Log.lqr = function(y,x,p=0.5,a=0,b=1,dist = "normal",nu="",gama="",precision = 10^-6,epsilon = 0.001,CI=0.95)
{
cat('\n')
call <- match.call()
cat("Call:\n")
print(call)
cat('\n')
ynovo = log((y - a + epsilon)/(b - y + epsilon))
output = lqr(ynovo,x,p,dist,nu,gama,precision,envelope = FALSE,CI)
cat('\n')
cat('\n')
cat('***\n')
cat('The function below converts the logistic quantile prediction curve (predlog) to the original quantile predicted curve for the bounded response. See example.\n')
cat('\n')
cat('pred = function(predlog,a,b)\n')
cat('{\n')
cat(' return((b*exp(predlog)+a)/(1+exp(predlog)))\n')
cat('}\n')
cat('\n')
cat('The interpretation of the regression coefficients is analogous to the interpretation of the coefficients of a logistic regression for binary outcomes. For references, please check Bottai et.al. (2009) Logistic quantile regression for bounded outcomes.\n')
cat('\n')
return(output)
}
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