我对以下研究生录取数据在R中进行了对数线性分析。
grad_admissions <- array(data = c(39, 10, 20, 15, 11, 41, 6, 60),
dim = c(2,2,2),
dimnames = list("department" = c("one","two"),
"gender" = c("male","female"),
"admission" = c("admitted","notadmitted")))
ftable(grad_admissions, row.vars = c("department"),col.vars = c("admission","gender"))
grad_admissions.df <- as.data.frame(as.table(grad_admissions))
grad_admissions.df$gender <- factor(grad_admissions.df$gender, levels = c("female","male"))
grad_admissions.df$department <- factor(grad_admissions.df$department, levels = c("two","one"))
grad_admissions.df$admission <- factor(grad_admissions.df$admission, levels = c("admitted","notadmitted"))
mod1 <- glm(Freq ~ department * gender * admission,
data = grad_admissions.df, family = poisson)
summary(mod1)
我还在同一数据集(SAV文件here)上运行了以下SPSS语法。
DATASET ACTIVATE DataSet2.
WEIGHT BY Count.
GENLOG Gender Admitted Department
/MODEL=POISSON
/PRINT=FREQ RESID ADJRESID ZRESID DEV ESTIM CORR COV
/PLOT=NONE
/CRITERIA=CIN(95) ITERATE(20) CONVERGE(0.001) DELTA(.5).
参数估计值如下。它们相似但不完全相同。在SPSS输出中,将男性编码为0,将女性编码为1。
谁能解释为什么他们不一样吗?
尝试以下操作:
GENLOG Department Gender Admitted
/MODEL=POISSON
/PRINT=FREQ RESID ADJRESID ZRESID DEV ESTIM CORR COV
/PLOT=NONE
/CRITERIA=CIN(95) ITERATE(20) CONVERGE(0.001) DELTA(0).
注意DELTA(0)
子命令上的CRITERIA
规范。默认情况下,SPSS GENLOG将饱和模型中每个像元的像元计数加.5,这是在对数线性模型中处理0个像元数的常用技术。