I'm trying to run a weighted Cox regression model but none of the resources I found were actually useful to figure out how to do that.
Basically, I just want to run the model specified below, but weighted.
coxph(Surv(df$outcome)~df$treatment)
Apparently the coxphw function is the way to do this, but I cannot figure out the necessary specifications to actually make that function run.
The 'template' option of the coxphw determines which type of (weighted) estimation of Cox regression is requested. You can choose "AHR" for estimation of average HRs or "ARE" for estimation of average regression effects and "PH" for unweighted Cox proportional hazards regression. Check the ref manual for more details.
Hope it helps
Related
I am currently working on univariate GARCH models with different specifications and got stuck on including the exponential term in the variance equation:
mean model (setting ω4 = 0)
variance model
I am using the rugarch package in R and (unsuccessfully) tried the 'eGARCH' model type and external regressor option for the recession dummy INBER to get the estimates. Is this generally the correct way for including the exponential part or am I completely off?
I have a dataset with data left censored and I wanted to apply a multilevel mixed-effects tobit regression, but I only find information about how to do it in Stata. Is it possible to do it in R?
I found the packages 'VGAM' and 'CensREG', but I don't get how to add fixed and random effects.
Also my data is log-normal distributed, is there a way to add this to the model?
Thanks!
According to Section 3.5 of a vignette, the censReg package can handle a mixed model if the data are prepared properly via the plm package.
This Cross Validated page shows an example.
I don't have experience with this; it might only work with formal panel data rather than more general random-effects structures.
If your data are truly log-normal, you could take logs first and set the lower censoring limit on the log scale. Note that an apparent log-normal distribution of outcomes might just represent a corresponding distribution of predictor values with an underlying normal error distribution around the predictions. Don't jump blindly into a log-normal assumption.
I have a huge dataset and I'm trying to build a good predictive linear model using the relaimpo package.
Using the calc.relimp function with type="lmg, i get an output of variables which are of relative importance. Although the proportion of variance explained by the model is only at 52%, I want to go and build a linear model using these variables.
Is there a way to build a lm model using these variables and somehow take into account the relative importance values into the model?
I'm not too familiar with this and was thinking maybe something along the lines of weighting each variable based on its relative importance value...?
I'm not a statistician, so I won't give you any Greek symbols, but I think you are confusing a few things.
As you correctly say, the relative importances based on the LMG method are more or less some sort of variance decomposition in case of correlated predictor variables, i.e. it tells you how much of your variance in the model is explained by which predictor.
However, this doesn't have to do anything with the lm function and its estimation itself. In fact, the R² of your lm model is exactly the same as you'll get by summing up the relative importances from calc.relimp.
There is no way to tell the lm function to pay more attention to a certain predictor during prediction/estimation.
What you probably want to do is an elastic net (which is a combination of LASSO and RIDGE regression), which basically does what you want, i.e. it shrinks the impact of "unimportant"/small predictors and emphasizes the impact of important/large predictors: https://en.wikipedia.org/wiki/Elastic_net_regularization (Lasso and Ridge regression are linked in the Wikipedia article).
I think this one here is the original package from Jerome Friedman, Trevor Hastie, Rob Tibshirani, et al.: https://cran.r-project.org/web/packages/glmnet/index.html
I'm trying to optimize a multivariate linear regression model lmMod=lm(depend_var~var1+var2+var3+var4....,data=df) and I'm presently working on the premises of the model: the constant variance of residuals and the absence of auto-correlation. For this I'm using:
Breusch-Pagan test for homo/heteroscedasticity: lmtest::bptest(lmMod)
Durbin Watson test for auto-correlation: durbinWatsonTest(lmMod)
I found examples which are testing either one independent variable at a time:
example for Breush-Pagan test – one independent variable:
https://datascienceplus.com/how-to-detect-heteroscedasticity-and-rectify-it/
example for Durbin Watson test - one independent variable:
http://math.furman.edu/~dcs/courses/math47/R/library/lmtest/html/dwtest.html
or the whole model with several independent variables at a time:
example for Durbin Watson test – multiple independent variable:
https://www.rdocumentation.org/packages/car/versions/2.1-6/topics/durbinWatsonTest
Here are the questions:
Can durbinWatsonTest() and bptest() be fed with a whole multivariate model
If answer to 1 is yes, how is it then possible to determine which variable is causing heteroscedasticity or auto-correlation in the model in order to fix it as each of those tests give only one p-value for the entire multivariate model?
If answer to 1 is no, the test should be then performed with one dependent variable at a time. But in the case of homoscedasticity, it can only be tested AFTER a particular regression has been modelled. Hence a pattern of homo/heteroscedasticity in an univariate regression model lmMod_1=lm(depend_var~var1, data=df) will be different from the pattern of a multivariate regression model lmMod_2=lm(depend_var~var1+var2+var3+var4....,data=df)
Thank very much in advance for your help!
I would like to try to give a first help
The answer to the first question: Yes, you can use the Breusch-Pagan test and the Durbin Watson test for mutlivariate models. (However, I have always used the dwtest() instead of the durbinWatsonTest()).
Also note that the dwtest() checks only the first-order autocorrelation. Unfortunately, I do not know how to find out which variable is causing heteroscedasticity or auto-correlation. However, if you encounter these problems, then one possible solution is that you use a robust estimation method, e.g. after NeweyWest (using: coeftest (regression model, vcov = NeweyWest)) at autocorrelation or with coeftest(regression model, vcov = vcovHC) at heteroscedasticity, both from the AER package.
I have a data structure with binary 0-1 variable (click & Purchase; click & not-purchase) against a vector of the attributes. I used logistic regression to get the probabilities of the purchase. How can I use Random Forest to get the same probabilities? Is it by using Random Forest regression? or is it Random Forest classification with type='prob' in R which gives the probability of categorical variable?
It won't give you the same result since the structure of the two method are different. Logistic regression is given by a definitive linear specification, where RF is a collective vote from multiple independent/random trees. If specification and input feature are properly tuned for both, they can produce comparable results. Here is the major difference between the two:
RF will give more robust fit against noise, outliers, overfitting or multicollinearity etc which are common pitfalls in regression type of solution. Basically if you don't know or don't want to know much about whats going in with the input data, RF is a good start.
logistic regression will be good if you know expertly about the data and how to properly specify the equation. Or somehow want to engineer how the fit/prediction works. The explicit form of GLM specification will allow you to do that.