assessBsFun: Assess the performance of group-sparse loading estimate
In feiyoung/SpagFainfer: Estimation and inference on high-dimensional group-sparse factor models
Description
Usage
Arguments
Value
Note
Author(s)
See Also
Examples
Description
Evaluate the model selection consistency rate (SCR), F-measure and the smallest canonical correlation.
Usage
1
assessBFun(hB, B0)
Arguments
hB
a p-by-q matrix, the estimated loading matrix.
B0
a p-by-q matrix, the true loading matrix.
Value
return a vecotor with three compoents whose names are scr,fmea, ccorB.
Note
nothing
Author(s)
Liu Wei
See Also
Examples
1
2
3
4
5
6
dat <- gendata(n = 300, p = 500)
res <- gsspFactorm(dat$X)
assessBsFun(res$sphB, dat$B0)
n <- nrow(dat$X)
res <- gsspFactorm(dat$X, lambda1=0.05*n^(1/4), lambda2=9*n^(1/4))
assessBsFun(res$sphB, dat$B0)
feiyoung/SpagFainfer documentation built on April 4, 2020, 5:20 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Note Author(s) See Also Examples
Description
Evaluate the model selection consistency rate (SCR), F-measure and the smallest canonical correlation.
Usage
1 | assessBFun(hB, B0)
|
Arguments
hB |
a |
B0 |
a |
Value
return a vecotor with three compoents whose names are scr,fmea, ccorB.
Note
nothing
Author(s)
Liu Wei
See Also
Examples
1 2 3 4 5 6 | dat <- gendata(n = 300, p = 500)
res <- gsspFactorm(dat$X)
assessBsFun(res$sphB, dat$B0)
n <- nrow(dat$X)
res <- gsspFactorm(dat$X, lambda1=0.05*n^(1/4), lambda2=9*n^(1/4))
assessBsFun(res$sphB, dat$B0)
|
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.