search for: glinearmodel

...Although this variable is continuous, the value of this variable for many items in this particular analysis is 0. The partitioning criterion is self-reported ability in a related area. > mob1 <- mob( correct ~ circular.mean | srp.dimension, control=mob_control(alpha=.001), model=glinearModel, family=binomial() ) > plot(mob1) Error in cut.default(x, breaks = breaks, include.lowest = TRUE) : 'breaks' are not unique The same persists if I specify either a desired number of breaks, or explicit breakpoints (e.g. breaks=3 or breaks=c(-0.1,0.1,0.5)). I guess this is to...

Dear useRs: I try to use mob from the party package (thanks Achim and Co.!) to model based recursive partition a data set. The model is a logistic regression specified with model=glinearModel and family=binomial(). Running mob results in a few warnings of the type: In glm.fit ... algorithm did not converge. As I speculate that this may be due to an insufficient number of iterations I am wondering if any of you knows how to pass arguments to glm.fit from within mob (e.g., epsilon and m...

...y variable and I should use for modelling a logistic regression (family=binomial). I read in the "MOB" Vignette that I could use a formula like this if I would like to have only partitioning variables apart from the response. Test.mob<-mob(Resp~1|Var1+Var2+...., data=dataframe, model=glinearModel, family=binomial()) but this gives me back an error-message: Fehler in `[.data.frame`(x, r, vars, drop = drop) : undefined columns selected Error in `[.data.frame`(x, r, vars, drop = drop) : undefined columns selected But Var1, Var2 and Resp are in my dataframe. Why do I get this error?...

...he relationship between the probability of being a student and number of siblings (alive). However, I need to include a number of relevant covariates. My code is below: fm3 <- mob(Student ~ age + alive + sex2 + cwa + cha + cym | Religion+Servant + Literacy, control = ctrl, data = samp2, model = glinearModel, family =binomial()) plot(fm3, tp_args = list(which = "alive"),tnex = 2, type = "simple" ) Which works fine. However, the plots generated do not show the conditional relationship between the dependent and independent variable at each node. Basically, there are spinogram leafs a...