search for: fitter

Dear all, I am reading the cch function source code. But I can not understand the following codes. Please help me. What's the fitter here? fitter <- get(method) out <- fitter(tenter = tenter, texit = texit, cc = cc, id = id, X = X, ntot = nn, robust = robust) [[alternative HTML version deleted]]

...l using few covariates, the model converge. Does it mean that this family is fitted differently from other glm? or the number of zeros in my response variable has a limiting factor? Thanks Bruno fit <- glm.nb(pfde~SEX+...., data=data1) Warning messages: 1: Algorithm did not converge in: glm.fitter(x = X, y = Y, w = w, etastart = eta, offset = offset, 2: Algorithm did not converge in: glm.fitter(x = X, y = Y, w = w, etastart = eta, offset = offset, 3: Algorithm did not converge in: glm.fitter(x = X, y = Y, w = w, etastart = eta, offset = offset, 4: Algorithm did not converge in: glm.fit...

hi, I applied coxph to my matrix of 300 samples and 215 variables and got the following error Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: Ran out of iterations and did not converge in: fitter(X, Y, strats, offset, init, control, weights = weights, 26% of time data is censored and here is the re...

Hi, I have a script that takes a subset of genes on a microarray and tries to fit a coxph model to the expression values for each gene. This seems to work fine but in some cases it produces warnings and/or errors. For example: Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not converge In this situation I would like to: 1) Deal with i...

...> kp$kyp <- kp$Kyphosis == "present" > set.seed(7) > imp <- aregImpute( ~ kyp + Age + Start + Number, dat = kp, n.impute = 10, + type = "pmm", match = "closest") Iteration 13 > f <- fit.mult.impute(kyp ~ Age + Start + Number, fitter=glm, xtrans=imp, + family = "binomial", data = kp) Variance Inflation Factors Due to Imputation: (Intercept) Age Start Number 1.06 1.28 1.17 1.12 Rate of Missing Information: (Intercept) Age Start Num...

...observations in my dataset and try to predict the dependent variable from 14 independent variables. My command is as follows clmtest1 <- clogit(Pin~Income+Bus+Pop+Urbpro+Health+Student+Grad+NE+NW+NCC+SCC+CH+SE+MRD+strata(IDD),data=clmdata) However, it produces the following errors: Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning messages: 1: In Surv(rep(1, 4096L), Pinmig) : Invalid status value, converted to NA 2: In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iter...

...l, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter' argument in Design's fit.mult.impute, but it is not clear whether this would work, especially as fit.mult.impute seems only to work with the default settings of its 'fitter' arguments, which rather defeats the purpose of quantile regression. Help!! ____________________________...

I am pretty pleased with baselines I fit to chromatograms using the runquantile() function in caTools(v1.6) when its probs parameter is set to 0.2 and its k parameter to ~1/20th of n (e.g., k ~ 225 for n ~ 4500, where n is time series length). This ignores occasional low- side outliers, and, after baseline subtraction, I can re-adjust any negative values to zero. But runquantile's

Hi, am very new to R and I've written an optim function, but can't get it to work least.squares.fitter<-function(start.params,gr,low.constraints,high.constraints,model.one.stepper,data,scale,ploton=F) { result<-optim(par=start.params,method=c('Nelder-Mead'),fn=least.squares.fit,lower=low.constraints,upper=high.constraints,data=data,scale=scale,ploton=ploton) return(result)...

John, You can make a local version of bootcov which either: deletes these arguments from the call to fitter, or modify the switch statement to include rq.fit, the latter would need to also modify rq() to return a fitFunction component, so the first option is simpler. One of these days I'll incorporate clustered se's into summary.rq, but meanwhile this seems to be a good alternative. Roger u...

...e past (despite having 4 GB in a UNIX OS), I had previously saved it and loaded it back into a clean run of R. The memory overflow did not occur with this approach, but it appears that the NaNs at the "high" end of the survival probability scale are causing fatal errors with the fitter. It appears that for half of the samples, an n of 40 is being used while for the other other half an adaptive sampling size is being used. I hope I have included enough detail below. Any thoughts on a way out of this? Would the full screen output be useful in construction a calibration cur...

While sm4.6ll<-fit.mult.impute(Surv(agesi, si)~partner+ in.love+ pubty+ FPA+ strat(gender),fitter = cph, xtrans = dated.sexrisk2.i, data = dated.sexrisk2, x=T,y=T,surv=T, time.inc=16) runs perfectly using Hmisc, Design and mice under R11 run via Sciviews-K, with library(Design) library(mice) ds2d<-datadist(dated.sexrisk2) options(datadist="ds2d") options(contrasts=c("contr.t...

...am trying to use the bootcov function to estimate the standard errors for some regression quantiles using a cluster bootstrap. However, it seems that bootcov passes arguments that rq.fit doesn't like, preventing the command from executing. Here is an example: e<-bootcov(rq(y~x),clust,B=10,fitter=rq.fit) (where clust is my clustering variable) results in Error in rq.fit.br(x, y, tau = tau, ...) : unused argument(s) (maxit = 15, penalty.matrix = NULL) In contrast, the lm.fit function seems to just ignore these arguments, resulting in the following warning: 10: In fitter(X[obs, , drop =...

R list: I am using a 'for' loop to run a number of different models (stratified by different variables) with coxph. The data becomes sparse when some strata are used causing the model to become unstable. The following error occurs and the analysis is terminated. >Error in fitter(X, Y, strats, offset, init, control, weights = weights, : (converted from warning) Loglik converged before variable 1 ; beta may be infinite. Is there any way to have R skip the models without convergence and apply 'NA' to the results, then go on to the other models? The s...

...shown below. Clearly I have done something wrong as the output of each of the 100 bootstrap values for the regression are exactly the same - there does not appear to be any bootstrap respampling!. What have I done wrong? # Define function to be run. Function will return # beta coefficeint for x. fitter<-function(d) { fit1<-lm(y~x,data=d) print(names(fit1)) print(summary(fit1)) summary(fit1)$coefficients[2,1] } # Define dataframe x<-1:10 y<-x+rnorm(10) d<-data.frame(x,y) #Run boot strap boot(d,fitter,R=100,sim="parametric") John David Sorkin M.D., Ph.D. Chief...

...owing (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of 5. # defaultLinear makes sense for our data. fmp <- fit.mult.impute(Y ~ X1 + X2 ... [for the model of interest], xtrans=f, fitter=lm, data=t1) and all goes well (usually) except that we get the following message at the end of the last step: Warning message: Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use Varcov(fit) to get the correct covariance matrix, sq...

hello, I just installed R-3.4.0 from scratch: $ sudo apt install r-base but when I try > library(survival, lib.loc = "/usr/lib/R/library") > fit <- coxph(Surv(exit, event) ~ x, data = mort) I get Error in fitter(X, Y, strats, offset, init, control, weights = weights, : object 'Ccoxmart' not found I was told on R-help that I need to > update.packages(checkBuilt = TRUE) (and it works), but 1. I get two versions of recommended packages, one in /usr/lib/R/library, and one in ~/R/x86_64-pc-l...

...(results)=c("coef","se","p") > > for(i in 1:ncol(x)){ + fit=summary(coxph(TIME~x[,i])) + results[i,1]=fit$coef[1] + results[i,2]=fit$coef[3] + results[i,3]=fit$coef[5] + rm(fit) + } Warning message: Loglik converged before variable 1 ; beta may be infinite. in: fitter(X, Y, strats, offset, init, control, weights = weights, > > results coef se p [1,] -0.5117033 5.647385e-01 0.36 [2,] -10.2256937 1.146168e+04 1.00 > > #To see which model gave the warning message > coxph(TIME~x[,1]) Call: coxph(formula = TIME ~ x[, 1])...

Dear useRs, a new version of the 'relations' package has appeared on CRAN. New features include: o support for fuzzy relations added o support for sets moved to separate 'sets' package o new SD fitters for the S ("symmetric") and M ("matches") families o fitters for Cook-Seiford method and Euclidean consensus added o fitters can now use a sparse constraint matrix representation o relations are now subsettable o relation_choice() for choosing "winner"...

Dear useRs, a new version of the 'relations' package has appeared on CRAN. New features include: o support for fuzzy relations added o support for sets moved to separate 'sets' package o new SD fitters for the S ("symmetric") and M ("matches") families o fitters for Cook-Seiford method and Euclidean consensus added o fitters can now use a sparse constraint matrix representation o relations are now subsettable o relation_choice() for choosing "winner"...