> library("Hmisc")> library("rms")> m1<-read.csv("RCS.csv")> options(datadist='dd')> dd<-datadist(m1)> options(datadist='dd')> m3<-cph(Surv(time,outcome)~rcs(SPISE,4),x=TRUE,y=TRUE,data=m1)> m3Cox Proportional Hazards Modelcph(formula = Surv(time, outcome) ~ rcs(SPISE, 4), data = m1, x = TRUE, y = TRUE) Model Tests Discrimination Indexes Obs 8867 LR chi2 27.32 R2 0.005 Events 423 d.f. 3 R2(3,8867)0.003 Center 0.5585 Pr(> chi2) 0.0000 R2(3,423)0.056 Score chi2 26.74 Dxy -0.147 Pr(> chi2) 0.0000 Coef S.E. Wald Z Pr(>|Z|)SPISE 0.1600 0.1102 1.45 0.1467 SPISE' -1.3253 0.4168 -3.18 0.0015 SPISE'' 4.1083 1.1935 3.44 0.0006 > m3<-cph(Surv(time,outcome)~rcs(SPISE,4),x=TRUE,y=TRUE,data = m1)> m3Cox Proportional Hazards Modelcph(formula = Surv(time, outcome) ~ rcs(SPISE, 4), data = m1, x = TRUE, y = TRUE) Model Tests Discrimination Indexes Obs 8867 LR chi2 27.32 R2 0.005 Events 423 d.f. 3 R2(3,8867)0.003 Center 0.5585 Pr(> chi2) 0.0000 R2(3,423)0.056 Score chi2 26.74 Dxy -0.147 Pr(> chi2) 0.0000 Coef S.E. Wald Z Pr(>|Z|)SPISE 0.1600 0.1102 1.45 0.1467 SPISE' -1.3253 0.4168 -3.18 0.0015 SPISE'' 4.1083 1.1935 3.44 0.0006 > anova(m3) Wald Statistics Response: Surv(time, outcome) Factor Chi-Square d.f. P SPISE 26.27 3 <.0001 Nonlinear 13.99 2 9e-04 TOTAL 26.27 3 <.0001> HR<-Predict(m3,SPISE,fun = exp,ref.zero = TRUE)Error in reformulate(attr(termobj, "term.labels")[-dropx], response = if (keep.response) termobj[[2L]], : 'termlabels'必需是长度至少为一的字节矢量> view(HR)Error in view(HR) : 没有"view"这个函数> View(HR)Error in View : 找不到对象'HR' |
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