使用lcmm::predictClass时基于样条链接函数的报错问题

使用lcmm包的predictClass()预测类别归属时，若模型采用基于样条的链接函数（如3-quant-splines）会触发报错Length of vector range is not correct.，但切换为默认线性链接函数时该功能可正常运行。同时predictRE()也出现相同报错，predictL()和predictY()可正常工作，已准备联系包维护者。

报错复现代码 #

## 定义初始化值以快速得到结果
BB <- c(-19.064,21.718,-1.192,-1.295,-1.205,-0.281,0.110,
         -0.232, 1.339,-1.007, 1.019,-9.395, 1.702,2.030,
          2.089, 1.352,-9.369, 1.220, 1.532, 2.481,1.223)

library(lcmm)
m2c <- multlcmm(Ydep1+Ydep2~1+Time*X2,
                random=~1+Time,
                subject="ID",
                link="3-quant-splines",
                ng=2,
                mixture=~1+Time,
                classmb=~1+X1,
                data=data_lcmm,
                B=BB)
## 3次迭代后收敛

## 定义预测数据集
library(dplyr)
X <- data_lcmm %>% 
  filter(ID %in% sample(ID,10)) %>% ## 随机选取10个ID
  select(ID,Ydep1,Ydep2,Time,X1,X2)

## 预测类别归属
predictClass(m2c, newdata=X)
## 报错信息：
## Error in multlcmm(fixed = Ydep1 + Ydep2 ~ 1 + Time * X2, mixture = ~1 +  : 
##   Length of vector range is not correct.

线性链接函数下的正常运行示例 #

library(lcmm)
m2 <- multlcmm(Ydep1+Ydep2~1+Time*X2,
               random=~1+Time,
               subject="ID",
               link="linear",
               ng=2,
               mixture=~1+Time,
               classmb=~1+X1,
               data=data_lcmm,
               B=c(18,-20.77,1.16,-1.41,-1.39,-0.32,0.16,
                   -0.26,1.69,1.12,1.1,10.8,1.24,24.88,1.89))
## 2次迭代后收敛

library(dplyr)
X <- data_lcmm %>% 
  filter(ID %in% sample(ID,10)) %>%
  select(ID,Ydep1,Ydep2,Time,X1,X2)

## 预测类别归属
predictClass(m2, newdata=X)
## 输出示例：
##     ID class       prob1        prob2
## 1   21     2 0.031948951 9.680510e-01
## 2   25     2 0.042938984 9.570610e-01
## 3   33     2 0.026053178 9.739468e-01
## 4   46     1 0.999999964 3.597409e-08
## 5   50     2 0.066291287 9.337087e-01
## 6   74     2 0.005630593 9.943694e-01
## 7  120     2 0.024787290 9.752127e-01
## 8  171     2 0.053499974 9.465000e-01
## 9  229     1 0.999999996 4.368222e-09
##10  235     2 0.008173507 9.918265e-01
## ...或类似结果

内容的提问来源于stack exchange，提问作者Big Old Dave