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Here's a high-level overview. Lasso can be used in three ways:
Prediction
Model selection
Inference
(1) entails predicting the value of an outcome conditional on a large set of potential regressors, both in and out of sample. (2) entails selecting a set of variables that predicts the outcome well, but not necessarily selecting variables in the "true" model ...
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Start by defining parameters $\beta_0$ and $\beta_1$. Then select a distribution $F$ for the error term $\epsilon_t$, for example a normal distribution in which case you have to specify mean $0$ and then select some value for the variance $\sigma^2$. Then choose some value $T$ number of timeperiods to simulate and some initial value for the process $y_0$ and ...
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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 ...
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In practice it means that your predicted values are negatively correlated with your outcome variable: when the true value is 1, your predicted values are close to zero, and vice versa. You can flip the ROC curve by subtracting from 1 your predicted values.
ROC curve can be plotted by either using "lroc" or by first generating a variable with your ...
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For a Gaussian glm (where the population parameter is the OLS parameter) you can just divide the dispersion parameter by the population variance and subtract from 1
Using one of the examples from the svyglm help page:
> data(api)
> dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
> api.reg <- svyglm(api.stu~enroll,...
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