mlr2: Multiple Linear Regression with Influence Diagnostics
In nmw: Understanding Nonlinear Mixed Effects Modeling for Population Pharmacokinetics
mlr2 R Documentation
Multiple Linear Regression with Influence Diagnostics
Description
Performs multiple linear regression and computes influence diagnostics
including DFFITS, DFBETAS, Cook's Distance, COVRATIO, and R-Student.
Usage
mlr2(y, x.raw, standardize = 0)
Arguments
y
numeric vector of response
x.raw
data.frame of predictors
standardize
integer, standardization method (0=none, 1=center, 2=scale by mean, 3=z-score)
Value
list with model estimates and influence diagnostics
nmw documentation built on May 8, 2026, 9:07 a.m.
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| mlr2 | R Documentation |
Multiple Linear Regression with Influence Diagnostics
Description
Performs multiple linear regression and computes influence diagnostics including DFFITS, DFBETAS, Cook's Distance, COVRATIO, and R-Student.
Usage
mlr2(y, x.raw, standardize = 0)
Arguments
y |
numeric vector of response |
x.raw |
data.frame of predictors |
standardize |
integer, standardization method (0=none, 1=center, 2=scale by mean, 3=z-score) |
Value
list with model estimates and influence diagnostics
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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