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my function is glm(a~log(b)+c+d+e,family=binomial,data=f)->aa I want to extract the original data set of aa. How to do it ? You may suggest the model.frame() function. In fact ,i have tried it. model.frame returns a data frame of containing a,log(b) NOT b,c,d,e I want to extract a data frame containing a,b,c,d,e,which is exactly the same as "f" How can I achieve this

...the object created by the call to glm(): form <- qq["formula"] or equivalently form <- qq$formula then apply the function all.vars() to the result, and return the first element variables.in.formula <- all.vars(form) variables.in.formula[1] # will be the response variable leffgh wrote: > > for example > glm(a~b+c+d,data=eee)->qq > > Is there a function able to return the name of the response variable? > suppose "ff" is the function satisfying this need, > ff(qq) then "a" (Not the value of a) will be returned. > could you tel...

I have a vector whose length is nearly 70 thousand. I need randomize it for 1000 times . for randomizing , I mean ,the elements of the vector remain intact while their order in the vector get changed randomly. I have written a function which seems to be able to solve short vectors , but waste a lot of time when dealing with the the long vector mentioned above. -- View this message in context:

Dear list: After reading the following two links: http://luna.cas.usf.edu/~mbrannic/files/regression/Logistic.html http://www.tufts.edu/~gdallal/logistic.htm I've known the mathematical basis for logistic regression.However I am still not so sure about the "logit " For a categorical independent variable, It is easy to understand the procedures how "log

dear List: glm(a~b+c,family=binomial,data=x)->fit deviance(fit) returns the same as the residual deviance. I don't not know much about logistic regression.Some book tells that: " Deviance (likelihood ratio statistic): Deviance = -2log( likelihoodof the currentmodel /likelihoodof thesaturated model) Note: (1). The current model is the model of interest. (2). The saturated model

Dear friends : using the "glm" function and setting family=binomial, I got a list of coefficients. The coefficients reflect the effects of predicted variables on the probability of the response to be "1". My response variable consists of "A" and "D" . I don't know which level of the response was set to be 1. is the first element of the response set

Dear friends: I am a new R user, and not very good at statistics and maths I find it difficult to find the minimum of this item: ((p*(b-1)-1)*(p+1)^(b-1)+1)/p^2 It seems that the method optim can find the minimum. I tried several times , but R constanly tells that " failure to find b or p" Would you please give some suggestions? Thanks in advance. Best

Dear all: "predict.glm" provides an example to perform logistic regression when the response variable is a tow-columned matrix. I find some paradox about the degree of freedom . > summary(budworm.lg) Call: glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375