
#201




I sat for the December sitting, scored very high on the analytics portion, but very low on the writing portion, and barley failed overall.
I'm pretty upset they just changed the format of the exam because I thought I would have any easy pass this time just by reviewing the material and copying their writing format for the December sitting. It still doesn't seem like it should be too bad, but I'm just about done reviewing and I'll probably be checking this forum regularly now. Whaddup everyone Also, I'm not sure anyone posted this yet, but it was in the Decmber thread and helped me a lot https://www.rbloggers.com/indepth...expertvideos/
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Last edited by Josh Peck; 05162019 at 04:54 PM.. 
#202




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Thanks for sharing! Gotta look into these videos after I get more familiar with module materials
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__________________ Prelims: VEE: FAP: 
#203




A couple things I was hoping to get some input on. Does anyone have a good way to check the residuals for a glmnet object? For a glm object all you have to do is plot(glm) and you'll get four model diagnostic plots. Any way to do a similar thing for a regularized regression model?
The only solution I can think of is to create a glm using the exact same features and coefficients as the glmnet model. Though any other easier ways to do this would be appreciated. Also on the topic of regularized regression, the family choices for a glmnet object don't include any positivitecontinuous distributions such as Gamma. What options do we have for making a regularized GLM with something like a Gamma distribution + log link? Last edited by Squeenasaurus; 05172019 at 03:13 PM.. 
#204




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This is a proxy to get to your gamma distribution, but not exactly perfect. It's the best you can do with the glmnet() function, and certainly will be good enough in the context of the exam. 
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#208




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Modeling a log(target) implies that ln(target) has a mean of XB with a distribution of your family function. You are right that E[f(x)] =/= f(E[x]). So they are not the same. But in the context of glmnet(), I am not sure a better approach exists. Last edited by midwesterner; 05172019 at 05:26 PM.. 
#209




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Y ~ Normal(mu, sigma) The link function comes into play by specifying a relationship the mean of this distribution has with the predictor variables. ln[E(Y)] = B0 + B1*X1 + ... + Bp*Xp + error or E(Y) = mu = e^(B0 + B1*X1 + ... + Bp*Xp + error) Notice how this says the expected value will always be positive but it places no restriction on the actual Y values. They can still be negative because it is normally distributed. Now let's say you have a OLS linear model (AKA a GLM with a Normal distribution and identity link function) but you logtransformed the target variable instead. This is saying: ln(Y) ~ Normal(mu, sigma) The link function is the identity so its simply saying: E[ln(Y)] = mu = B0 + B1*X1 + ... + Bp*Xp + error Notice how here we are saying the actual Y values must always be positive. If you look closely, Y actually follows a LogNormal distribution. This will then also always produce positive expected values for Y. In short, they are not the same because E[ln(Y)] != ln[E(Y)] 
#210




GLM Functions
Does anyone know where I can get a good summary of the different distributions and link functions associated with a GLM? In the new sample problem I was able to pick the Logit and Probit link functions as appropriate but really wasnt able to expound upon why. I dont think the modules go into very good detail on these topics.

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