View source: R/rms_SandwichAddon.R
hatvalues.ols | R Documentation |
The hat matrix comes from the residual definition:
epsilon = y - Xbeta_hat = (I_n - X(X'X)X')y = (I_n - H)y
where the H is called the hat matrix since
Hy = y_hat
. The hat
values are actually the diagonal elements of the matrix that sum up
to p (the rank of X, i.e. the number of parameters + 1).
See ols.influence()
.
## S3 method for class 'ols' hatvalues(model, ...)
model |
The ols model fit |
... |
arguments passed to methods. |
vector
# 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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