grpTheilSen: Grouped Theil-Sen Robust Regression
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities
Description
Usage
Arguments
Value
References
View source: R/suffDimReduct2.R
Description
This is analagous to the grpOLS function, except the robust
Theil-Sen estimator replaces ordinary least squares.
Usage
1
grpTheilSen(X, Y, idx, ranks = NULL, tol = 1e-05, maxiter = 100)
Arguments
X
a model matrix (must be numeric, not categorical)
Y
the outcome variable (must be numeric, not categorical)
idx
group id labels
ranks
an indicator for each group whether the covariates of said group are active.
Value
an sdr object
References
Liu, Y., Chiaromonte, F. and Li, B. (2017) Structured Ordinary Least Squares: A Sufficient Dimension Reduction approach for regressions with partitioned predictors and heterogeneous units. Biom, 73: 529-539. doi:10.1111/biom.12579
abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.
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Description Usage Arguments Value References
View source: R/suffDimReduct2.R
Description
This is analagous to the grpOLS function, except the robust
Theil-Sen estimator replaces ordinary least squares.
Usage
1 | grpTheilSen(X, Y, idx, ranks = NULL, tol = 1e-05, maxiter = 100)
|
Arguments
X |
a model matrix (must be numeric, not categorical) |
Y |
the outcome variable (must be numeric, not categorical) |
idx |
group id labels |
ranks |
an indicator for each group whether the covariates of said group are active. |
Value
an sdr object
References
Liu, Y., Chiaromonte, F. and Li, B. (2017) Structured Ordinary Least Squares: A Sufficient Dimension Reduction approach for regressions with partitioned predictors and heterogeneous units. Biom, 73: 529-539. doi:10.1111/biom.12579
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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