(1998). Pairwise deletion for missing data in struc- tural equation models: nonpositive definite matrices, param- eter estimates, goodness of fit, and adjusted sample sizes. Structural Equation Modeling, 5, 22-36.Marsh HW (1998) Pairwise deletion for missing data in structural equation models:...
The use of sample covariance matrices constructed with pairwise deletion for data missing completely at random (SPW) is addressed in a simulation study based on 3 sample sizes (n = 200, 500, 1,000) and 5 levels of missing data (%miss = 0, 1, 10, 25, and 50). Parameter estimates ...
例如,如果在图形中使用变量x,y和z,那么仅当缺少其中一个变量的值时,才会排除观测值。 不考虑其他变量的缺失。missing.pairwise是处理缺失值的缺省行为。 示例 图1。 示例: 成对排除缺失值 SOURCE: s = csvSource(file("mydata.csv"), missing.pairwise())...
Examples Figure 1. Example: Excluding missing values pairwise SOURCE: s = csvSource(file("mydata.csv"), missing.pairwise())
Examples Figure 1. Example: Excluding missing values pairwise SOURCE: s = csvSource(file("mydata.csv"), missing.pairwise())
例如,如果在圖形中使用變數x、y和Z,則只有在觀察值遺漏其中一個變數的值時,才會排除該觀察值。 不考量其他變數的遺漏值。missing.pairwise是處理遺漏值的預設行為。 範例 圖1. 範例: 成對排除遺漏值 SOURCE: s = csvSource(file("mydata.csv"), missing.pairwise())...