coefficient of logistic regression
Thomas W Blackwell wrote:
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
Dear Mr. Fox
thank you very much all.
So, using the formula -exp(coef)/(1+exp(coef))- for getting probability
of each predictor is correct.
Because of related to your answer. I ask you directly if you don't mind
I studied several ways after my email.
I wonder whether pgeo<-predict.glm(glm.ob,type="terms")
gives same result with probability value I asked before.
I tried on it. But it gives "Error in rep(1/n,n) %*%
model.matrix(object): non conformable
arguments" .
could you tell me why ?
cordially
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