Description Usage Arguments Value See Also Examples
Computes precision weights that account for heteroscedasticity in RNAseq count data based on nonparametric local linear regression estimates.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  sp_weights(
y,
x,
phi,
use_phi = TRUE,
preprocessed = FALSE,
doPlot = FALSE,
gene_based = FALSE,
bw = c("nrd", "ucv", "SJ", "nrd0", "bcv"),
kernel = c("gaussian", "epanechnikov", "rectangular", "triangular", "biweight",
"tricube", "cosine", "optcosine"),
exact = FALSE,
transform = TRUE,
verbose = TRUE,
na.rm = FALSE
)

y 
a numeric matrix of size 
x 
a numeric matrix of size 
phi 
a numeric design matrix of size 
use_phi 
a logical flag indicating whether conditional means should be conditioned
on 
preprocessed 
a logical flag indicating whether the expression data have
already been preprocessed (e.g. log2 transformed). Default is 
doPlot 
a logical flag indicating whether the meanvariance plot should be drawn.
Default is 
gene_based 
a logical flag indicating whether to estimate weights at the genelevel.
Default is 
bw 
a character string indicating the smoothing bandwidth selection method to use. See

kernel 
a character string indicating which kernel should be used.
Possibilities are 
exact 
a logical flag indicating whether the nonparametric weights accounting
for the meanvariance relationship should be computed exactly or extrapolated
from the interpolation of local regression of the mean against the
variance. Default is 
transform 
a logical flag indicating whether values should be transformed to uniform
for the purpose of local linear smoothing. This may be helpful if tail observations are sparse and
the specified bandwidth gives suboptimal performance there. Default is 
verbose 
a logical flag indicating whether informative messages are printed
during the computation. Default is 
na.rm 
logical: should missing values (including 
a n x G
matrix containing the computed precision weights.
1 2 3 4 5 6 7 8 9 
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