fitWLS: Weighted Least Squares
In zrmacc/BinReg: BinReg
Description
Usage
Arguments
Value
Description
Fits the following weighted least squares model:
y_{i}=x_{i}'β+ε_{i}. Here, the subject-specific residual is
normally distributed with mean zero and variance σ^{2}/w_{i}.
w_{i} is a known, subject-specific weight, and σ is a
common scale parameter.
Usage
1
fitWLS(y, X, w)
Arguments
y
Response vector.
X
Design matrix.
w
Weight vector.
Value
List containing the following:
Beta
Regression coefficient.
V
Outcome variance.
Ibb
Information matrix for beta.
Resid
Outcome residuals.
zrmacc/BinReg documentation built on May 9, 2019, 8:08 a.m.
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Description Usage Arguments Value
Description
Fits the following weighted least squares model: y_{i}=x_{i}'β+ε_{i}. Here, the subject-specific residual is normally distributed with mean zero and variance σ^{2}/w_{i}. w_{i} is a known, subject-specific weight, and σ is a common scale parameter.
Usage
1 | fitWLS(y, X, w)
|
Arguments
y |
Response vector. |
X |
Design matrix. |
w |
Weight vector. |
Value
List containing the following:
Beta |
Regression coefficient. |
V |
Outcome variance. |
Ibb |
Information matrix for beta. |
Resid |
Outcome residuals. |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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