0
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My data consists of 202 cases, each stand for a single interview. The variables reflect the interviewers' and interviewees' behaviours during four different parts of the interview: p1, g1, pA, gA. in some interviews, certain parts were not carried out. part p1 wasn't carried out in one interview. part g1 wasn't conducted in 46 interviews. part pA wasn't conducted in 14 interviews and gA in 27.

In each part, Different variables are different facets of the same underlying concept (or latent variable). for example, all four variables belonging to part pA - pAx1, pAx2, pAx3, pAx4 - are different measures of the interviewee's cooperativeness during part pA.

I would like to impute the missing values while accounting for the fact that there is a pattern for values to be missing, such that if a value is missing for one variable of part pA, e.g., pAx1, that means that the other values pertaining to pA - pAx2, pAx3, pAx4 - are also necessarily missing. and so on.

How do I do that?

Help would be much appreciated!

this is my data -

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, 
NA, 0.08, 0.59, NA, 0.21, NA, 0.8, NA, 0.12, 0.08, 0.39, 
0.15, 0.41, 0.21, 0.73, 0.45, 0.52, 0.04, NA, 0.36, 0.43, 
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$\endgroup$
  • 1
    $\begingroup$ Your title indicates data missing not at random (MNAR), which means there's a systematic relationship between the missing values and the fact that they're missing. On the other hand, your post only suggests that there's a relationship between the missingness of particular measurements. This doesn't necessarily imply MNAR. So, are your data MNAR or not? The validity of various strategies for handling the missing data depends on this. $\endgroup$ – user20160 Aug 25 at 13:09
  • $\begingroup$ Thanks, user20160, and sorry for missing your comment earlier. I'll try to rephrase as I am not sure what is missing in my description. My variables can be seen as divided into four groups (interview parts). When there is a missing value, for any group, by definition, all variables pertaining to that group are missing. Hope that clears things up. Please let me know what is missing if it doesn't. $\endgroup$ – Uri Aug 25 at 18:05
  • $\begingroup$ Thanks. What I mean is that being MNAR isn't a consequence of the pattern of missingness. An example: 1) Suppose all parts of an interview were conducted on the same day. If a subject happens to be sick on that day, then all parts will be missing together. But, the reason they're missing has nothing to do with how the subject would have responded had they not been sick. So, this is not MNAR. $\endgroup$ – user20160 Aug 25 at 18:32
  • $\begingroup$ 2) Suppose subjects who use drugs are more likely to decline to answer questions about drug use. Drug-related questions will be missing together in these cases. This data is MNAR because the fact that data is missing is related to the missing values. Notice that there's a definitive pattern of missingness in both (1) and (2), but (2) is MNAR whereas (1) is not. $\endgroup$ – user20160 Aug 25 at 18:32
  • $\begingroup$ In my case, there is no reason in the data for why cases are missing, so I guess according to your explanation it isn't MNAR? How should imputation be dealt with in this case given the pattern I described? $\endgroup$ – Uri Aug 25 at 19:20
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There are methods such as K-Nearest Neighbours that can be used. They are described here: http://r-statistics.co/Missing-Value-Treatment-With-R.html

You would use the other variables to help you predict the missing values.

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    $\begingroup$ The validity of this approach depends on the missing data mechanism. E.g. it's not suited for data missing not at random (MNAR), which the OP's title suggests. But, they have yet to clarify the exact type of missing data. $\endgroup$ – user20160 Aug 25 at 13:13
  • $\begingroup$ thanks, william3031. This was very helpful! $\endgroup$ – Uri Aug 25 at 13:26

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