Ahmet - In a logistic regression model, fitted probabilities make sense for individual cases (rows in the data set), as well as for future cases (predictions) for which no outcome (success or failure) has been observed yet. Fitted probabilities are calculated from the matrix formula:
Pr[success] = exp( X %*% beta) / (1 + exp( X %*% beta) where X is an [n x (p+1)] matrix, containing all p predictor variables as columns, preceded by a column of 1s for the intercept, and beta is the [(p+1) x 1] vector of logistic regression coefficients. One can interpret the sign and the magnitude of an individual regression coeffient by saying that an increase of 1 unit in predictor variable [i] will increase or decrease the odds of success by a multiplier of exp(beta[i]). When beta[i] > 0 the odds increase, because exp(beta[i]) > 1, and when beta[i] < 0 the odds decrease, because exp(beta[i]) < 1. I hope this explanation helps. - tom blackwell - u michigan medical school - ann arbor - On Tue, 3 Jun 2003, orkun wrote: > Hello > > in logistic regression, > I want to know that it is possible to get probability values of each > predictors by > using following formula for each predictor one by one (keeping constant > the others) > <<< exp(coef)/(1+exp(coef)) >>> > > thanks in advance > Ahmet Temiz ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
