R 如何不随机地插补缺失值？

我的数据包括202个案例，每个案例代表一次访谈。变量反映了采访者和被采访者在采访的不同部分的行为：p1、g1、pA、gA。在一些采访中，某些部分没有进行。第一部分没有在一次采访中进行。46例未进行第g1部分检查。pA部分没有对14名受试者进行，gA部分没有对27名受试者进行

不同的变量是同一基本概念或潜在变量的不同方面。例如，属于pA部分的所有四个变量——pAx1、pAx2、pAx3、pAx4——都是在pA部分中对受访者合作性的不同衡量

我想

估算

缺失值，同时考虑到存在缺失值的模式这一事实，因此，如果部分pA变量（例如pAx1）缺失值，则与部分pA相关的其他值（pAx2、pAx3、pAx4）也必然缺失

非常感谢您的帮助

这是我的数据-

df <- structure(list(p1x1 = c(0.54, 0.77, 0.84, 0.84, 0.75, 0.35, 0.67, 
0.23, 0.9, 0.81, 0.76, 0.85, 0.61, 0.8, 0.1, 0.81, 0.96, 0.68, 
0.83, 0.8, 0.89, 0.85, 1, 0.83, 0.52, 0.74, 0.47, 0.51, 1, 0.83, 
0.93, 0, 0.31, 0.95, 0, 0.39, 0.84, 0.62, 0.81, 0.58, 0.7, 0.54, 
0.94, 0.76, 0.76, 0.14, 0.67, 0.65, 1, 0.69, 0.31, 0.43, 0.83, 
0.79, 0.94, 0.84, 0.28, 0.76, 0.78, 0.91, 0.89, 0.63, 0.76, 0.34, 
0.91, 1, 0.72, 0.89, 0.43, 0.85, 0.8, 0.45, 0.12, 0.19, 0.91, 
0.74, 0.88, 0.62, 0.92, 0.72, 0.54, 0.59, 0.74, 0.8, 1, 0.66, 
0.48, 0.7, 0.96, 0.87, 0.65, 0.61, 0.79, 0.8, 0.93, 0.83, 0.88, 
0.76, 0.58, 0.79, 0.65, 0.88, 0.37, 0.74, 0.63, 0.64, 0.58, 0.86, 
0.62, 0.57, 0.09, 0.61, 0.29, 0.9, 0.91, 0.73, 0.92, 0.9, 0.56, 
0.89, 0.89, 0.62, 0.24, 0.65, 0.76, 0.69, 0.42, 0.8, 0.39, 0.58, 
0.72, 0.73, 0.48, NA, 0.5, 0.72, 0.91, 0.58, 0.8, 0, 0.47, 0.5, 
0.85, 0.93, 0.81, 0.89, 0.93, 0.55, 0.78, 0.72, 0.77, 0.44, 0.57, 
0.78, 0.84, 0.83, 0.62, 0.3, 0.67, 0.96, 0.62, 0.73, 0.29, 0.76, 
0.86, 0.7, 0.54, 0.28, 0.74, 0.67, 0.17, 0.05, 0.62, 0.76, 0.73, 
1, 0.7, 0.92, 0.31, 1, 0.33, 0.59, 0.62, 0.78, 0.26, 0.76, 0.7, 
0.81, 0.82, 0.81, 0.83, 0.3, 0.79, 0, 0.72, 0.67, 0.78, 0.11, 
0.32, 0.39, 0.6, 0.7), p1x2 = c(0, 0.08, 0.32, 0.11, 0.12, 0, 
0.17, 0.08, 0.38, 0.12, 0, 0.15, 0.25, 0.05, 0, 0.15, 0.13, 0.08, 
0.08, 0.13, 0.06, 0.46, 0.21, 0.14, 0.19, 0.11, 0.24, 0.08, 0.36, 
0.08, 0.29, 0, 0, 0.14, 0, 0.07, 0.16, 0.04, 0.33, 0.32, 0.22, 
0.08, 0.29, 0.06, 0.43, 0.07, 0.06, 0.16, 0.18, 0.19, 0.08, 0.1, 
0.17, 0.21, 0.06, 0.11, 0.06, 0.24, 0.22, 0.13, 0.21, 0.26, 0.1, 
0, 0.23, 0.44, 0.21, 0.16, 0, 0.15, 0.4, 0.07, 0, 0, 0.31, 0.1, 
0.38, 0.43, 0.16, 0.12, 0.12, 0.18, 0.3, 0.45, 0.33, 0.02, 0.19, 
0.15, 0.15, 0.2, 0.02, 0.04, 0.21, 0.27, 0.07, 0.14, 0.06, 0.05, 
0.37, 0.05, 0.35, 0.25, 0.21, 0.09, 0.08, 0.08, 0.06, 0.71, 0.04, 
0.05, 0, 0.04, 0.32, 0.4, 0.55, 0.12, 0.08, 0, 0.19, 0.33, 0.11, 
0.06, 0.02, 0.29, 0.12, 0.03, 0.04, 0.33, 0.27, 0.25, 0, 0, 0.19, 
NA, 0.08, 0.32, 0.48, 0.08, 0.07, 0, 0.11, 0.17, 0.2, 0.33, 0.19, 
0.22, 0.33, 0.09, 0.28, 0.28, 0, 0.44, 0.27, 0.17, 0.32, 0.06, 
0.29, 0, 0.1, 0.25, 0.22, 0.45, 0, 0.09, 0.14, 0.33, 0, 0.24, 
0.21, 0.06, 0, 0, 0.5, 0.52, 0.36, 0.4, 0.2, 0.33, 0.14, 0.12, 
0.08, 0.17, 0.31, 0, 0, 0.16, 0.02, 0, 0.45, 0.19, 0, 0, 0.02, 
0, 0.25, 0.43, 0.39, 0, 0.21, 0, 0.02, 0.25), p1x3 = c(0.46, 
0.12, 0.21, 0.47, 0.29, 0.4, 0.33, 0.38, 0.21, 0.12, 0.41, 0.1, 
0.29, 0.45, 0.9, 0.3, 0.22, 0.18, 0, 0.27, 0.17, 0.23, 0, 0.28, 
0.19, 0.16, 0.59, 0.38, 0.07, 0.25, 0.36, 1, 0.75, 0.14, 1, 0.43, 
0.21, 0.42, 0.1, 0.42, 0.39, 0.53, 0.06, 0.35, 0.33, 0.64, 0.28, 
0.29, 0.24, 0.19, 0.69, 0.61, 0.08, 0.37, 0.06, 0.26, 0.56, 0.34, 
0.48, 0.17, 0.25, 0.11, 0.14, 0.24, 0.14, 0.07, 0.28, 0.37, 0.46, 
0.35, 0.6, 0.52, 0.81, 0.39, 0.07, 0.23, 0.08, 0.19, 0.08, 0.44, 
0.73, 0.3, 0.11, 0.15, 0.25, 0.32, 0.24, 0.44, 0.07, 0.13, 0.22, 
0.26, 0.29, 0.2, 0.29, 0.28, 0.06, 0.29, 0.42, 0.05, 0.6, 0.25, 
0.68, 0.26, 0.42, 0.31, 0.36, 0.14, 0.29, 0.03, 0.5, 0.14, 0.54, 
0.3, 0.05, 0.35, 0.38, 0.3, 0.06, 0.11, 0.3, 0.41, 0.44, 0.47, 
0.18, 0.28, 0.67, 0, 0.45, 0.25, 0.28, 0.27, 0.24, NA, 0.42, 
0.24, 0.48, 0.21, 0.2, 1, 0.79, 0.33, 0.1, 0.07, 0.19, 0.28, 
0.13, 0.45, 0.17, 0.17, 0.08, 0.62, 0.2, 0.26, 0.12, 0.17, 0.29, 
0.7, 0.33, 0.04, 0.38, 0.18, 0.71, 0.24, 0.21, 0.41, 0.31, 0.56, 
0, 0.39, 0.83, 0.65, 0.62, 0, 0.32, 0, 0.4, 0.08, 0.43, 0.65, 
0.25, 0.28, 0.31, 0.09, 0.71, 0.08, 0.09, 0.17, 0.09, 0.24, 0.33, 
0.52, 0.21, 1, 0.28, 0, 0.22, 0.89, 0.32, 0.48, 0.53, 0.45), 
p1x4 = c(0, 0.71, 0.78, 0.73, 0.73, 0.75, NA, 0, 0.78, 1, 
0.8, 0.71, 0.88, 0.9, NA, 0.73, 1, 0.57, 0.83, 0.67, 0.67, 
1, 1, 0.47, 0, 0.86, NA, 0.4, 0.88, 0.86, 1, NA, 0.33, 0.73, 
0, 0.28, 0.89, 0.62, 0.45, 0.4, 0.75, 0.42, 0.8, 0.5, 0.67, 
0.33, 0.54, 0.25, 0.9, 0.54, NA, 0.33, 0, 0.67, 0.82, 0.62, 
NA, 0.62, 0.5, NA, 0.81, 0, 0.6, 0, 0.88, 0, 0.45, 0.8, 0, 
0.89, NA, 0.47, NA, 0.3, 0.25, NA, 0, 0, 0.82, 0, 0.5, 0.53, 
0.61, 0.58, 1, 0, 0.23, 0.53, 0.78, 0, 0.33, 0.57, 0.57, 
0.89, 1, 0.6, 0.88, 0.9, 0.5, 0.56, 0.42, 0.75, NA, 0.71, 
0, 0.59, NA, NA, 0.33, 0.4, 0.22, 0.33, 0.3, 0.86, 0.7, 0.78, 
1, 0.92, 0, 0.89, 0.61, 0.6, 0.16, 0.4, 0.55, 0, 0.36, 0.6, 
0, 0.43, 0.5, 0.42, 0.36, NA, 0.33, 0.8, 0.81, 0, 0.62, 0, 
0.56, 0.6, 0, 0.88, 0.67, 0.83, 1, 0.36, 0, 0.4, 0, 0.29, 
0.45, 0.82, 0.67, 0.8, 0.59, 0.17, 0.24, 0, 0, 0.69, 0.25, 
0.56, 0.38, 0.64, NA, 0, 0.64, 0.75, NA, NA, 0.44, 0.65, 
0.67, 1, 0.78, NA, 0.17, 0.9, 0, 0.53, 0.22, 1, 0, 0, 0.53, 
0.56, 1, 0.77, 0, 0, 0, NA, 0.73, 0.33, 0.71, NA, 0, 0, 0.46, 
0.78), p1y1 = c(0.42, 0.27, 0.63, 0.32, 0.46, 0.8, 0.5, 0.31, 
0.59, 0.38, 0.24, 0.55, 0.71, 0.7, 0.8, 0.59, 0.35, 0.08, 
0.33, 0.6, 0.22, 0.46, 0.43, 0.38, 0.33, 0.32, 0.41, 0.24, 
0.43, 0.33, 0.64, 1, 0.44, 0.33, 0.5, 0.25, 0.53, 0.29, 0.33, 
0.89, 0.26, 0.34, 0.59, 0.35, 0.48, 0.43, 0.44, 0.45, 0.53, 
0.46, 0.69, 0.18, 0.54, 0.32, 0.41, 0.58, 0.17, 0.28, 0.26, 
0.35, 0.43, 0.58, 0.33, 0.07, 0.27, 0.59, 0.59, 0.58, 0.14, 
0.54, 1, 0.24, 0.35, 0.24, 0.29, 0.13, 0.88, 0.38, 0.48, 
0.16, 0.35, 0.36, 0.41, 0.45, 1, 0.22, 0.33, 0.22, 0.15, 
0.27, 0.02, 0.35, 0.57, 0.6, 0.5, 0.52, 0.41, 0.57, 0.42, 
0.53, 0.35, 0.31, 0.58, 0.34, 0.37, 0.5, 0.44, 0.71, 0.46, 
0.16, 0.32, 0.39, 0.43, 0.6, 0.86, 0.38, 0.33, 0.55, 0.5, 
0.56, 0.19, 0.38, 0.13, 0.53, 0.65, 0.22, 0.46, 0.4, 0.42, 
0.5, 0.32, 0.42, 0.33, 0, 0.5, 0.56, 0.26, 0.12, 0.47, 0.5, 
0.53, 0, 0.55, 0.4, 0.29, 0.17, 0.33, 0.45, 0.72, 0.33, 0.77, 
0.75, 0.6, 0.25, 0.48, 1, 0.33, 0.5, 0.59, 0.38, 0.22, 0.45, 
0.35, 0.24, 0.57, 0.48, 0.31, 0.36, 0.32, 0.56, 0.46, 0.25, 
0.25, 0.64, 0.91, 0.67, 0.5, 0.92, 0.17, 0.47, 0.83, 0.24, 
0.23, 0.43, 0.32, 0.55, 0.14, 0.09, 0.73, 0.29, 0.39, 0.39, 
0.32, 1.2, 0.39, 0.48, 0.39, 0.33, 0.74, 0.55, 0.29, 0.6), 
g1y2 = c(0.46, 0.79, 0.83, 0.44, NA, 0.84, NA, NA, 1.44, 
0.55, 0.86, 0.35, 0.63, 1.05, NA, 1.45, 0.67, 0.85, 0.45, 
1.13, 0.42, 0.45, 0.6, 1.12, 1, 0.63, NA, NA, 0.68, 1.09, 
1.28, NA, 1.17, 0.93, NA, 0.45, 0.5, 1.06, 0.51, 0.86, 1.09, 
1.28, 0.83, 0.94, 1.1, NA, 0.95, NA, 1.1, 0.94, NA, 0.31, 
1.33, 0.97, 0.57, 0.94, NA, NA, 0.79, NA, 1.02, 0.62, 1.11, 
0.52, 0.97, 0.89, NA, 1, 0.46, 0.85, NA, 0.5, NA, 1.25, 0.75, 
NA, 0.71, 1, 0.6, 0.51, 0.8, 0.86, 1.03, 0.8, 0.79, 0.6, 
NA, 0.87, 0.57, 0.36, 0.64, 0.43, 0.88, 1.14, 0.76, NA, 0.71, 
0.77, 0.7, 0, 0.94, 0.93, NA, 0.47, NA, 0.98, NA, NA, NA, 
0.44, 1, 0.62, 0.7, 0.96, 0.94, 0.74, 0.65, 0.86, 1.5, 0.92, 
NA, 1.11, 0.75, 1.09, 0.79, 0.6, 0.75, 0.71, NA, 0.62, 1.08, 
0.58, 0.62, NA, 0.67, 1.11, 1.11, 0.32, 0.77, NA, 1.5, 0.47, 
NA, 0.93, NA, 0.4, NA, 0.94, 1, 0.72, 0.85, 0.73, 0.79, 0.32, 
0.81, 0.92, 0.93, NA, 1, 0.7, 0.88, 1, NA, 0.85, 1, 0.92, 
0.67, NA, 0.68, 0.64, NA, NA, 0.67, 1, NA, 1.08, 1.21, NA, 
NA, 1, NA, 0.72, 0.5, 0.95, 1, 0.79, 0.65, 0.72, 1.03, 0.86, 
0.84, NA, 1.11, NA, 0.97, NA, 0.85, NA, NA, 1.22, 0.31, 0.81
), g1y3 = c(0.21, 0.05, 0.13, 0, NA, 0.18, NA, NA, 0.12, 
0.1, 0.27, 0.08, 0.11, 0.35, NA, 0.36, 0.33, 0.03, 0.27, 
0.13, 0.17, 0.05, 0.4, 0.06, 0.5, 0.07, NA, NA, 0.08, 0.18, 
0.11, NA, 0.5, 0.13, NA, 0.27, 0.17, 0.06, 0.14, 0.29, 0.18, 
0.05, 0.12, 0.19, 0.05, NA, 0.2, NA, 0.3, 0.28, NA, 0.38, 
0.33, 0.12, 0.05, 0.29, NA, NA, 0.15, NA, 0.07, 0.12, 0.06, 
0, 0.05, 0.09, NA, 0.09, 0, 0.15, NA, 0.12, NA, 0.12, 0.12, 
NA, 0.06, 0.25, 0.08, 0, 0.06, 0.14, 0.09, 0.16, 0.07, 0.07, 
NA, 0.1, 0.11, 0.36, 0.06, 0.29, 0.19, 0.14, 0.05, NA, 0.09, 
0.04, 0.04, 0, 0.1, 0.21, NA, 0.07, NA, 0.14, NA, NA, NA, 
0.08, 0, 0.23, 0.03, 0.15, 0.18, 0.04, 0.15, 0.1, 0.5, 0.08, 
NA, 0.05, 0.5, 0.27, 0.03, 0.1, 0.09, 0.18, NA, 0.1, 0.15, 
0.18, 0.23, NA, 0.1, 0.05, 0.33, 0.05, 0.31, NA, 0.08, 0, 
NA, 0.31, NA, 0.2, NA, 0.18, 0.17, 0.11, 0.15, 0.04, 0.14, 
0.09, 0.06, 0.08, 0.21, NA, 0.12, 0.04, 0.27, 0.14, NA, 0.07, 
0.11, 0.12, 0, NA, 0.04, 0.18, NA, NA, 0.09, 0.17, NA, 0.08, 
0.12, NA, NA, 0.15, NA, 0.13, 0.3, 0.09, 0.12, 0.09, 0.18, 
0.1, 0.16, 0.29, 0.05, NA, 0.17, NA, 0.06, NA, 0.08, NA, 
NA, 0.11, 0.2, 0.19), g1y4 = c(0, 0, 0, 0, NA, 0, NA, NA, 
0, 0, 0, 0, 0, 0, NA, 0, 0, 0.17, 0, 0, 0, 0, 0, 0, 0, 0, 
NA, NA, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, NA, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, NA, NA, 0, NA, 0, 
0, 0, 0.1, 0, 0, NA, 0, 0, 0, NA, 0, NA, 0, 0, NA, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 
0, 0, 0, 0, 0, NA, 0, NA, 0, NA, NA, NA, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA, 
0, 0.08, 0, 0, 0, NA, 0, 0, NA, 0, NA, 0, NA, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, 0, 0, NA, 0, 
0, NA, NA, 0, 0, NA, 0, 0, NA, NA, 0, NA, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, NA, 0, NA, 0, NA, 0, NA, NA, 0, 0, 0), g1y5 = c(0.21, 
0.11, 0.13, 0.25, NA, 0, NA, NA, 0.12, 0.25, 0, 0.23, 0.37, 
0.05, NA, 0, 0, 0.1, 0.18, 0.13, 0.33, 0.36, 0.1, 0.06, 0, 
0.2, NA, NA, 0.16, 0, 0, NA, 0.17, 0, NA, 0.09, 0.2, 0.06, 
0.3, 0.14, 0, 0, 0.12, 0.25, 0, NA, 0, NA, 0, 0.06, NA, 0.23, 
0, 0, 0.3, 0, NA, NA, 0.06, NA, 0, 0.5, 0.03, 0.07, 0.28, 
0.08, NA, 0.15, 0.15, 0, NA, 0.31, NA, 0, 0, NA, 0.37, 0, 
0.2, 0.34, 0.1, 0, 0, 0, 0.21, 0.37, NA, 0.03, 0.18, 0.18, 
0.24, 0.21, 0, 0, 0.05, NA, 0.13, 0.12, 0.32, 0, 0, 0, NA, 
0.25, NA, 0, NA, NA, NA, 0.28, 0, 0.15, 0.22, 0, 0.12, 0.13, 
0.15, 0, 0, 0, NA, 0, 0, 0, 0.24, 0.4, 0.06, 0.18, NA, 0.38, 
0, 0.21, 0, NA, 0.29, 0.02, 0, 0.26, 0, NA, 0, 0.35, NA, 
0, NA, 0.2, NA, 0, 0, 0, 0, 0.12, 0, 0.5, 0.1, 0.2, 0, NA, 
0.08, 0.36, 0, 0, NA, 0.07, 0, 0.08, 0, NA, 0.28, 0.11, NA, 
NA, 0.03, 0, NA, 0, 0, NA, NA, 0, NA, 0.06, 0.1, 0, 0, 0.27, 
0.11, 0.17, 0.08, 0, 0.11, NA, 0, NA, 0, NA, 0.15, NA, NA, 
0, 0.4, 0), g1y6 = c(0.68, 0.47, 0.43, 0.44, NA, 0.47, NA, 
NA, 0.44, 0.65, 0.32, 0.77, 0.63, 0.7, NA, 0.45, 0.67, 0.24, 
0.91, 0.47, 0.92, 0.77, 0.8, 0.21, 0.5, 0.6, NA, NA, 0.43, 
0.18, 0.22, NA, 1, 0.13, NA, 0.73, 0.67, 0.31, 0.6, 0.43, 
0.27, 0.26, 0.5, 0.75, 0.08, NA, 0.2, NA, 0.5, 0.44, NA, 
0.85, 0.33, 0.34, 0.54, 0.29, NA, NA, 0.3, NA, 0.13, 0.75, 
0.17, 0.57, 0.44, 0.28, NA, 0.5, 0.46, 0.38, NA, 0.69, NA, 
0.25, 0.62, NA, 0.57, 0.25, 0.52, 0.54, 0.29, 0.14, 0.11, 
0.32, 0.55, 0.53, NA, 0.27, 0.5, 0.91, 0.52, 0.86, 0.44, 
0.14, 0.3, NA, 0.38, 0.31, 0.56, 1, 0.16, 0.29, NA, 0.6, 
NA, 0.14, NA, NA, NA, 0.68, 0.29, 0.77, 0.46, 0.19, 0.47, 
0.35, 0.8, 0.28, 0.5, 0.15, NA, 0.05, 0.5, 0.36, 0.47, 0.7, 
0.31, 0.53, NA, 0.71, 0.31, 0.61, 0.69, NA, 0.62, 0.11, 0.33, 
0.84, 0.43, NA, 0.17, 0.59, NA, 0.52, NA, 1, NA, 0.29, 0.25, 
0.5, 0.31, 0.45, 0.36, 0.82, 0.52, 0.6, 0.25, NA, 0.48, 0.47, 
0.39, 0.23, NA, 0.26, 0.11, 0.33, 0.67, NA, 0.44, 0.46, NA, 
NA, 0.42, 0.17, NA, 0.17, 0.25, NA, NA, 0.23, NA, 0.32, 0.7, 
0.32, 0.12, 0.45, 0.49, 0.45, 0.32, 0.43, 0.37, NA, 0.39, 
NA, 0.11, NA, 0.35, NA, NA, 0.11, 0.8, 0.31), g1y7 = c(0.46, 
0.42, 0.3, 0.44, NA, 0.29, NA, NA, 0.31, 0.55, 0.05, 0.69, 
0.53, 0.35, NA, 0.09, 0.33, 0.21, 0.64, 0.33, 0.75, 0.73, 
0.4, 0.15, 0, 0.53, NA, NA, 0.35, 0, 0.11, NA, 0.5, 0, NA, 
0.45, 0.5, 0.25, 0.47, 0.14, 0.09, 0.21, 0.38, 0.56, 0.02, 
NA, 0, NA, 0.2, 0.17, NA, 0.46, 0, 0.22, 0.49, 0, NA, NA, 
0.15, NA, 0.07, 0.62, 0.11, 0.57, 0.38, 0.19, NA, 0.41, 0.46, 
0.23, NA, 0.56, NA, 0.12, 0.5, NA, 0.51, 0, 0.44, 0.54, 0.22, 
0, 0.03, 0.16, 0.48, 0.47, NA, 0.17, 0.39, 0.55, 0.45, 0.57, 
0.25, 0, 0.24, NA, 0.29, 0.27, 0.52, 1, 0.06, 0.07, NA, 0.53, 
NA, 0, NA, NA, NA, 0.6, 0.29, 0.54, 0.43, 0.04, 0.29, 0.3, 
0.65, 0.17, 0, 0.08, NA, 0, 0, 0.09, 0.44, 0.6, 0.22, 0.35, 
NA, 0.62, 0.15, 0.42, 0.46, NA, 0.52, 0.06, 0, 0.79, 0.11, 
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---

df如果插补值是有意义的，那么即使你没有零件的4个问题，你也可以使用变量之间的关系和观察值之间的相似性来预测它们。
为了考虑共线性，可以使用基于低秩的方法，
例如，可以使用imputePCA或imputeMFA函数查看missMDA包，此外，您还可以查看网站


供参考，
最好的，
JJ
如果估算值是合理的，那么即使你没有一部分的4个问题，你也可以使用变量之间的关系和观察值之间的相似性来预测它们。
为了考虑共线性，可以使用基于低秩的方法，
例如，可以使用imputePCA或imputeMFA函数查看missMDA包，此外，您还可以查看网站


供参考，
最好的，
JJ
在没有很好地理解您的确切问题的情况下，您是否研究了问题？详细说明方法和包装的论文。您的陈述“如果pA部分变量（如pAx1）缺少一个值，则意味着与pA相关的其他值（pAx2、pAx3、pAx4）也缺少”。。。这是否意味着当你在一个变量而不是其他变量中有缺失时，你想为该主题放弃其他变量？（参见
df[1:7,1:4]
了解这方面的第一个示例。）“考虑到存在缺失值的模式这一事实”表明，您希望在缺失本身中找到模式，并以某种方式将值填入这些间隙中，以便。。。保持这种模式。是吗？谢谢，里奇。我用的是老鼠。据我所知，MICE的算法在插补时会删除线性依赖的变量，我怀疑在我的情况下会这样，我不确定这是正确的解决方案。事实上，这就是我要问的，如果MICE处理我的数据的方法是正确的。谢谢，伊万斯。我的意思是，对于给定的面试，四个变量中不可能只有一个缺少值。如果一个部分在给定的访谈中没有携带一个（例如，pA），则属于该部分的所有变量（例如，pAx1，…pAx4）必须缺失。这就是我刚才描述的模式。问题是，我必须考虑这一事实，以及如何在不知不觉中理解你的确切问题，你看了吗？详细说明方法和包装的论文。您的陈述“如果pA部分变量（如pAx1）缺少一个值，则意味着与pA相关的其他值（pAx2、pAx3、pAx4）也缺少”。。。这是否意味着当你在一个变量而不是其他变量中有缺失时，你想为该主题放弃其他变量？（参见
df[1:7,1:4]
了解这方面的第一个示例。）“考虑到存在缺失值的模式这一事实”表明，您希望在缺失本身中找到模式，并以某种方式将值填入这些间隙中，以便。。。保持这种模式。是吗？谢谢，里奇。我用的是老鼠。据我所知，MICE的算法在插补时会删除线性依赖的变量，我怀疑在我的情况下会这样，我不确定这是正确的解决方案。事实上，这就是我要问的，如果MICE处理我的数据的方法是正确的。谢谢，伊万斯。我的意思是，对于给定的面试，四个变量中不可能只有一个缺少值。如果一个部分在给定的访谈中没有携带一个（例如，pA），则属于该部分的所有变量（例如，pAx1，…pAx4）必须缺失。这就是我刚才描述的模式。问题是，我必须考虑这一事实，以及如何在归入时，朱莉，是否有一种方法来检索维度的内容，使用<代码>估计NCPPCA < /代码>，即在每个维度中涉及哪些变量？ThanksJulie，是否有方法检索使用estim_ncpPCA估算的维度内容，即每个维度涉及哪些变量？谢谢