Questions with estimate.wqs -- (1) bootstrap convergence error, (2) setting minimum weights


I am trying to develop a weighted quantile sum regression-based index and have been using the estimate.wqs function ( I am running into two issues, and I have not been able to find a solution. I have a dataset of 108 observations and am including 8 domains as the independent variables to be weighted. Here is my code (modeled directly after the above documentation), and my issues/questions are described below it:

if( requireNamespace("wqs", quietly = TRUE) ){
Wa=estimate.wqs(y=df.2$LEB, X=df.2[,2:9],
B = 100, verbose=(TRUE))

message("you need to install the package wqs for this example")

Issue 1: Bootstrap convergence error

I received this error message: "5 bootstrap regression estimates in the training sets have not converged. Proceed results with caution." A colleague suggested I adjust the iterations within a given bootstrap, but I am not sure how to do that within this code. Does anyone know, or are there any other thoughts on how to ensure that all boostrap regressions converge?

Issue 2: Setting minimum weights

I would like to set a minimum weight of 0.05 for each of the 8 variables. How can I go about doing that?

Thank you for your help!

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