Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk
I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <- spss.get("GustoW.sav") fit <- glm(DAY30~AGE+HYP+factor(KILLIP)+HRT+ANT,family=binomial(link="logit"),data=gusto,x=TRUE,y=TRUE) However, my review of the literature and other websites suggest I need to use "lrm" for the purposes of producing a nomogram. When I run the command using "lrm" (see below) I get an error message saying: Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : Unable to fit model using "lrm.fit" My code is as follows: gusto2 <- gusto[,c(1,3,5,8,9,10)] gusto2$HYP <- factor(gusto2$HYP, labels=c("No","Yes")) gusto2$KILLIP <- factor(gusto2$KILLIP, labels=c("1","2","3","4")) gusto2$HRT <- factor(gusto2$HRT, labels=c("No","Yes")) gusto2$ANT <- factor(gusto2$ANT, labels=c("No","Yes")) var.labels=c(DAY30="30-day Mortality", AGE="Age in Years", KILLIP="Killip Class", HYP="Hypertension", HRT="Tachycardia", ANT="Anterior Infarct Location") label(gusto2)=lapply(names(var.labels),function(x) label(gusto2[,x])=var.labels[x]) ddist = datadist(gusto2) options(datadist='ddist') fit1 <- lrm(DAY30~AGE+HYP+KILLIP+HRT+ANT,gusto2) Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : Unable to fit model using "lrm.fit" Online solutions to this problem involve checking whether any variables are redundant. However, the results for my data suggest that none are. redun(~AGE+HYP+KILLIP+HRT+ANT,gusto2) Redundancy Analysis redun(formula = ~AGE + HYP + KILLIP + HRT + ANT, data = gusto2) n: 2188 p: 5 nk: 3 Number of NAs: 0 Transformation of target variables forced to be linear R-squared cutoff: 0.9 Type: ordinary R^2 with which each variable can be predicted from all other variables: AGE HYP KILLIP HRT ANT 0.028 0.032 0.053 0.046 0.040 No redundant variables I've also tried just considering "lrm.fit" and that code seems to run without error too: lrm.fit(cbind(gusto2$AGE,gusto2$KILLIP,gusto2$HYP,gusto2$HRT,gusto2$ANT),gusto2$DAY30) Logistic Regression Model lrm.fit(x = cbind(gusto2$AGE, gusto2$KILLIP, gusto2$HYP, gusto2$HRT, gusto2$ANT), y = gusto2$DAY30) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 2188 LR chi2 233.59 R2 0.273 C 0.846 0 2053 d.f. 5 g 1.642 Dxy 0.691 1 135 Pr(> chi2) <0.0001 gr 5.165 gamma 0.696 max |deriv| 4e-09 gp 0.079 tau-a 0.080 Brier 0.048 Coef S.E. Wald Z Pr(>|Z|) Intercept -13.8515 0.9694 -14.29 <0.0001 x[1] 0.0989 0.0103 9.58 <0.0001 x[2] 0.9030 0.1510 5.98 <0.0001 x[3] 1.3576 0.2570 5.28 <0.0001 x[4] 0.6884 0.2034 3.38 0.0007 x[5] 0.6327 0.2003 3.16 0.0016 I was therefore hoping someone would explain why the "lrm" code is producing an error message, while "lrm.fit" and "glm" do not. In particular I would welcome a solution to ensure I can produce a nomogram. Kind regards, Laura Dr Laura Bonnett NIHR Post-Doctoral Fellow Department of Biostatistics, Waterhouse Building, Block F, 1-5 Brownlow Street, University of Liverpool, Liverpool, L69 3GL 0151 795 9686 l.j.bonn...@liverpool.ac.uk [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.