Question

我想拟合非线性混合模型，然后测试治疗组和对照组的参数之间的差异。

我正在使用lme4软件包中的nlmer。我正在使用Oranges数据集作为此问题的测试数据。随时间测量5棵树的周长。每棵树都显示逻辑增长。在基本示例中，我们将Tree包含为随机效果。我扩展了数据，以便有一个治疗和对照组（治疗只是圆周值加倍的对照的副本）。我的问题是，我想将“治疗”作为固定效应，然后测试治疗组和对照组的非线性模型参数Asym之间的差异。

   library(lme4)

   #Toy data based on Orange (lme4)
   # Create a copy of Orange data, double the circumference values, make new labels for trees (no. 6-10) and label all as treatment (1)
   Orange.with.treatment<-Orange
   Orange.with.treatment$circumference<-Orange.with.treatment$circumference*2
   Orange.with.treatment$Tree <- as.factor(as.numeric(Orange.with.treatment$Tree) + 5)
   Orange.with.treatment$treat<- as.factor(rep(1,length(Orange$Tree)))

   # Create a copy of Orange data and label all as control (1)
   Orange.control<-Orange
   Orange.control$treat<- as.factor(rep(0,length(Orange$Tree)))

   # combine into one dataframe
   Orange.full<-(rbind(Orange.control,Orange.with.treatment))


   # a nlmer fit not considering treatment as a factor
   startvec <- c(Asym = 200, xmid = 725, scal = 350)
   (nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree,
                Orange.full, start = startvec))

   # a nlmer fit considering treatment as a fixed factor?
   startvec <- c(Asym = 200, xmid = 725, scal = 350)
   (nm2 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym+treat|Tree,
                Orange.full, start = startvec))

   # test differences in parameters between treat and control?

我尝试在公式中与Asym一起添加对待，但我认为这是不正确的。我想总结一下治疗和控制方面的Asym，以及一种统计检验两者之间差异的方法。

Answer 1

由于您似乎愿意使用其他工具，因此这里提供了nlme解决方案：

library(nlme)
mod <- nlme(circumference ~ SSlogis(age, Asym, xmid, scal), data = Orange.full,
    fixed = Asym + xmid + scal ~ treat, random = Asym + xmid + scal ~ 1 | Tree,
    start = c(200, 200, 725, 0, 350, 0), control = nlmeControl(msMaxIter = 1000))
summary(mod)

#Nonlinear mixed-effects model fit by maximum likelihood
#  Model: circumference ~ SSlogis(age, Asym, xmid, scal) 
# Data: Orange.full 
#       AIC      BIC    logLik
#  608.9452 638.1756 -291.4726
#
#Random effects:
# Formula: list(Asym ~ 1, xmid ~ 1, scal ~ 1)
# Level: Tree
# Structure: General positive-definite, Log-Cholesky parametrization
#                 StdDev   Corr         
#Asym.(Intercept) 43.23426 As.(I) xm.(I)
#xmid.(Intercept) 38.35359 -0.031       
#scal.(Intercept) 32.49873 -0.968  0.279
#Residual         11.27260              
#
#Fixed effects: Asym + xmid + scal ~ treat 
#                    Value Std.Error DF   t-value p-value
#Asym.(Intercept) 191.2135  22.30629 55  8.572177  0.0000
#Asym.treat1      193.0409  31.56922 55  6.114847  0.0000
#xmid.(Intercept) 722.4272  53.37976 55 13.533729  0.0000
#xmid.treat1        5.0466  62.02158 55  0.081368  0.9354
#scal.(Intercept) 349.4497  41.68009 55  8.384092  0.0000
#scal.treat1        7.3181  48.41709 55  0.151146  0.8804
#
#<snip>

如您所见，这显示了对渐近线的显着治疗效果，但对其他参数却没有达到预期的效果。

在非线性混合模型中将其视为固定效应

1 个答案: