Fast computation of several simple linear regression, where the outcome is analyzed with several marginal analyses, or where several outcome are analyzed separately, or a combination of both.
plr(y, x, addintercept = TRUE)
# S3 method for class 'numeric'
plr(y, x, addintercept = TRUE)
# S3 method for class 'matrix'
plr(y, x, addintercept = TRUE)a data frame (if Y is a vector) or list of data frames (if Y is a matrix)
mfastLmCpp
N <- 1000 # Number of observations
Nx <- 20 # Number of independent variables
Ny <- 80 # Number of dependent variables
# Simulate outcomes that are all standard Gaussians
Y <- matrix(rnorm(N*Ny), ncol=Ny)
X <- matrix(rnorm(N*Nx), ncol=Nx)
plr(Y, X)
#> [[1]]
#> coefficients stderr tstat
#> 1 0.033151356 0.03080686 1.07610292
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#>
#> [[2]]
#> coefficients stderr tstat
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#>
#> [[3]]
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#>
#> [[4]]
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#>
#> [[5]]
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#>
#> [[6]]
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#>
#> [[7]]
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#>
#> [[8]]
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#> [[9]]
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#> [[10]]
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