How to extract metafor::rma.mv() equivalent of lme4::VarCorr() for multilevel mixed effect meta-regression with random slopes

huangapple go评论79阅读模式
英文:

How to extract metafor::rma.mv() equivalent of lme4::VarCorr() for multilevel mixed effect meta-regression with random slopes

问题

我正在尝试计算多层混合效应元回归(包括随机斜率)的伪R平方,使用metafor包中的类似方法(即rma.mv()对象),该方法与以下引文类似:

https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12225

我正在研究这篇引文中的代码,该代码使用lme4/nlme包演示了如何进行计算,似乎需要一个metafor等效的lme4/nlme拟合模型的VarCorr()输出摘要。

我正在努力弄清楚如何获得rma.mv()对象中每个随机效应层级的方差(或标准差)的摘要。在这方面,任何见解都将不胜感激!

这是引文中用于计算伪R平方的代码:


# 拟合模型
orangemod.rs <- 
    lmer(circumference ~ ageYears + (ageYears | Tree), data = Orange)


# 首先我们需要设计矩阵(X)、观测数量(n)和固定效应估计值(Beta)

  X <- model.matrix(orangemod.rs)
  n <- nrow(X)
  Beta <- fixef(orangemod.rs)

# 首先计算固定效应的方差(Nakagawa & Schielzeth的方程27):

  Sf <- var(X %*% Beta)

# 其次,计算随机效应的协方差矩阵列表。
# 这里列表只有一个元素,因为只有一个随机效应层级。

  (Sigma.list <- VarCorr(orangemod.rs))

# 使用论文中的方程11估计随机效应的方差分量。
# 使用sapply确保在存在多个随机效应时(即length(Sigma.list) > 1)也能正常工作。

  Sl <- 
    sum(
      sapply(Sigma.list,
        function(Sigma)
        {
          Z <-X[,rownames(Sigma)]
          sum(diag(Z %*% Sigma %*% t(Z)))/n
        }))

# 由于该模型是LMM,加性离散方差Se等于残差方差。残差标准差存储为Sigma.list的属性:

  Se <- attr(Sigma.list, "sc")^2

# 最后,LMM的分布特定方差Sd为零:

  Sd <- 0

# 使用Nakagawa & Schielzeth的方程29和30估计边际和条件R平方:

  total.var <- Sf + Sl + Se + Sd
  (Rsq.m <- Sf / total.var) 
  (Rsq.c <- (Sf + Sl) / total.var) 

除了使用VarCorr()函数的步骤之外,所有这些步骤都可以轻松地用于rma.mv()对象:

# 其次,计算随机效应的协方差矩阵列表。
# 这里列表只有一个元素,因为只有一个随机效应层级。

  (Sigma.list <- VarCorr(orangemod.rs))

# Groups   Name        Std.Dev. Corr  
# Tree     (Intercept)  1.8966        
#          ageYears     8.7757  -1.000
# Residual             10.0062      

这是一个简单的模型,用于说明这一点,并突出了rma.mv()模型的两个可能有必要信息的组成部分,但我不确定如何将其转换为最终的VarCorr()格式。

library(metafor)
library(tidyverse)

data("dat.konstantopoulos2011")
df=dat.konstantopoulos2011%>%
  as.data.frame()

# 拟合随机斜率模型
m1=rma.mv(yi ~ year, vi, random = list(~year|study, ~year|district, ~1|school), data = df,
          cvvc = TRUE)

lme4::VarCorr(m1)
nlme::VarCorr(m1)

# 可能有用的组成部分
m1$V
m1$vvc


我希望重新创建的内容

# Groups   Name        Std.Dev. Corr  
# study    (Intercept)  [VALUE]        
#          ageYears     [VALUE]  [VALUE]
# district (Intercept)
#          ageYears              [VALUE]
# school   (Intercept)
# Residual             [VALUE]      
英文:

I am attempting to calculate pseudo R squared's for a multilevel mixed-effect meta-regression that includes random slopes in the metafor package (i.e., rma.mv() object) using a similar approach to the following citation:

https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12225

Digging into the code of this citation that uses the lme4/nlme packages to demonstrate how to perform the calculations, it seems like a metafor equivalent of a VarCorr() output of a lme4/nlme fit model is needed.

I am struggling to figure out how to obtain this summary of the variances (or SD) at each level of the random effects for a rma.mv() object. Any insight here is appreciated!

This is the code from the citation used to calculate the pseudo R squared's:


# Fit Model
orangemod.rs &lt;- 
    lmer(circumference ~ ageYears + (ageYears | Tree), data = Orange)


# First we need the design matrix (X), the number of observations (n)
# and the fixed effect estimates (Beta)

  X &lt;- model.matrix(orangemod.rs)
  n &lt;- nrow(X)
  Beta &lt;- fixef(orangemod.rs)

# First the fixed effects variance (eqn 27 of Nakagawa &amp; Schielzeth): 

  Sf &lt;- var(X %*% Beta)

# Second, the list of covariance matrices of the random effects.
# Here the list has only a single element because there is only
# one level of random effects.

  (Sigma.list &lt;- VarCorr(orangemod.rs))

# Use equation 11 in the paper to estimate the random effects variance 
# component.
# Using sapply ensures that the same code will work when there are multiple
# random effects (i.e. where length(Sigma.list) &gt; 1)

  Sl &lt;- 
    sum(
      sapply(Sigma.list,
        function(Sigma)
        {
          Z &lt;-X[,rownames(Sigma)]
          sum(diag(Z %*% Sigma %*% t(Z)))/n
        }))

# As this model is an LMM, the additive dispersion variance, Se, is
# equivalent to the residual variance. The residual standard deviation
# is stored as an attribute of Sigma.list:
  
  Se &lt;- attr(Sigma.list, &quot;sc&quot;)^2

# Finally, the distribution-specific variance, Sd, is zero for LMMs:

  Sd &lt;- 0

# Use eqns 29 and 30 from Nakagawa &amp; Schielzeth to estimate marginal and
# conditional R-squared:

  total.var &lt;- Sf + Sl + Se + Sd
  (Rsq.m &lt;- Sf / total.var) 
  (Rsq.c &lt;- (Sf + Sl) / total.var) 

All of these steps can be easily reproduced for the rma.mv() object besides the step with VarCorr() as this function doesn't accept rma.mv() objects:

# Second, the list of covariance matrices of the random effects.
# Here the list has only a single element because there is only
# one level of random effects.

  (Sigma.list &lt;- VarCorr(orangemod.rs))

# Groups   Name        Std.Dev. Corr  
# Tree     (Intercept)  1.8966        
#          ageYears     8.7757  -1.000
# Residual             10.0062      

Here is a simple model that illustrates the point and highlights two of the components of the rma.mv() model that I think might have the necessary information but I'm not exactly sure how to get it into the final VarCorr() format.

library(metafor)
library(tidyverse)

data(&quot;dat.konstantopoulos2011&quot;)
df=dat.konstantopoulos2011%&gt;%
  as.data.frame()

#fit random slope model
m1=rma.mv(yi ~ year, vi, random = list(~year|study, ~year|district, ~1|school), data = df,
          cvvc = TRUE)

lme4::VarCorr(m1)
nlme::VarCorr(m1)

#potentially useful components
m1$V
m1$vvc


What I&#39;m looking to recreate

# Groups   Name        Std.Dev. Corr  
# study    (Intercept)  [VALUE]        
#          ageYears     [VALUE]  [VALUE]
# district (Intercept)
#          ageYears              [VALUE]
# school   (Intercept)
# Residual             [VALUE]      

答案1

得分: 0

~year|study对应的随机效应的方差-协方差矩阵可以通过m1$G提取。如果只需要方差,可以使用m1$tau2~year|district的方差-协方差矩阵是m1$H,方差在m1$gamma2中。~1|school对应的方差是m1$sigma2

请注意,如果您想要随机斜率,那么您正在拟合的模型不提供该功能。您需要使用以下代码:

rma.mv(yi ~ year, vi, 
       random = list(~year|study, ~year|district, ~1|school),
       struct=c("GEN","GEN"), data = df)

参考链接:https://wviechtb.github.io/metafor/reference/rma.mv.html#specifying-random-effects

英文:

The variance-covariance matrix of the random effects corresponding to ~year|study can be extracted with m1$G. If you only need the variances, then m1$tau2 will do. The var-cov matrix for ~year|district is m1$H and the variances are in m1$gamma2. The variance corresponding to ~1|school is m1$sigma2.

Note that if you want random slopes, then the model you are fitting doesn't provide that. You would need to use:

rma.mv(yi ~ year, vi, 
       random = list(~year|study, ~year|district, ~1|school),
       struct=c(&quot;GEN&quot;,&quot;GEN&quot;), data = df)

See: https://wviechtb.github.io/metafor/reference/rma.mv.html#specifying-random-effects

huangapple
  • 本文由 发表于 2023年8月8日 22:39:25
  • 转载请务必保留本文链接:https://go.coder-hub.com/76860616.html
匿名

发表评论

匿名网友

:?: :razz: :sad: :evil: :!: :smile: :oops: :grin: :eek: :shock: :???: :cool: :lol: :mad: :twisted: :roll: :wink: :idea: :arrow: :neutral: :cry: :mrgreen:

确定