search for: logreg

I have been trying to run a logistic regression using a number of studies. Below is the syntax, error message & data. Any advice regarding what I am doing wrong or solutions are appreciated, regards Bob Green > logreg <- read.csv("c:\\logregtest.csv",header=T) > attach(logreg) > names(logreg) [1] "medyear" "where" "who" "dxbroad" "firstep" "standard" [7] "age" "sex" "successes...

I really like these Trellis graphics but how do I get this code to skip the missing? logreg<-read.csv("logreg.csv", header=TRUE, sep=",", na.string=" ") attach(logreg) bwplot(yesno~bc_pcb_tot |varlist, data=logreg, main="Box Cox PCB transformation", auto.key=TRUE, fontfamily = "HersheySans" ) Dean Sonneborn M.S. Public Health Scien...

...has all the rows that are not hemlock species replaced with #"NA"s. hemhem=glm(hempresence~logarea, family=binomial(logit), data=hemdata) hemyb=glm(hempresence~logarea, family=binomial(logit), data=birchdata) hemsm=glm(hempresence~logarea, family=binomial(logit), data=mapledata) attach(logreg) #logreg is the full dataset plot(logarea, hempresence, xlab = "Surface area of log (m2)", ylab="Probability of hemlock seedling presence", type="n", font.lab=2, cex.lab=1.5, axes=TRUE) lines(logarea,fitted(hemhem), lty=1, lwd=2) lines(logarea,fitted(hemyb), lty=&quo...

...ackage in a list, but got an error message "Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1". All the response variables have value of 0 or 1. It worked well for summary and anova but not for logistic.display in the loop. Thanks for any help, Tom logreg1<-vector("list", length(l)) logreg.anov1<-vector("list",length(l)) logreg.summ1<-vector("list",length(l)) logreg.conf1<-vector("list",length(l)) for (i in c(13:16)){ logreg1[[i-x]]<-glm(dat[,i]~group + age, family=binomial, data=Ndat) log...

Hi, I was trying to plot the logistic regression from a regression "logreg" I just ran. I downloaded the "car" package from the R website and went to Packages -> install package from local zip file I checked in my library file and the package is there. I restarted R. I then ran the command: reg.line(logreg,col=palette()[2], lwd=2, lty=1) And I get the e...

...vels. I see r-help inquiries regarding odds ratios for what looked like a continuous predictor variable. I was wondering how to get the pairwise odds ratios for comparisons of levels of a categorical predictor variable. I can't seem to get the correct output using: > sp.glm=glm(cohort$logreg~cohort$hab, family=binomial) > summary(sp.glm) This gives me the coefficient for the effect of hab overall, but not the coefficients for the comparisons of each of the levels. I suspect this has something to do with the contrasts statement, but I can't figure it out, and would be very a...

Hi there: recently i try to use LogicReg package for a tree model(logistics fit ) . i list my code and error below: > dim(model.dat) [1] 48000 745 > fit1 <- logreg(resp = model.dat[,745], bin=model.dat[, 9:700], type = 3, select = 3, ntrees = c(1,2), nleaves=c(1,7), ) Insufficient declaration LGCn1MAX in logreg() is 20000 LGCn1MAX should be at least 48000 Please fix and recompile.... i find the help file which said i need to reset them in *slogic.f* a...

...sso regression? ? Looks like this is the grouping part: ? index<-c(NA,) ? but I'm not sure how to specify the df for the variables past the NA for the intercept. ? Once that's defined the penalty can be specified: ? lambda <- lambdamax(x, y = y, index = index, penscale = sqrt, model = LogReg()) * 0.5^(0:5)? In my case I'd use LinReg for the model.? ? Then the model: ? fit <- grplasso(x, y = y, index = index, lambda = lambda, model = LogReg(), penscale = sqrt, control = grpl.control(update.hess = "lambda", trace = 0)) ? again using LinReg for the model. This can be pl...

Hello. I have run the command imp<-mice(mydata, im=c("","pmm","logreg","logreg"),m=5)  for a variable with no missing data, a numeric one and two variables with binary data. I got the following message: There were 37 warnings (use warnings() to see them) > warnings() Warning messages: 1: In any(predictorMatrix[j, ]) ... : coercing argument of t...

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...mes(X) <- paste("case", 1:nrow(X), sep="") # Define expected result: Y = (NOT X2) AND X6 Y <- as.numeric(!X[,2] & X[,6]) # set seed for reproducible test set.seed(19937) # 100 interations too few: some results in single node with |Parameter| < 1 Annealing <- logreg.anneal.control(start = -1, end = -4, iter = 500, update = 50) logicfit <- logreg(resp=Y, bin=X, type = REGRESSION.TYPE<-2, select = FIT.SINGLE.MODEL<-1, ntrees=1, nleaves=2, # force shape of final tree...

...lt;- paste("X", 1:ncol(X), sep="") rownames(X) <- paste("case", 1:nrow(X), sep="") # Define expected result: Y = (NOT X1) AND X5 Y <- as.numeric(!X[,1] & X[,5]) z<-as.numeric(runif(50)) model.dat<-cbind(X,z,Y) set.seed(12345) Annealing <- logreg.anneal.control(start = 4, end = -4, iter = 1000, update = 50) logicfit <- logreg(resp=model.dat[,10], bin=model.dat[,1:8],sep=model.dat[,9], type = 3, select = 2, ntrees=2, nleaves=3, anneal.control=An...

R 2.7.2 PPC Mac OS X 10.4.11 library mice 1.13.1 I try to use mice for multivariate data imputation. My variables are numeric, factors, count data, ordered factors. First I created a vector for the methods to use with each variable ImpMethMice<-c(rep("logreg", 62), rep("polyreg",1), rep("norm",12), rep("polyreg",12)) next step was Test<-mice(df, im=ImpMethMice) I got the following error message: iter imp variable 1 1 variablenameFehler in impute.logreg(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,...

...but which in essence actually have no restrictions. So if one needs to change them, he sure can, only that he has to change a file with an .f or .ff extension (I believe that it stands for Fortran code, but I am not sure) and then recompile it. http://finzi.psych.upenn.edu/R/library/LogicReg/html/logreg.html I have found several posts in the mailing-archive, but they seem to deal with Windows. And I have a Gentoo-based distro. I have also found a manual "Installation and Administration". But it all seems to be destined for developers... and usually talks about creating your own package...

...ies for Test Data # >>>> >>>>########################### >>>> >>>> >>>>>New.Data <- data.frame(predictors.train1=predictors.test[,1], >>>>>predictors.train2=predictors.test[,2]) >>>>> >>>>>logreg.pred.prob.test <- >>>> >>>>predict.glm(log.reg,New.Data,type="response") >>>> >>>> >>>>>logreg.pred.prob.test >>>> >>>>Instead, use: >>>> >>>> test <- data.frame(predictor...

...;status','age','sex','hepmeg','platelet', 'trt', 'trig')] d[d==-9] <- NA d[,c(4,5,7)] <- lapply(d[,c(4,5,7)], FUN=as.factor) str(d) imp <- mice(d, m=10, maxit=10, diagnostics=T, seed=500, defaultImputationMethod=c('norm', 'logreg', 'polyreg')) fit <- cox.mids(Surv(time,status) ~ age + sex + hepmeg + platelet + trt + trig, imp) pool(fit) ------------ I have looked at the "pool" function but cannot figure out what I have done wrong. Would really appreciate any help with this. Thanks, Brant Inm...

...is. To illustrate my problem I have created a dataset, which consists of 1 continuous and 1 binary variable. The continuous variable is complete and the binary variable is partially observed. I am able to impute using the MICE command where the imputation methods is logistic regression (option logreg). However I have been unsuccessful when I've tried to impute the binary variable using linear discriminant analysis (option lda) Instead I get the following error message "Error in colMeans(as.matrix(imp[[visitSequence[j]]])) : 'x' must be numeric" I can however impute usi...

...0 5 0 0 6 1 NA 7 1 NA 8 0 1 9 0 0 10 0 0 11 0 NA 12 0 0 13 0 NA 14 0 0 15 0 0 16 0 1 17 0 0 18 0 0 19 0 0 20 0 NA I tried calling the function mice.impute.logreg and mi.dichotomous - both giving me errors in return. I'm quite sure I'm doing it incorrectly, so, I would really appreciate it if someone can show me how. Thanks in advance! :) Cheers,Yen [[alternative HTML version deleted]]

...emed involved every time. The "textbook example" (example for amelia function) runs perfectly. Package "mice" (Stef van Buuren & Karin Groothuis-Oudshoorn) : > system.time(DataTest.mice<-mice(DataTest,m=5)) iter imp variable 1 1 cadredpErreur dans mice.impute.logreg(c(1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, : dims [produit 28] ne correspond pas ? la longueur de l'objet [31] De plus : Warning message: In beta + rv %*% rnorm(ncol(rv)) : la taille d'un objet plus long n'est pas multiple de la taille d'un objet plus court Timing stopped at: 0...