Nothing
best.reg <-
function (x)
{
est.reg1 <- function(x, col_indep) {
cols <- ncol(x)
rows <- nrow(x)
estimated_matrix <- matrix(ncol = cols, nrow = rows)
if (col_indep == 1) {
deps <- 2:cols
}
else if (col_indep == cols) {
deps <- 1:(cols - 1)
}
else {
deps <- c(1:(col_indep - 1), (col_indep + 1):cols)
}
ndeps <- length(deps)
indep <- x[, col_indep]
rcoefs <- numeric()
for (i in 1:ndeps) {
vari <- deps[i]
lm_fit <- lm(log10(x[, vari]) ~ log10(indep))
lm_sum <- summary.lm(lm_fit)
rcoefs[i] <- sqrt(lm_sum$r.squared)
}
ranks <- rank(rcoefs)
newindep <- indep
for (m in 1:length(deps)) {
a <- m - 1
whichvari <- ifelse(ranks == (length(rcoefs) - a),
1, 0)
strongest <- sum(deps * whichvari)
indeps_lm <- lm(log10(newindep) ~ log10(x[, strongest]))
indeps_coef <- indeps_lm$coefficients
logestimate_indep <- indeps_coef[1] + indeps_coef[2] *
log10(x[, strongest])
estimate_indep <- 10^logestimate_indep
missings <- ifelse(is.na(newindep), 1, 0)
nonmissings <- ifelse(is.na(newindep), 0, 1)
fillnonmissing <- ifelse(is.na(newindep), 0, newindep)
fillmissing <- ifelse(is.na(newindep), estimate_indep,
0)
newindep <- fillmissing + fillnonmissing
}
return(newindep)
}
estimated.matrix <- matrix(ncol = ncol(x), nrow = nrow(x))
for (i in 1:ncol(x)) {
estimated.matrix[, i] <- est.reg1(x, i)
}
return(estimated.matrix)
}
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