search for: pfit

...see it. I used the help sample code to generate a weighted model, with the addition of a "weights=albumin" argument (I only chose albumin because it had no missing values, not because of any real relevance). Below are my code with the resulting error messages. Thanks in advance! > pfit <- coxph(Surv(time,status>0) ~ trt + log(bili) + log(protime) + age + + + platelet, data=pbc + ) > > pfit Call: coxph(formula = Surv(time, status > 0) ~ trt + log(bili) + log(protime) + age + +platelet, data = pbc) coef exp(coef) se(coef) z...

Hello, I'm trying to fit some points with a 8-degrees polynom (result of lm is stored in pfit). In most of the case, it is ok but for some others, some coefficients are "NA". I don't really understand the meaning of these "NA". And the problem is that I can't perform a derivation (pderiv<-as.function((deriv(polynomial(pfit$coefficients))))) on pfit due to the...

...lt;-lm(ab ~ poly(a,8,raw=T)) - to use the package polynom to make the polynom library(polynom) polynomial(abfit$coefficients) 0.03537506 - 0.8506516*x + 8.302545*x^2 - 44.80418*x^3 + 144.2161*x^4 - 283.2458*x^5 + 331.1776*x^6 - 210.5144*x^7 + 55.86646*x^8 - to derive the polynom deriv(polynomial(pfit$coefficients)) -0.8506516 + 16.60509*x - 134.4125*x^2 + 576.8643*x^3 - 1416.229*x^4 + 1987.066*x^5 - 1473.601*x^6 + 446.9317*x^7 - to find the zero solve(deriv(polynomial(pfit$coefficients))) [1] 0.1749897+0.0568886i 0.1749897-0.0568886i 0.3742571-0.1235393i 0.3742571+0.1235393i 0.4778418+0.000000...

Hi all, For the simple linear regression, I want to find the input "x" value so that the lower confidnece limit is a specific number, say 0.2. In other words, I want to find the value of x so that the lower confidence bound crosses the horizontal line 0.2. Is there a simple way (an R function) that can do this? Thanks. Hanna [[alternative HTML version deleted]]

...288*(q*Rg(L,b))^(-2/v)-1.651*(q*Rg(L,b))^(-1/v)) %%Another defined function cf<-function(L,b,pa,pb)pa/(L/b)^pb %%A function of q, quote Pexv, Rgsq, cf Psfcex<-function(v,L,b,pa,pb)Pexv(v,L,b)+cf(L,b,pa,pb)*b/L/15*(4+7/q^2/Rgsq(L,b)-(11+7/q^2/Rgsq(L,b))*exp(-q^2*Rgsq(L,b))) %%non-linear fit Pfit<-function(p)sum((I-Psfcex(p[1],p[2],p[3],p[4],p[5]))^2) wavefit<-nlm(Pfit,c(1.5,500,5,1,0.1),hessian=TRUE) error code: Error in c(1.5, 500, 5, 1, 0.1) : unused argument(s) (0.1) the fifth parameter is pb, which goes from Psfcex to alpha, actually L,b and pa in alpha are used, why not pb?...

Hi I have the following formula (I hope it is clear - if no, I can try to do better the next time) h(x, a, b) = integral(0 to pi/2) ( ( integral(D/sin(alpha) to Inf) ( ( f(x, a, b) ) dx ) dalpha ) and I want to do an mle with it. I know how to use mle() and I also know about integrate(). My problem is to give the parameter values a and b to the

...on 1: how theta is converted into the specific effect on (intercept) for the random effect ? # Then how a theta parameter is converted into intercepts? # _______________________________________________________________________ intercepts <- ranef(g0)$ID # This part is ok, the predict is correct pfit <- 1-c(1/(1+exp(fixep["(Intercept)"]+intercepts["A", 1]+x[ID=="A"]*fixep["x"])), ? 1/(1+exp(fixep["(Intercept)"]+intercepts["B", 1]+x[ID=="B"]*fixep["x"])), ? 1/(1+exp(fixep["(Intercept)"]+intercepts[&q...

Hi all, I am trying to plot the one-sided confidence limits for the regression line. It seems it is ok to use predict function to compute the two sided confidence limits. Does any one know a easy way to compute the one sided confidence limits? Thank you very much in advance. Hannah [[alternative HTML version deleted]]

...atomic for 'sort.list' > Have you called 'sort' on a list? In the R documentation on ?ols() it states concerning the values returned: "the same objects returned from |lm| (/unless |penalty| or |penalty.matrix| are given/ - then an abbreviated list is returned since |lm.pfit| is used as a fitter)..." Unfortunately no information seems to be available on lm.pfit. Does anyone know why the using that function leads to an abbreviated return list? Is there a trick to circumvent that? Thanks Benjamin Volland P.S. Currently using R-version 2.7.1 on a Windows PC.

I saw a similar question but I still don't fully understand how to implement optim. Can someone help me out with this? Thanks. Keun-Hyung > vol<-rep(c(0.03, 0.5, 2, 4, 8, 16, 32), 3) > time<-rep(c(2,4,8),each=7) > p.mated<-c(0.47, 0.48, 0.43, 0.43, 0.26, 0.23, "null", 0.68, 0.62, 0.64, 0.58, 0.53, 0.47, + 0.24, 0.8, 0.79, 0.71, 0.56, 0.74, 0.8, 0.47) >

...t,bootBCa,bootcov,bootplot,bplot,calibrate,cph,catg,combineRelatedPredictors,confplot,contrast,coxphFit,cph,cr.setup,datadist,effective.df,fastbw,formatNP,gendata,gIndex,GiniMd,Glm,Gls,groupkm,Hazard,hazard.ratio.plot,histdensity,"%ia%",ie.setup,interactions.containing,legend.nomabbrev,lm.pfit,lrm,lrtest,lsp,matinv,matrx,Newlabels,Newlevels,nomogram,num.intercepts,ols,ols.influence,oos.loglik,pantext,Penalty.matrix,Penalty.setup,pentrace,perimeter,perlcode,plot.xmean.ordinaly,pol,pphsm,predab.resample,Predict,psm,rcs,related.predictors,reVector,robcov,Rq,sascode,scored,sensuc,setPb,show....

...ts: >> Estimate Std. Error >> alpha -1.17522678 0.21572863 >> beta 0.03674818 0.00656062 >> >> -2 log L: 111.1682 >> >> The result of profile(z,1) was not correct. I tried to track the bug (or >> feature?), and found that: >> >> pfit<- tryCatch(eval.parent(call, 2L), error = identity) >> >> give the following error message: >> >> <simpleError in match.arg(method): object 'method' not found> >> >> Therefore, I thought that >> call$method<- fitted at method >> sh...

...the starting point for each profile optimization is the previous best-fit solution, rather than the overall MLEs of the parameters. Actually fairly easy to implement (I think: I haven't really tested that it works on anything hard, just that it doesn't seem to break profiling) -- requires pfit to be assigned globally within onestep() and a few lines of code further down. added an AIC method for mle objects collapsed the absVal/!absVal code cases slightly added a "sqrVal" argument for those who want to see the value of the log-likelihood, not the square root or signed square...

Dear List, Is there an automated way to use the survival package to generate survival rate estimates and their standard errors? To be clear, *not *the survivorship estimates (which are cumulative), but the survival *rate * estimates... Thank you in advance for any help. Best, Brian [[alternative HTML version deleted]]

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