在分成三类时，summary(模型)后没有p值和se，也没有score test值和p，结果如下:
Joint latent class model for quantitative outcome and competing risks
fitted by maximum likelihood method
Jointlcmm(fixed = y~ 年龄 + 海马, mixture = ~年龄 + 海马, random = ~年龄, subject = "ID", classmb = ~海马 + 年龄, ng = 2, survival = Surv(进入队列年龄, 最后诊断年龄, 诊断结果) ~ AP0E4 + FAQ, hazard = "Weibull", data = paquidS)
Statistical Model:
Dataset: paquidS
Number of subjects: 210
Number of observations: 369
Number of latent classes: 2
Number of parameters: 19
Event 1:
Number of events: 62
Class-specific hazards and
Weibull baseline risk function
Iteration process:
Maximum number of iteration reached without convergence
Number of iterations: 100
Convergence criteria: parameters= 0.0021
: likelihood= 8.1e-05
: second derivatives= 1
Goodness-of-fit statistics:
maximum log-likelihood: -826.29
AIC: 1690.58
BIC: 1754.18
Maximum Likelihood Estimates:
Fixed effects in the class-membership model:
(the class of reference is the last class)
coef Se Wald p-value
intercept class1 10.21166
海马 class1 -0.00234
年龄 class1 0.34270
Parameters in the proportional hazard model:
coef Se Wald p-value
event1 +/-sqrt(Weibull1) class 1 5.65317
event1 +/-sqrt(Weibull2) class 1 0.58915
event1 +/-sqrt(Weibull1) class 2 0.12621
event1 +/-sqrt(Weibull2) class 2 2.24641
AP0E4 -0.09107
FAQ 0.06437
Fixed effects in the longitudinal model:
coef Se Wald p-value
intercept class1 4.41830
intercept class2 7.10469
海马 class1 -0.00046
海马 class2 -0.00112
年龄 class1 0.00717
年龄 class2 0.06943
……
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