LOBPCG Eigensolver Control Options

Construct a validated list of control options for the LOBPCG eigensolver used by pca_imp(). Most users do not need to call this directly.

Usage

lobpcg_control(warmup_iters = 10L, tol = 1e-09, maxiter = 20)

Arguments

warmup_iters

Integer. Number of warm-up iterations before the main LOBPCG solve. Must be non-negative.

tol

Numeric. Convergence tolerance for the LOBPCG eigensolver. Must be non-negative and finite.

maxiter

Integer. Maximum number of LOBPCG iterations. Must be non-negative. In pca_imp(), maxiter must be positive when solver = "auto" or solver = "lobpcg". Use solver = "exact" to force the exact solver.

Value

A named list of class "slideimp_lobpcg_control" containing warmup_iters, tol, and maxiter.

Examples

set.seed(123)
obj <- sim_mat(10, 10)$input

# Use all defaults
lobpcg_control()
#> $warmup_iters
#> [1] 10
#> 
#> $tol
#> [1] 1e-09
#> 
#> $maxiter
#> [1] 20
#> 
#> attr(,"class")
#> [1] "slideimp_lobpcg_control"
#> attr(,"explicit")
#> warmup_iters          tol      maxiter 
#>        FALSE        FALSE        FALSE 

# Override a single option
lobpcg_control(maxiter = 50)
#> $warmup_iters
#> [1] 10
#> 
#> $tol
#> [1] 1e-09
#> 
#> $maxiter
#> [1] 50
#> 
#> attr(,"class")
#> [1] "slideimp_lobpcg_control"
#> attr(,"explicit")
#> warmup_iters          tol      maxiter 
#>        FALSE        FALSE         TRUE 

# Force the exact solver from pca_imp()
pca_imp(obj, ncp = 2, solver = "exact")
#> Method: PCA imputation
#> Dimensions: 10 x 10
#> 
#>          feature1   feature2  feature3  feature4  feature5   feature6
#> sample1 0.5784798 0.06296727 0.3155309 0.1199980 0.1807391 0.31919912
#> sample2 0.4991812 0.44077231 0.2120510 0.3257524 0.1919521 0.14843947
#> sample3 0.7709271 0.75477764 1.0000000 0.5099311 0.6027900 0.75450992
#> sample4 0.5068375 0.37347042 0.6018860 1.0000000 0.5850265 0.08171420
#> sample5 0.4165827 0.42553421 0.6024750 0.7728095 0.2295133 0.08343629
#> sample6 1.0000000 0.93942190 0.9867413 0.5851340 1.0000000 1.00000000
#> # Showing 6 of 10 rows and 6 of 10 columns

# Pass directly to pca_imp()
pca_imp(obj, ncp = 2, lobpcg_control = lobpcg_control(tol = 1e-9))
#> Method: PCA imputation
#> Dimensions: 10 x 10
#> 
#>          feature1   feature2  feature3  feature4  feature5   feature6
#> sample1 0.5784798 0.06296727 0.3155309 0.1199980 0.1807391 0.31919912
#> sample2 0.4991812 0.44077231 0.2120510 0.3257524 0.1919521 0.14843947
#> sample3 0.7709271 0.75477764 1.0000000 0.5099311 0.6027900 0.75450992
#> sample4 0.5068375 0.37347042 0.6018860 1.0000000 0.5850265 0.08171420
#> sample5 0.4165827 0.42553421 0.6024750 0.7728095 0.2295133 0.08343629
#> sample6 1.0000000 0.93942190 0.9867413 0.5851340 1.0000000 1.00000000
#> # Showing 6 of 10 rows and 6 of 10 columns

# Or use a named list
pca_imp(obj, ncp = 2, lobpcg_control = list(maxiter = 50))
#> Method: PCA imputation
#> Dimensions: 10 x 10
#> 
#>          feature1   feature2  feature3  feature4  feature5   feature6
#> sample1 0.5784798 0.06296727 0.3155309 0.1199980 0.1807391 0.31919912
#> sample2 0.4991812 0.44077231 0.2120510 0.3257524 0.1919521 0.14843947
#> sample3 0.7709271 0.75477764 1.0000000 0.5099311 0.6027900 0.75450992
#> sample4 0.5068375 0.37347042 0.6018860 1.0000000 0.5850265 0.08171420
#> sample5 0.4165827 0.42553421 0.6024750 0.7728095 0.2295133 0.08343629
#> sample6 1.0000000 0.93942190 0.9867413 0.5851340 1.0000000 1.00000000
#> # Showing 6 of 10 rows and 6 of 10 columns