I have had a similar problem in MICE, see my self-discussion here. The problem occurs because you have overfitted your model (too many parameters, variables), some variables are highly colinear or you have cases that have missings on all variables.

In my case the model was overfitted. One way to solve this issue is by adjusting the predictor matrix of MICE. You may give imp$pred where impis your mids object, to look at the predictor matrix. You can use

new.pred<-quickpred(data)

mice(...,pred=new.pred)

to automatically generate a predictor matrix based on the bivariate correlations of the variables in the data (eg Pearson, Spearman), where .10 is the default cutoff. This may solve your problem. More generally build your models wisely and do not just include all variables you may have.

I have had a similar problem in MICE, see my self-discussion here. The problem occurs because you have overfitted your model (too many parameters, variables), some variables are highly colinear or you have cases that have missings on all variables.

In my case the model was overfitted. One way to solve this issue is by adjusting the predictor matrix of MICE. You may give imp$pred where impis your mids object, to look at the predictor matrix. You can use

new.pred <- quickpred(data)

mice(..., pred=new.pred)

to automatically generate a predictor matrix based on the bivariate correlations of the variables in the data (eg Pearson, Spearman), where .10 is the default cutoff. This may solve your problem. More generally build your models wisely and do not just include all variables you may have.

I have had a similar problem in MICE, see my self-discussion here. The problem occurs because you have overfitted your model (too many parameters, variables), some variables are highly colinear or you have cases that have missings on all variables.

In my case the model was overfitted. One way to solve this issue is by adjusting the predictor matrix of MICE. You may give imp$pred where impis your mids object, to look at the predictor matrix. You can use

new.pred<-quickpred(data)

mice(...,pred=new.pred)

to automatically generate a predictor matrix based on the bivariate correlations of the variables in the data (eg Pearson, Spearman), where .10 is the default cutoff. This may solve your problem. More generally build your models wisely and do not just include all variables you may have.

I have had a similar problem in MICE, see my self-discussion here. The problem occurs because you have overfitted your model (too many parameters, variables), some variables are highly colinear or you have cases that have missings on all variables.

In my case the model was overfitted. One way to solve this issue is by adjusting the predictor matrix of MICE. You may give imp$pred where impis your mids object, to look at the predictor matrix. You can use

new.pred<-quickpred(data)

mice(...,pred=new.pred)

to automatically generate a predictor matrix based on the bivariate correlations of the variables in the data (eg Pearson, Spearman), where .10 is the default cutoff. This may solve your problem. More generally build your models wisely and do not just include all variables you may have.