This is very nice also. I am going to use this approach in the future when I use lm. However, I can't seem to get to work the way I want with cenmle. I will continue to experiment. Thanks folks for the suggestions.
Tom David Winsemius wrote: > > > On Nov 12, 2008, at 8:48 AM, Tom La Bone wrote: > >> >> I figured it out. In case anyone else ever has this question -- >> given the >> following output from cenmle: >> >>> fit.cen <- cenmle(obs, censored, groups) >>> fit.cen >> Value Std. Error z p >> (Intercept) 1.19473 0.0772 15.4695 5.58e-54 >> groups1 0.00208 0.0789 0.0263 9.79e-01 >> Log(scale) -0.40221 0.0168 -23.9034 2.82e-126 >> >> Scale= 0.669 >> >> Log Normal distribution >> Loglik(model)= -3908.9 Loglik(intercept only)= -3908.9 >> Loglik-r: 0.0006265534 >> >> Chisq= 0 on 1 degrees of freedom, p= 0.98 >> Number of Newton-Raphson Iterations: 1 >> n = 1766 >> >> The p-value is (for example): >> >> p.value <- summary(fit.cen)[[9]][[11]] > > An approach that may yield somewhat more self-documenting code would > be to examine either the fit object or the summary object with str and > then to access results by extracting named elements. Since I don't > have the package in question, let me use the lm object on its help > page as an example: > > ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) > trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) > group <- gl(2,10,20, labels=c("Ctl","Trt")) > weight <- c(ctl, trt) > lm.D9 <- lm(weight ~ group) > > str(lm.D9) > str(summary(lm.D9)) > summary(lm.D9)$coefficients > summary(lm.D9)$coefficients["groupTrt", "Pr(>|t|)"] > > # to get the p-value > > summary(lm.D9)$coefficients["groupTrt","Pr(>|t|)"] > [1] 0.2490232 > > You had originally asked for a method to extract coefficients and for > that purpose you may want to look at: > > ?coefficients > > > slope <- coefficients(lm.D9)["groupTrt"] > > slope > groupTrt > -0.371 > > -- > David Winsemius > Heritage Labs > >> >> Tom >> >> >> >> >> Tom La Bone wrote: >>> >>> The cenmle function is used to fit two sets of censored data and >>> test if >>> they are significantly different. I can print out the results of the >>> analysis on the screen but can't seem to figure out how to access >>> these >>> results in R and assign them to new variables, e.g., assign the slope >>> calculated with cenmle to the variable m. Any suggestions? >>> >>> Tom >>> >> >> -- >> View this message in context: >> http://www.nabble.com/Accessing-Results-from-cenmle-function-in-NADA-package-tp20437420p20460676.html >> Sent from the R help mailing list archive at Nabble.com. >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/Accessing-Results-from-cenmle-function-in-NADA-package-tp20437420p20464040.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.