27 May
2016
27 May
'16
11 a.m.
Hi there,
I think it might be useful to use the cor(new_data, use = "pairwise")
function to get correlations based on pairwise complete data. Then you
might find more highly correlated variables that should be removed before
imputation.
Cheers,
Matt
~~~~~~~~~~~
Matthew Blackwell
Assistant Professor of Government
Harvard University
url: http://www.mattblackwell.org
On Sat, May 7, 2016 at 7:46 PM, Gülce Kale <gulce.kale(a)gmail.com> wrote:
Hi everyone,
I have a dataset with 20 features and 102 observation. When I use Amelia I
got this error error: inv_sympd(): matrix appears to be singular. The
resulting variance matrix was not invertible. Please check your data
for highly collinear variables. Then I used spearman correlation to get rid
of highly collinear features:
cor_spear <- rcorr(as.matrix(new_data), type="spearman")
cor<-cor_spear$r
I found the elements that have value >= 0.9 in my cor matrix. I removed
corresponding features from dataset. There were some elements with value NA
in cor matrix, I don't know what they mean but I removed them as well.
Then, I only got 12 features which are correlated less than 0.9. However I
still get this error:
The resulting variance matrix was not invertible. Please check your data
for highly collinear variables.
*How can I correct this error? *
*Thank you!*
--
Amelia mailing list served by HUIT
[Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia
More info about Amelia: http://gking.harvard.edu/amelia
Amelia mailing list
Amelia(a)lists.gking.harvard.edu
To unsubscribe from this list or get other information:
https://lists.gking.harvard.edu/mailman/listinfo/amelia