WLS: Computes a weighted least squares linear regression on...
In UStatBookABSC: A Companion Package to the Book "U-Statistics, M-Estimation and Resampling"
Description
Usage
Arguments
Value
Examples
Description
Computes a weighted least squares linear regression
on possibly multivariate responses
Usage
1
WLS(Y, X, W)
Arguments
Y
a numeric matrix, to act as response
X
a numeric matrix, to act as covariates
W
a numeric matrix, to act as weights
Value
a vector of regression coefficients
Examples
1
2
3
4
5
6
## Not run:
DataY = cbind(CCU12_Precip$Precip, CCU12_Precip$TMax);
DataX = cbind(rep(1, length(CCU12_Precip$Precip)), CCU12_Precip$TMin)
BetaHat.New = WLS(DataY, DataX)
## End(Not run)
UStatBookABSC documentation built on May 2, 2019, 8:24 a.m.
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Description Usage Arguments Value Examples
Description
Computes a weighted least squares linear regression on possibly multivariate responses
Usage
1 | WLS(Y, X, W)
|
Arguments
Y |
a numeric matrix, to act as response |
X |
a numeric matrix, to act as covariates |
W |
a numeric matrix, to act as weights |
Value
a vector of regression coefficients
Examples
1 2 3 4 5 6 | ## Not run:
DataY = cbind(CCU12_Precip$Precip, CCU12_Precip$TMax);
DataX = cbind(rep(1, length(CCU12_Precip$Precip)), CCU12_Precip$TMin)
BetaHat.New = WLS(DataY, DataX)
## End(Not run)
|
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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