fls.fit: FLS fit
In GediminasB/fls: Time-Varying Linear Regression via Flexible Least Squares
Description
Usage
Arguments
Value
References
Description
This function is used to fit Time-Varying Linear Regression via Flexible least squares (FLS) as discribed in R. Kalaba and L. Tesfatsion (1989).
Usage
1
Arguments
X
design matrix of dimensuin n * K.
y
vector of observations of length n.
mu
parameter controling relative weight of sum of dynamic errors (r_D^2) vs sums of squared residual measurement errors (r_M^2).
smooth
logical. If TRUE, a smoothed estimate is provided.
Value
A n * K matrix coefficient estimates.
References
\insertRefKALABA19891215fls
GediminasB/fls documentation built on Nov. 13, 2019, 12:43 a.m.
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Description Usage Arguments Value References
Description
This function is used to fit Time-Varying Linear Regression via Flexible least squares (FLS) as discribed in R. Kalaba and L. Tesfatsion (1989).
Usage
1 |
Arguments
X |
design matrix of dimensuin n * K. |
y |
vector of observations of length n. |
mu |
parameter controling relative weight of sum of dynamic errors (r_D^2) vs sums of squared residual measurement errors (r_M^2). |
smooth |
logical. If TRUE, a smoothed estimate is provided. |
Value
A n * K matrix coefficient estimates.
References
\insertRefKALABA19891215fls
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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