I am interested in calculating p-values within a Cox PH model based upon the maximal test statistic, to get very robust estimates. Does anyone have experience with this?
I have played around a bit with the R package 'coxphf' that incorporates Firth's penalized likelihood, but it seems to be giving me different coefficients and p-values if I chose firth=FALSE vs. use the standard coxph function in 'survival'.
I do not discount being completely lost on this, so any advice would be useful.
Thanks!
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I have been tasked with calculating the SE for logistic regression point estimates (where all my predictor variables are factors). I typically use ggpredict to estimate my predictions which provides CI's. However, we are comparing our results to estimates from program MARK and we find readers have a better grasp at understanding our plots with SE as opposed to 95% CI's.
Based on reading the package notes, it appears I can simply calculate (conf.high - predicted value)/1.96). Am I correct? Or am I missing something and that is not the correct way to calculate SE for the predicted estimates. If I am wrong, any ideas on how I can do this or do I need to just use CI's?
Thank you very much for your help.
I'm looking for a function for interaction effects visualization which has a correspondence with ivreg or plm. My model is 2sls with fixed effects but it seems there are no packages available for calculating interaction effects in R.
I'd be pleased if someone could solve my concern.
You might want to have a look at interplot(). You can use this function to visualize e.g. the estimated coefficient of regressor X on outcome Y conditional on values of instrument Z by simply plugging in the fitted values from ivreg(). (The confidence intervals are trickier, but you are probably less interested in those in the first instance.)
https://cran.r-project.org/web/packages/interplot/vignettes/interplot-vignette.html
I'm working on a classification problem (predicting three classes) and I'm comparing SVM against Random Forest in R.
For evaluation and comparison I want to calculate the bias and variance of the models. I've looked up the two terms in many machine learning books and I'd say I do understand the sense of variance and bias (easiest explanation with the bullseye). But I can't really figure out how to apply it in my case.
Let's say I predict the results for a test set with 4 SVM-models that were trained with 4 different training sets. Each time I get a total error (meaning all wrong predictions/all predictions).
Do I then get the bias for SVM by calculating this?
which would mean that the bias is more or less the mean of the errors?
I hope you can help me with not to complicated formula, because I've already seen many of them.
I am trying to estimate confidence intervals for a mixed effects poisson model using robust standard errors in R. I followed these instructions and was able to estimate confidence intervals for a model without random effects.
Now, I would like to estimate confidence intervals with robust standard errors for a poisson model, with a random term. It seems like the sandwich command does not work for glmer, only glm. I have not been able to find a good solution yet. Any suggestions?
I want to know the goodness of fit while fitting a power law distribution in R using poweRlaw package.
After estimate_xmin() , I had a p-value 0.04614726. But the bootstrap_p() returns another p-value 0.
So why do these two p-value differ? And how can I judge if it is a power law distribution?
here is the plot when using poweRlaw for fittingpoweRlaw fitting result
You're getting a bit confused. One of the statistics that estimate_xmin returns is the Kolmogorov-Smirnoff statistic (as described in Clauset, Shalizi, Newman (2009)). This statistic is used to estimate the best cut-off value for your model, i.e. xmin. However, this doesn't tell you anything about the model fit.
To assess model suitability is where the bootstrap function comes in.