RegSDCcomp | R Documentation |
Implementation of equation 8 in the paper.
RegSDCcomp(
y,
compCorr = NA,
x = NULL,
doSVD = FALSE,
makeunique = TRUE,
ensureIntercept = TRUE
)
y |
Matrix of confidential variables |
compCorr |
Required component score correlations (possibly recycled) |
x |
Matrix of non-confidential variables |
doSVD |
SVD when TRUE and QR when FALSE |
makeunique |
Parameter to be used in GenQR |
ensureIntercept |
Whether to ensure/include a constant term. Non-NULL x is subjected to |
NA component score correlation means independent random. Input matrices are subjected to EnsureMatrix
.
Generated version of y
Øyvind Langsrud
x <- matrix(1:10, 10, 1)
y <- matrix(rnorm(30) + 1:30, 10, 3)
# Same as IPSO (RegSDCipso)
RegSDCcomp(y, NA, x)
# Using QR and SVD
yQR <- RegSDCcomp(y, c(0.1, 0.2, NA), x)
ySVD <- RegSDCcomp(y, c(0.1, 0.2, NA), x, doSVD = TRUE)
# Calculation of residuals
r <- residuals(lm(y ~ x))
rQR <- residuals(lm(yQR ~ x))
rSVD <- residuals(lm(ySVD ~ x))
# Correlations for two first components as required
diag(cor(GenQR(r)$Q, GenQR(rQR)$Q))
diag(cor(GenQR(r, doSVD = TRUE)$Q, GenQR(rSVD, doSVD = TRUE)$Q))
# Identical covariance matrices
cov(yQR) - cov(ySVD)
cov(rQR) - cov(rSVD)
# Identical regression results
summary(lm(y[, 1] ~ x))
summary(lm(yQR[, 1] ~ x))
summary(lm(ySVD[, 1] ~ x))
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