Sorry. I should have included some data. I've attached a subset of my data (50/192) cases in a Rdata file and have pasted it below.
Running anova I get the following: > anova(sr.reg.s4.nore) Df Deviance Resid. Df -2*LL P(>|Chi|) NULL NA NA 45 33.89752 NA as.factor(lifedxm) 2 2.438211 43 31.45931 0.2954943 That would just be an omnibus test right and should that first NULL NA line be worrisome? What if I want to test specifically that CONTROL and BIPOLAR were different and that MAJOR DEPRESSION and BIPOLAR were different? I'll look at Hauck-Donner effect. Thanks, Chris > bip.surv.s age_sym4 sym4 lifedxm 1 16.12868 0 MAJOR 2 19.32649 0 MAJOR 3 16.55031 0 CONTROL 4 19.36756 0 CONTROL 5 16.09035 0 MAJOR 6 21.50582 0 MAJOR 7 16.36140 0 MAJOR 8 20.57221 0 MAJOR 9 16.45722 0 CONTROL 10 19.94524 0 CONTROL 11 15.79192 0 MAJOR 12 20.76660 0 MAJOR 13 16.15058 0 BIPOLAR 14 19.25804 0 BIPOLAR 15 17.36345 0 MAJOR 16 21.18001 0 MAJOR 17 NA 0 BIPOLAR 18 NA 0 BIPOLAR 19 16.31759 1 MAJOR 20 18.29706 0 MAJOR 21 16.40794 0 MAJOR 22 19.13758 0 MAJOR 23 16.19439 0 CONTROL 24 21.36893 0 CONTROL 25 15.89049 0 CONTROL 26 18.99795 0 CONTROL 27 NA 0 BIPOLAR 28 18.90486 0 BIPOLAR 29 16.36413 0 MAJOR 30 20.42710 0 MAJOR 31 16.65982 0 MAJOR 32 19.45791 0 MAJOR 33 16.64339 0 CONTROL 34 19.40041 0 CONTROL 35 15.37303 1 BIPOLAR 36 19.83847 0 BIPOLAR 37 15.42231 1 MAJOR 38 19.37029 0 MAJOR 39 15.06913 0 MAJOR 40 17.81520 0 MAJOR 41 15.50445 0 BIPOLAR 42 17.92197 0 BIPOLAR 43 15.34565 0 CONTROL 44 18.07529 0 CONTROL 45 15.59480 0 CONTROL 46 19.67420 0 CONTROL 47 14.78987 0 MAJOR 48 20.05476 0 MAJOR 49 14.78713 0 MAJOR 50 19.86858 0 MAJOR On Fri, 2010-07-23 at 11:52 -0700, Charles C. Berry wrote: > On Fri, 23 Jul 2010, Christopher David Desjardins wrote: > > > Hi, > > I am trying to fit the following model: > > > > sr.reg.s4.nore <- survreg(Surv(age_sym4,sym4), as.factor(lifedxm), > > data=bip.surv) > > Next time include a reproducible example. i.e. something we can run. > > Now, Google "Hauck Donner Effect" to understand why > > anova(sr.reg.s4.nore) > > is preferred. > > Chuck > > > > > > Where age_sym4 is the age that a subject develops clinical thought > > problems; sym4 is whether they develop clinical thoughts problems (0 or > > 1); and lifedxm is mother's diagnosis: BIPOLAR, MAJOR DEPRESSION, or > > CONTROL. > > > > I am interested in whether or not survival differs by this covariate. > > > > When I run my model, I am getting the following output: > > > >> summary(sr.reg.s4.nore) > > > > Call: > > survreg(formula = Surv(age_sym4, sym4) ~ as.factor(lifedxm), > > data = bip.surv) > > Value Std. Error z p > > (Intercept) 4.037 0.455 8.86643 > > 0.000000000000000000755 > > as.factor(lifedxm)CONTROL 14.844 4707.383 0.00315 > > 0.997484052845082791450 > > as.factor(lifedxm)MAJOR 0.706 0.447 1.58037 > > 0.114022774867277756905 > > Log(scale) -0.290 0.267 -1.08493 > > 0.277952437474223823521 > > > > Scale= 0.748 > > > > Weibull distribution > > Loglik(model)= -76.3 Loglik(intercept only)= -82.6 > > Chisq= 12.73 on 2 degrees of freedom, p= 0.0017 > > Number of Newton-Raphson Iterations: 21 > > n=186 (6 observations deleted due to missingness) > > > > > > I am concerned about the p-value of 0.997 and the SE of 4707. I am > > curious if it has to do with the fact that the CONTROL group doesn't > > have a mixed response, meaning that all my subjects do not develop > > clinical levels of thought problems and subsequently 'survive'. > > > >> table(bip.surv$sym4,bip.surv$lifedxm) > > > > BIPOLAR CONTROL MAJOR > > 0 41 60 78 > > 1 7 0 6 > > > > Is there some sort of way that I can overcome this? Is my model > > misspecified? Is this better suited to be run as a Bayesian model using > > priors to overcome the lack of a mixed response? > > > > Also, please cc me on an email as I am a digest subscriber. > > Thanks, > > Chris > > > > > > -- > > Christopher David Desjardins > > PhD student, Quantitative Methods in Education > > MS student, Statistics > > University of Minnesota > > 192 Education Sciences Building > > http://cddesjardins.wordpress.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. > > > > Charles C. Berry (858) 534-2098 > Dept of Family/Preventive > Medicine > E mailto:cbe...@tajo.ucsd.edu UC San Diego > http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901 > > -- Christopher David Desjardins PhD student, Quantitative Methods in Education MS student, Statistics University of Minnesota 192 Education Sciences Building http://cddesjardins.wordpress.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.