I don't know Stata (at all...), so I'm making some guesses here. Your real data differ somehow, or Stata is doing something I can't divine, because you have yhats for two patients with missing responses. Using complete case analysis, I replicated the logistic regression model in R. Your yhats are predicted probabilities from a standard ...


1

I would make a scatterplot colored by grouping for entity_type. In Python you could use Seaborn with import seaborn as sns. The plot would then be executed by: sns.pairplot(x_vars=['row_count'], y_vars=['duration_of_execution'], data=df_name, hue='entity_type', size=5) You may need to adjust some of the calls based on how your dataframe is named in Python, ...


1

Figured it out eventually. You can call several graphs in the same window, but in order to make it fit the dimensions, graphing on the same variables is important. Therefore the new call (in stata) becomes: twoway (scatter x1 x2, mlabel(y) mlabposition(12) mlabangle(forty_five)) || (lfit x1 x2 if y == 1) || (lfit x1 x2 if y == 0)) The || denotes (AND) and ...


1

The comment from @whuber to this question is correct. The chart in question is not a histogram though if the same visualization showed different data it could be a histogram. A histogram doesn't have categories. It shows data continuously, in degrees, amounts or some other measurement. This could be modified to be a histogram by changing the input data. ...


1

You have created this map from a MCA. Hope you have categorical data and that you are aware that this technique (as PCA) tries to find some structures behind your dataset. Thus the map does not represent the raw values rather than a relational model from the associations between the variables of your sample. Hope you have already consider a principal ...


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