As Peter points out, it is better to ask the maintainer of the program for detailed questions.

As Peter correctly surmised, print.psych (which is used to print the output from the fa function), knows that you have an oblique solution and is reporting the amount of variance associated with the oblique factors (taking into account that they are correlated). The default print method assumes orthogonal factors.

If you compare the total amount of variance accounted for (cumulative Var) for all of the factors (.59) , this will match that found using orthogonal rotations, while the default print method of the loadings does not.

> On Dec 6, 2014, at 10:48 AM, peter dalgaard <

[hidden email]> wrote:

>

> Firstly, there is no fa() function in base R. There is one in package psych(), which has a maintainer, etc.

>

> I guess that it is because fa() does a non-orthogonal factor rotation and its print method knows about it, whereas the default print method for loadings assumes that rotations are orthogonal.

>

> - Peter D.

>

>> On 05 Dec 2014, at 13:28 , Rena Büsch <

[hidden email]> wrote:

>>

>> Hello,

>> I am trying a factor analysis via R.

>> When running the pricipal axis analysis I do get different tables depending

>> on the print command.

>> This is my factor analysis:

>> fa.pa_cor_3_2<- fa(ItemsCor_4, nfactors=3, fm="pa",rotate="oblimin")

>>

>> To get the h2 I did the following print command:

>> print (fa.pa_cor_3_2, digits=2, cut=.3, sort=T)

>> To just get the loadings I did the following print command:

>> print (fa.pa_cor_3_2$loadings, digits=2, cutoff=.3, sort=T)

>>

>> The result of the first print is the following Eigenvalue-cumulative

>> variance table:

>> PA1 PA2 PA3

>> SS loadings 20.59 18.16 5.03

>> Proportion Var 0.28 0.25 0.07

>> Cumulative Var 0.28 0.52 0.59

>>

>> With the second print command I get a different table:

>> PA1 PA2 PA3

>> SS loadings 17.63 15.12 3.14

>> Proportion Var 0.24 0.20 0.04

>> Cumulative Var 0.24 0.44 0.49

>>

>> The loadings are the same for both commands. There is just this slight

>> difference in the cumulative Var.

>>

>> Does anyone have an idea of a cause for the difference? What can I report?

>> Did I post enough information to fully understand my problem?

>> Thanks in Advance

>> Rena

>>

>> ______________________________________________

>>

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>>

https://stat.ethz.ch/mailman/listinfo/r-help>> PLEASE do read the posting guide

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>

> --

> Peter Dalgaard, Professor,

> Center for Statistics, Copenhagen Business School

> Solbjerg Plads 3, 2000 Frederiksberg, Denmark

> Phone: (+45)38153501

> Email:

[hidden email] Priv:

[hidden email]
>

> ______________________________________________

>

[hidden email] mailing list -- To UNSUBSCRIBE and more, see

>

https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide

http://www.R-project.org/posting-guide.html> and provide commented, minimal, self-contained, reproducible code.