search for: logitregvr

...g() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here is the function, logitregVR <- function(x, y, wt = rep(1, length(y)), intercept = T, start = rep(0, p), ...) { #-------------------------------------------# # A function to be minimized (or maximized) # #-------------------------------------------# fmin <- function(beta, X, y, w) { p <- plogis(X %*% beta) #p &lt...

...eg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here is the function, logitregVR <- function(x, y, wt = rep(1, length(y)), intercept = T, start = rep(0.1, p), ...) { #-------------------------------------------# # A function to be minimized (or maximized) # #-------------------------------------------# fmin <- function(beta, X, y, w) { p <- plogis(X %*% b...

...g() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here is the function, logitregVR <- function(x, y, wt = rep(1, length(y)), intercept = T, start = rep(0, p), ...) { #-------------------------------------------# # A function to be minimized (or maximized) # #-------------------------------------------# fmin <- function(beta, X, y, w) { p <- plogis(X %*% beta) #p &lt...