What does MICE LoggedEvents mean?
I am trying to run a multiple imputation using the mice function (from the package of the same name) in R. I get a Warning that events have been logged. Here is the output from mice(.)$loggedEvents from my MWE (see below):
it im dep meth out
1 1 X pmm H
I'm not sure what is causing this warning and what the implications are. From what I understand, this can be caused by collinearity amongst variables, but this should be prevented by using remove_collinear=FALSE, but this isn't fixing the Warning.
MWE:
Pop <- data.frame(X = c( NA, 0.02, -1.15, 0.54, -0.61, -2.07),
Z = c( 0.83, 1.40, -3.07, -0.07, -0.20, -1.90),
D = c( 0, 0, 0, 1, 0, 0),
H = c( 0.01, 0.01, 0.01, 0.01, 0.02, 0.02))
Pop.Imp <- mice(Pop, m = 1, maxit = 1, print = T)
Obviously my original issue involved much more rows and columns of data and a higher number of imputations and iterations, but I've managed to trim this down to find this MWE.
Any help into figuring out what's causing this problem would be great. Is there some sort of cut-off that mice uses when deciding if/when a covariable is collinear? If it's very high, would this override the remove_collinear=FALSE parameter?
r r-mice
asked Nov 20 at 22:00
Michael Barrowman
186
add a comment |
I am trying to run a multiple imputation using the mice function (from the package of the same name) in R. I get a Warning that events have been logged. Here is the output from mice(.)$loggedEvents from my MWE (see below):
it im dep meth out
1 1 X pmm H
I'm not sure what is causing this warning and what the implications are. From what I understand, this can be caused by collinearity amongst variables, but this should be prevented by using remove_collinear=FALSE, but this isn't fixing the Warning.
MWE:
Pop <- data.frame(X = c( NA, 0.02, -1.15, 0.54, -0.61, -2.07),
Z = c( 0.83, 1.40, -3.07, -0.07, -0.20, -1.90),
D = c( 0, 0, 0, 1, 0, 0),
H = c( 0.01, 0.01, 0.01, 0.01, 0.02, 0.02))
Pop.Imp <- mice(Pop, m = 1, maxit = 1, print = T)
Obviously my original issue involved much more rows and columns of data and a higher number of imputations and iterations, but I've managed to trim this down to find this MWE.
Any help into figuring out what's causing this problem would be great. Is there some sort of cut-off that mice uses when deciding if/when a covariable is collinear? If it's very high, would this override the remove_collinear=FALSE parameter?
r r-mice
asked Nov 20 at 22:00
Michael Barrowman
186
add a comment |
I am trying to run a multiple imputation using the mice function (from the package of the same name) in R. I get a Warning that events have been logged. Here is the output from mice(.)$loggedEvents from my MWE (see below):
it im dep meth out
1 1 X pmm H
I'm not sure what is causing this warning and what the implications are. From what I understand, this can be caused by collinearity amongst variables, but this should be prevented by using remove_collinear=FALSE, but this isn't fixing the Warning.
MWE:
Pop <- data.frame(X = c( NA, 0.02, -1.15, 0.54, -0.61, -2.07),
Z = c( 0.83, 1.40, -3.07, -0.07, -0.20, -1.90),
D = c( 0, 0, 0, 1, 0, 0),
H = c( 0.01, 0.01, 0.01, 0.01, 0.02, 0.02))
Pop.Imp <- mice(Pop, m = 1, maxit = 1, print = T)
Obviously my original issue involved much more rows and columns of data and a higher number of imputations and iterations, but I've managed to trim this down to find this MWE.
Any help into figuring out what's causing this problem would be great. Is there some sort of cut-off that mice uses when deciding if/when a covariable is collinear? If it's very high, would this override the remove_collinear=FALSE parameter?
r r-mice
asked Nov 20 at 22:00
Michael Barrowman
186
I am trying to run a multiple imputation using the mice function (from the package of the same name) in R. I get a Warning that events have been logged. Here is the output from mice(.)$loggedEvents from my MWE (see below):
it im dep meth out
1 1 X pmm H
I'm not sure what is causing this warning and what the implications are. From what I understand, this can be caused by collinearity amongst variables, but this should be prevented by using remove_collinear=FALSE, but this isn't fixing the Warning.
MWE:
Pop <- data.frame(X = c( NA, 0.02, -1.15, 0.54, -0.61, -2.07),
Z = c( 0.83, 1.40, -3.07, -0.07, -0.20, -1.90),
D = c( 0, 0, 0, 1, 0, 0),
H = c( 0.01, 0.01, 0.01, 0.01, 0.02, 0.02))
Pop.Imp <- mice(Pop, m = 1, maxit = 1, print = T)
Obviously my original issue involved much more rows and columns of data and a higher number of imputations and iterations, but I've managed to trim this down to find this MWE.
Any help into figuring out what's causing this problem would be great. Is there some sort of cut-off that mice uses when deciding if/when a covariable is collinear? If it's very high, would this override the remove_collinear=FALSE parameter?
r r-mice
r r-mice
asked Nov 20 at 22:00
Michael Barrowman
186
asked Nov 20 at 22:00
Michael Barrowman
186
asked Nov 20 at 22:00
Michael Barrowman
186
asked Nov 20 at 22:00
Michael Barrowman
186
asked Nov 20 at 22:00
Michael Barrowman
186
186
add a comment |
add a comment |
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