Re: [R] coefficient of logistic regression

[orkun]

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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