As missing data is handled a little different for the ols
than for the lm we need to change the estfun to work with the
## S3 method for class 'ols' estfun(x, ...)
A fitted ols model object.
arguments passed to methods.
I have never worked with weights and this should probably be checked as this just uses the original estfun.lm as a template
matrix A matrix containing the empirical estimating functions.
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# Generate some data n <- 500 x1 <- runif(n)*2 x2 <- runif(n) y <- x1^3 + x2 + rnorm(n) library(rms) library(sandwich) dd <- datadist(x1, x2, y) org.op <- options(datadist = "dd") # Main function f <- ols(y ~ rcs(x1, 3) + x2) # Check the bread bread(f) # Check the HC-matrix vcovHC(f, type="HC4m") # Adjust the model so that it uses the HC4m variance f_rob <- robcov_alt(f, type="HC4m") # Get the new HC4m-matrix # - this function just returns the f_rob$var matrix vcov(f_rob) # Now check the confidence interval for the function confint(f_rob) options(org.op)
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