Analysis 2015-06-05 15:33:08 +0530
= Statsample::Factor::MAP
There are 2 real factors on data
== Principal Component Analysis
Number of factors: 1
Communalities
+----------+---------+------------+--------+
| Variable | Initial | Extraction | % |
+----------+---------+------------+--------+
| v0 | 1.000 | 0.893 | 89.251 |
| v1 | 1.000 | 0.904 | 90.403 |
| v2 | 1.000 | 0.895 | 89.502 |
| v3 | 1.000 | 0.898 | 89.795 |
| v4 | 1.000 | 0.902 | 90.224 |
| v5 | 1.000 | 0.889 | 88.943 |
| v6 | 1.000 | 0.860 | 86.031 |
| v7 | 1.000 | 0.859 | 85.867 |
| v8 | 1.000 | 0.888 | 88.845 |
| v9 | 1.000 | 0.869 | 86.903 |
+----------+---------+------------+--------+
Total Variance Explained
+--------------+---------+---------+---------+
| Component | E.Total | % | Cum. % |
+--------------+---------+---------+---------+
| Component 1 | 8.858 | 88.576% | 88.576 |
| Component 2 | 0.781 | 7.806% | 96.382 |
| Component 3 | 0.081 | 0.812% | 97.194 |
| Component 4 | 0.058 | 0.578% | 97.772 |
| Component 5 | 0.057 | 0.572% | 98.344 |
| Component 6 | 0.055 | 0.545% | 98.889 |
| Component 7 | 0.035 | 0.354% | 99.243 |
| Component 8 | 0.028 | 0.276% | 99.519 |
| Component 9 | 0.026 | 0.263% | 99.782 |
| Component 10 | 0.022 | 0.218% | 100.000 |
+--------------+---------+---------+---------+
Component matrix
+----+------+
| | PC_1 |
+----+------+
| v0 | .945 |
| v1 | .951 |
| v2 | .946 |
| v3 | .948 |
| v4 | .950 |
| v5 | .943 |
| v6 | .928 |
| v7 | .927 |
| v8 | .943 |
| v9 | .932 |
+----+------+
Traditional Kaiser criterion (k>1) returns 1 factors
== Velicer's MAP
Eigenvalues
+----------+
| Value |
+----------+
| 8.857635 |
| 0.780560 |
| 0.081194 |
| 0.057825 |
| 0.057185 |
| 0.054539 |
| 0.035362 |
| 0.027592 |
| 0.026269 |
| 0.021840 |
+----------+
Velicer's Average Squared Correlations
+----------------------+----------------------------+
| number of components | average square correlation |
+----------------------+----------------------------+
| 0 | 0.767626 |
| 1 | 0.431737 |
| 2 | 0.037660 |
| 3 | 0.061151 |
| 4 | 0.099789 |
| 5 | 0.163735 |
| 6 | 0.192856 |
| 7 | 0.308091 |
| 8 | 0.472457 |
| 9 | 1.000000 |
+----------------------+----------------------------+
The smallest average squared correlation is : 0.037660
The number of components is : 2
Velicer's MAP Test returns 2 factors to preserve