model_lin: Linear regression
In ArtemSokolov/gelnet: Generalized Elastic Nets
Description
Usage
Arguments
Details
Value
Description
Defines a linear regression task
Usage
1
Arguments
y
n-by-1 numeric vector of response values
a
n-by-1 vector of sample weights
nonneg
set to TRUE to enforce non-negativity constraints on the weights (default: FALSE)
fix_bias
set to TRUE to prevent the bias term from being updated (default: FALSE)
Details
The objective function is given by
\frac{1}{2n} ∑_i a_i (y_i - (w^T x_i + b))^2 + R(w)
Value
A GELnet task definition that can be combined with gelnet() output
ArtemSokolov/gelnet documentation built on Sept. 13, 2019, 4:01 a.m.
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Description Usage Arguments Details Value
Description
Defines a linear regression task
Usage
1 |
Arguments
y |
n-by-1 numeric vector of response values |
a |
n-by-1 vector of sample weights |
nonneg |
set to TRUE to enforce non-negativity constraints on the weights (default: FALSE) |
fix_bias |
set to TRUE to prevent the bias term from being updated (default: FALSE) |
Details
The objective function is given by
\frac{1}{2n} ∑_i a_i (y_i - (w^T x_i + b))^2 + R(w)
Value
A GELnet task definition that can be combined with gelnet() output
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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