Apr
16
comment PH assumption: categorical variable (1 category does not interact with time)
Creating dummy variables beforehand is a possible way to go. In this example, the variable drug has 3 levels (2 dummy variables in the model), but I let only one of the 2 dummy variables (i.e. only one of the drugs, here) to interact with log(time): webuse cancer xi i.drug stcox i.drug, tvc(_Idrug_3) texp(log(_t)) - hope the syntax is clear
Apr
14
comment Is $R^2$ useful or dangerous?
@probabilityislogic Do you have a reference for the relation between $AIC$ and $R^2$?
Mar
25
comment Discrepancy between metafor and weighted lm() standard errors
The PDF manual for Stata's vwls command contains the answer to your question: stata.com/manuals13/rvwls.pdf See Section "Remarks and examples".
Mar
14
comment Can p-value = <0.05 with CI that includes 0 using chi2 in Stata?
Quickly: the standard errors used to calculate the test and the CI are different. Take a look at the PDF of the Stata User's Guide (help prtest).
Mar
1
comment A Book for Multiple Regression and Multivariate analysis
I really like Applied Regression Analysis by Draper and Smith. It covers also topic in which you might not be interested (ridge regression, nonlinear estimation, GLM...), but the parts on linear regression are outstanding, in my opinion. The chapters on the geometry of least squares are probably the best part of this book.
Feb
22
comment Is the likelihood a true function?
Why the bayesian tag?
Feb
16
awarded Yearling
Feb
16
awarded Yearling
Dec
23
comment Required number for logistic regression
If you have say 100 subjects, 20 of whom die and 80 survive, then according to this rule of thumb you should have no more than 2 covariates (predictor variables) in your model. In fact, with 2 covariates the condition is satisfied: $\frac{\min(20,80)}{2} \ge 10$, while with 3 covariates it is not $\frac{\min(20,80)}{3}<10$
Oct
16
awarded Good Question
Oct
16
comment Ridge & LASSO norms
@user12202013: thank you for pointing that out. I didn't notice that.
Oct
16
comment Ridge & LASSO norms
@Benjamin: in point #1 did you actually mean "(not all penalized estimators will be unbiased)"? Ridge regression –just to name one– is biased.
Oct
15
comment Ridge & LASSO norms
The penalty term in ridge regression is the squared L2 norm. See these slides written by Tibshirani as an example (slide 7) stat.cmu.edu/~ryantibs/datamining/lectures/16-modr1.pdf See also here en.wikipedia.org/wiki/Tikhonov_regularization
Oct
6
comment Weighted Kaplan-Meier Curve Log Rank Test
I don't know of any R function to does that, but if you're willing to write your own code, this papers might be useful to you: ncbi.nlm.nih.gov/pubmed/16189810 and ncbi.nlm.nih.gov/pubmed/19199275
Sep
30
awarded Explainer
Sep
24
awarded Autobiographer
Sep
24
awarded Autobiographer
Sep
24
awarded Autobiographer
Sep
24
awarded Autobiographer
Sep
24
awarded Autobiographer
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