Memory usage of imputation with mice in R
I am currently working on the imputation of 10 large datasets (by first creating a prediction matrix with correlation of 0.3, dfpred03) with mice in R and I am having a lot of issues like the following:
imptest <- mice(df, m=1, maxit = 1, method='cart',predictorMatrix=dfpred03)
iter imp variable
1 1 VAR1 VAR2 VAR3 VAR4Error: cannot allocate vector of size 446 Kb
Error during wrapup: cannot allocate vector of size 3.6 Mb
I understand I will have to make some concessions, but because I am not sure what the bottleneck is I do not really know which concessions to make.
Is there any documentation about how mice in R uses data?
My most important questions:
EDITED: How can I adapt the predictor matrix in order to make the imputation process less memory consuming? I am thinking of setting variables which have both low row and column sums to zero, but then I get left with NA's. Another thing I could do is to remove those variables and other variables with low observations.
If I succeed in doing one iteration of a dataset, would that mean that I can increase
maxitandmbecause the maximum memory usage is in the iteration?Is most memory used because of the amount of predictors or the amount of missingness in observations (and therefore the amount of observations)?
More generally, where does the spike in memory usage lie?
r memory out-of-memory imputation r-mice
asked Nov 18 '18 at 11:49
TomTom
343111
add a comment |
I am currently working on the imputation of 10 large datasets (by first creating a prediction matrix with correlation of 0.3, dfpred03) with mice in R and I am having a lot of issues like the following:
imptest <- mice(df, m=1, maxit = 1, method='cart',predictorMatrix=dfpred03)
iter imp variable
1 1 VAR1 VAR2 VAR3 VAR4Error: cannot allocate vector of size 446 Kb
Error during wrapup: cannot allocate vector of size 3.6 Mb
I understand I will have to make some concessions, but because I am not sure what the bottleneck is I do not really know which concessions to make.
Is there any documentation about how mice in R uses data?
My most important questions:
EDITED: How can I adapt the predictor matrix in order to make the imputation process less memory consuming? I am thinking of setting variables which have both low row and column sums to zero, but then I get left with NA's. Another thing I could do is to remove those variables and other variables with low observations.
If I succeed in doing one iteration of a dataset, would that mean that I can increase
maxitandmbecause the maximum memory usage is in the iteration?Is most memory used because of the amount of predictors or the amount of missingness in observations (and therefore the amount of observations)?
More generally, where does the spike in memory usage lie?
r memory out-of-memory imputation r-mice
asked Nov 18 '18 at 11:49
TomTom
343111
I think that it will take more memory, due to the way r manages matrices and data frame while sorting calculations, than it will just running the full matrix to completion.
– sconfluentus
Nov 18 '18 at 17:28
Thank you for your comment! I tried and I think you are right.. So I guess it would be better to remove parts of the dataset all together (for which there are few predictors or many entries missing)?
– Tom
Nov 18 '18 at 17:34
Is there anything else living in memory when you are doing this? If you save all your variable and data in.rdata( )thenrm()EVERYTHING from the environment and usegc(). Close all other applications. It will free up as much memory as possible
– sconfluentus
Nov 18 '18 at 17:40
No, everything else is removed, as per your suggestion. That's why I have to figure out other options..
– Tom
Nov 18 '18 at 17:46
add a comment |
I am currently working on the imputation of 10 large datasets (by first creating a prediction matrix with correlation of 0.3, dfpred03) with mice in R and I am having a lot of issues like the following:
imptest <- mice(df, m=1, maxit = 1, method='cart',predictorMatrix=dfpred03)
iter imp variable
1 1 VAR1 VAR2 VAR3 VAR4Error: cannot allocate vector of size 446 Kb
Error during wrapup: cannot allocate vector of size 3.6 Mb
I understand I will have to make some concessions, but because I am not sure what the bottleneck is I do not really know which concessions to make.
Is there any documentation about how mice in R uses data?
My most important questions:
EDITED: How can I adapt the predictor matrix in order to make the imputation process less memory consuming? I am thinking of setting variables which have both low row and column sums to zero, but then I get left with NA's. Another thing I could do is to remove those variables and other variables with low observations.
If I succeed in doing one iteration of a dataset, would that mean that I can increase
maxitandmbecause the maximum memory usage is in the iteration?Is most memory used because of the amount of predictors or the amount of missingness in observations (and therefore the amount of observations)?
More generally, where does the spike in memory usage lie?
r memory out-of-memory imputation r-mice
asked Nov 18 '18 at 11:49
TomTom
343111
I am currently working on the imputation of 10 large datasets (by first creating a prediction matrix with correlation of 0.3, dfpred03) with mice in R and I am having a lot of issues like the following:
imptest <- mice(df, m=1, maxit = 1, method='cart',predictorMatrix=dfpred03)
iter imp variable
1 1 VAR1 VAR2 VAR3 VAR4Error: cannot allocate vector of size 446 Kb
Error during wrapup: cannot allocate vector of size 3.6 Mb
I understand I will have to make some concessions, but because I am not sure what the bottleneck is I do not really know which concessions to make.
Is there any documentation about how mice in R uses data?
My most important questions:
EDITED: How can I adapt the predictor matrix in order to make the imputation process less memory consuming? I am thinking of setting variables which have both low row and column sums to zero, but then I get left with NA's. Another thing I could do is to remove those variables and other variables with low observations.
If I succeed in doing one iteration of a dataset, would that mean that I can increase
maxitandmbecause the maximum memory usage is in the iteration?Is most memory used because of the amount of predictors or the amount of missingness in observations (and therefore the amount of observations)?
More generally, where does the spike in memory usage lie?
r memory out-of-memory imputation r-mice
r memory out-of-memory imputation r-mice
asked Nov 18 '18 at 11:49
TomTom
343111
asked Nov 18 '18 at 11:49
TomTom
343111
edited Nov 19 '18 at 9:31
Tom
asked Nov 18 '18 at 11:49
TomTom
343111
asked Nov 18 '18 at 11:49
TomTom
343111
asked Nov 18 '18 at 11:49
TomTom
343111
343111
I think that it will take more memory, due to the way r manages matrices and data frame while sorting calculations, than it will just running the full matrix to completion.
– sconfluentus
Nov 18 '18 at 17:28
Thank you for your comment! I tried and I think you are right.. So I guess it would be better to remove parts of the dataset all together (for which there are few predictors or many entries missing)?
– Tom
Nov 18 '18 at 17:34
Is there anything else living in memory when you are doing this? If you save all your variable and data in.rdata( )thenrm()EVERYTHING from the environment and usegc(). Close all other applications. It will free up as much memory as possible
– sconfluentus
Nov 18 '18 at 17:40
No, everything else is removed, as per your suggestion. That's why I have to figure out other options..
– Tom
Nov 18 '18 at 17:46
add a comment |
I think that it will take more memory, due to the way r manages matrices and data frame while sorting calculations, than it will just running the full matrix to completion.
– sconfluentus
Nov 18 '18 at 17:28
Thank you for your comment! I tried and I think you are right.. So I guess it would be better to remove parts of the dataset all together (for which there are few predictors or many entries missing)?
– Tom
Nov 18 '18 at 17:34
Is there anything else living in memory when you are doing this? If you save all your variable and data in.rdata( )thenrm()EVERYTHING from the environment and usegc(). Close all other applications. It will free up as much memory as possible
– sconfluentus
Nov 18 '18 at 17:40
No, everything else is removed, as per your suggestion. That's why I have to figure out other options..
– Tom
Nov 18 '18 at 17:46
I think that it will take more memory, due to the way r manages matrices and data frame while sorting calculations, than it will just running the full matrix to completion.
– sconfluentus
Nov 18 '18 at 17:28
I think that it will take more memory, due to the way r manages matrices and data frame while sorting calculations, than it will just running the full matrix to completion.
– sconfluentus
Nov 18 '18 at 17:28
Thank you for your comment! I tried and I think you are right.. So I guess it would be better to remove parts of the dataset all together (for which there are few predictors or many entries missing)?
– Tom
Nov 18 '18 at 17:34
Thank you for your comment! I tried and I think you are right.. So I guess it would be better to remove parts of the dataset all together (for which there are few predictors or many entries missing)?
– Tom
Nov 18 '18 at 17:34
Is there anything else living in memory when you are doing this? If you save all your variable and data in
.rdata( ) then rm() EVERYTHING from the environment and use gc(). Close all other applications. It will free up as much memory as possible– sconfluentus
Nov 18 '18 at 17:40
Is there anything else living in memory when you are doing this? If you save all your variable and data in
.rdata( ) then rm() EVERYTHING from the environment and use gc(). Close all other applications. It will free up as much memory as possible– sconfluentus
Nov 18 '18 at 17:40
No, everything else is removed, as per your suggestion. That's why I have to figure out other options..
– Tom
Nov 18 '18 at 17:46
No, everything else is removed, as per your suggestion. That's why I have to figure out other options..
– Tom
Nov 18 '18 at 17:46
add a comment |
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I think that it will take more memory, due to the way r manages matrices and data frame while sorting calculations, than it will just running the full matrix to completion.
– sconfluentus
Nov 18 '18 at 17:28
Thank you for your comment! I tried and I think you are right.. So I guess it would be better to remove parts of the dataset all together (for which there are few predictors or many entries missing)?
– Tom
Nov 18 '18 at 17:34
Is there anything else living in memory when you are doing this? If you save all your variable and data in
.rdata( )thenrm()EVERYTHING from the environment and usegc(). Close all other applications. It will free up as much memory as possible– sconfluentus
Nov 18 '18 at 17:40
No, everything else is removed, as per your suggestion. That's why I have to figure out other options..
– Tom
Nov 18 '18 at 17:46