如何不随机地估算缺失值？
How to impute missing values not at random?
我的数据包含 202 个案例，每个案例代表一次采访。这些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。在一些访谈中，某些部分没有进行。 p1部分没有在一次采访中进行。在 46 个案例中没有进行 g1 部分。 pA 部分未对 14 名受试者进行，gA 部分未对 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 - 是受访者在 pA 部分合作的不同衡量标准。
我想 impute 缺失值，同时考虑到存在缺失值模式的事实，这样如果 pA 部分的变量缺失值，例如 pAx1 ，那么，与 pA 部分有关的其他值 - pAx2、pAx3、pAx4 - 也必然缺失。
不胜感激！
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,
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1, 0.7, NA, 0.4, 0.69, 0.89, 0.36, 0.67, 0.87, 0.38, 1.08,
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NA, 0, 0, 0, 0, 0, 0.33, 0.12, 0, 0.08, NA, 0.13, 0.14, 0.5,
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NA, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0.57, 0, 0, 0, 0, 0, 0, NA,
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0.08, 0, NA, 0, 0, 0.08, 0.15, 0, 0, 0.13, NA, 0, 0.19, 0,
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0.09, 0.29, 0.2, 0.25, 0.08, 0.18, NA, 0.27, 0.29, 0, NA,
0, 0, 0, 0, 0), gAy6 = c(NA, 0.8, 0.27, 0.67, 0.37, 1, NA,
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0.71, 1, 0.3, 1, 0, 0.29, 0.2, 0.5, NA, 0, 0.2, 0, 0.5, 0.33,
NA, 0.78, 0, 0.11, 0.5, 0.57, 0.64, 0.75, 0.24, 0.6, 0.88,
0.7, 0, 0, NA, 0.23, 0.54, 0.5, 0.5, 0.6, 0.33, NA, 0.8,
0.69, 0.14, 0.36, 0.57, 0.4, 0.62, 0.08, 0.27, NA, 0.47,
0.71, 0.33, NA, 0, 0.27, 0.15, 0.95, 0)), row.names = c(NA,
-202L), class = "data.frame")
如果估算值有意义，那么即使您没有部分的 4 个问题，您也可以使用变量之间的关系和观察之间的相似性来预测它们。
要考虑共线性，您可以使用基于低秩的方法，
例如，查看具有 imputePCA 或 imputeMFA 功能的包 missMDA，此外您还可以查看网站
https://rmisstastic.netlify.com/
有关信息，
最好，
贾杰
我的数据包含 202 个案例，每个案例代表一次采访。这些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。在一些访谈中，某些部分没有进行。 p1部分没有在一次采访中进行。在 46 个案例中没有进行 g1 部分。 pA 部分未对 14 名受试者进行，gA 部分未对 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 - 是受访者在 pA 部分合作的不同衡量标准。
我想 impute 缺失值，同时考虑到存在缺失值模式的事实，这样如果 pA 部分的变量缺失值，例如 pAx1 ，那么，与 pA 部分有关的其他值 - pAx2、pAx3、pAx4 - 也必然缺失。
不胜感激！
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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-202L), class = "data.frame")
如果估算值有意义，那么即使您没有部分的 4 个问题，您也可以使用变量之间的关系和观察之间的相似性来预测它们。 要考虑共线性，您可以使用基于低秩的方法， 例如，查看具有 imputePCA 或 imputeMFA 功能的包 missMDA，此外您还可以查看网站
https://rmisstastic.netlify.com/
有关信息， 最好， 贾杰