我对将lm语法转换为lavaan感兴趣,特别是当因子具有时,在因子x数字变量之间进行了效应编码交互之后> 2个级别。 (提醒:effects-coding是伪编码类别变量的替代方法,例如代码为-1、1和0。)
下面您将看到一个最小的示例(无意义)。您将看到lm(线性回归)语法,然后看到相应的lavaan语法(回归部分)。它适用于没有交互但不具有交互的回归。
首先考虑具有效应编码因子的无交互回归。
这有效
library(lavaan)
# Use iris data as minimal example
#
# 1. Linear regression model
# Change contrasts to effects-coding
contrasts(iris$Species) <- contr.sum(3)
# Linear regression
lmmodel <- Sepal.Length ~ Species # the regression model
lmfit <- lm(lmmodel, iris) # fit it
# 2. SEM
# first, re-code the factors
iris$s1 <- contrasts(iris$Species)[iris$Species, 1] # Numeric and effects-coed
iris$s2 <- contrasts(iris$Species)[iris$Species, 2] # - " -
semmodel <- 'Sepal.Length ~ s1 + s2' # the SEM model
semfit <- sem(semmodel, iris) # fit it
# 3. Compare the coefficients lm vs. sem, should be equal (and are equal)
cbind(coef(lmfit)[-1], coef(semfit)[-length(coef(semfit))])
# [,1] [,2]
# Species1 -0.83733333 -0.83733330
# Species2 0.09266667 0.09266664
这是我如何通过互动进行操作 我要去哪里错了?
# 1. Linear regression w/ interaction
lmmodel <- Sepal.Length ~ Species + Species:Sepal.Width
lmfit <- lm(lmmodel, iris)
# 2. SEM
iris$s3 <- as.numeric(iris$Species=='virginica') # Code third species
iris$s1_w <- iris$s1 * iris$Sepal.Width # Numeric interaction
iris$s2_w <- iris$s2 * iris$Sepal.Width # - " -
iris$s3_w <- iris$s3 * iris$Sepal.Width # - " -"
semmodel <- 'Sepal.Length ~ s1 + s2 + s1_w + s2_w + s3_w'
semfit <- sem(semmodel, iris)
# 3. Compare the coefficients lm vs. sem
cbind(coef(lmfit)[-1], coef(semfit)[-length(coef(semfit))])
# [,1] [,2]
# Species1 -0.7228562 -0.7228566
# Species2 0.1778772 0.1778772
# Speciessetosa:Sepal.Width 0.6904897 0.6904899
# Speciesversicolor:Sepal.Width 0.8650777 0.8650779 <----- equal
# Speciesvirginica:Sepal.Width 0.9015345 2.4571023 <----- not equal
答案 0 :(得分:0)
问题不在于 lavaan
,您只是没有正确编码 Virginica Species
的对比:
从第 101 行到第 150 行,您应该有 0,0,1
,即:
iris[101:150,"s2_w"] <- 0
iris[101:150,"s1_w"] <- 0
重新运行原始代码:
semmodel <- 'Sepal.Length ~ s1 + s2 + s1_w + s2_w + s3_w'
semfit <- sem(model = semmodel, data = iris, estimator="ml")
# 3. Compare the coefficients lm vs. sem
cbind(coef(lmfit)[-1], coef(semfit)[-length(coef(semfit))])
并检查:
(¬_¬)# 3. Compare the coefficients lm vs. sem
(¬_¬)cbind(coef(lmfit)[-1], coef(semfit)[-length(coef(semfit))])
[,1] [,2]
Species1 -0.7228562 -0.7228563
Species2 0.1778772 0.1778772
Speciessetosa:Sepal.Width 0.6904897 0.6904898
Speciesversicolor:Sepal.Width 0.8650777 0.8650778
Speciesvirginica:Sepal.Width 0.9015345 0.9015345