On Sat, 2011-02-05 at 23:39 -0600, Paul Johnson wrote:
> On Sat, Feb 5, 2011 at 9:19 AM, David Winsemius
> wrote:
> > cbind(scalermca[,1] * 0.827094, scalermca[,2] * -0.7644828)
> [,1][,2]
> 1 1.06070017 -0.8154
> 2 0.77057891 0.63456780
> 3 1.07031764 -1.30675217
>
>> When I came to David's comment, I understood the theory, but not the
>> numbers in his answer. I wanted to see the MASS mca answers "match
>> up" with SAS, and the example did not (yet).
I am inclined to write, "O yea of little faith." David showed perfectly well
that when the results of th
On Sat, Feb 5, 2011 at 9:19 AM, David Winsemius wrote:
>
> On Feb 4, 2011, at 7:06 PM, Gong-Yi Liao wrote:
>
>> Dear list:
>>
>> I have tried MASS's mca function and SAS's PROC corresp on the
>> farms data (included in MASS, also used as mca's example), the
>> results are different:
>>
>> R: m
On Feb 4, 2011, at 7:06 PM, Gong-Yi Liao wrote:
Dear list:
I have tried MASS's mca function and SAS's PROC corresp on the
farms data (included in MASS, also used as mca's example), the
results are different:
R: mca(farms)$rs:
1 2
1 0.059296637 0.0455871427
Dear list:
I have tried MASS's mca function and SAS's PROC corresp on the
farms data (included in MASS, also used as mca's example), the
results are different:
R: mca(farms)$rs:
1 2
1 0.059296637 0.0455871427
2 0.043077902 -0.0354728795
3 0.059834286
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