Displaying 2 results from an estimated 2 matches for "presabs".
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presyabs
2005 Jul 12
1
Design: predict.lrm does not recognise lrm.fit object
...model, before trying to
make predictions against an independent set of data using predict.lrm with
the reduced model. I wouldn't normally use this method, but I'm
contrasting the results with an AIC/MMI approach. The script contains:
# Determine full logistic regression
lrm_logist = lrm(PresAbs ~ Size + X2ndpc + soil + AAR + tjan.jun,
data=training)
# Backward selection of variables in model
lrm_stp = fastbw(lrm_logist, rule="p", sls=0.05)
# Fit reduced model
lrm_reduced = lrm.fit(training[,lrm_stp$parms.kept[-1]], training$PresAbs)
# Predict using parameters from reduced model...
2009 Jun 06
1
large numbers of observations using ME() of spdep
...coords, nnmult = 12);
tai.gab.nb<-graph2nb(TaiminGabrielGraph,sym=TRUE);
nbtaim_distsg <- nbdists(tai.gab.nb, coords);
nbtaim_simsg <- lapply(nbtaim_distsg, function(x) (1-((x/(4*50))^2)) );
MEtaig.listw <- nb2listw(tai.gab.nb, glist=nbtaim_simsg, style="B");
sevmtaig <- ME(Presabs
~Age+Curv+Zon2005+ZoneMeiji+Wi+Sol+Slope+Seadist+Elev+Basin,data=taimin,
family=binomial,listw=MEtaig.listw)
Any help is welcome!
Thanks
Lucero Mariani, Yokohama National University, Japan.