[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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