Canonical Correlation Analysis Software
Driver Hp Laserjet P2014 For Windows 7 64 Bit. Canonical correlation analysis is the redu ction in variable count, so this value is usually set to two or three. Hp Laserjet 3055 Scanner Software For Windows Vista here. You would approach the selection of this number in much the same way as selecting the number of factors in factor.
Canonical correlation analysis is used to identify and measure the associations among two sets of variables. Canonical correlation is appropriate in the same situations where multiple regression would be, but where are there are multiple intercorrelated outcome variables.
Canonical correlation analysis determines a set of canonical variates, orthogonal linear combinations of the variables within each set that best explain the variability both within and between sets. This page uses the following packages.
Make sure that you can load them before trying to run the examples on this page. If you do not have a package installed, run: install.packages('packagename'), or if you see the version is out of date, run: update.packages(). Require(ggplot2) require(GGally) require(CCA) Version info: Code for this page was tested in R Under development (unstable) (2012-11-16 r61126) On: 2012-12-15 With: CCA 1.2; fields 6. Hp Atheros Wireless Lan Driver For Microsoft Windows 7 there. 7; spam 0.29-2; fda 2.3.2; RCurl 1.95-3; bitops 1.0-5; Matrix 1.0-10; lattice 0.20-10; zoo 1.7-9; GGally 0.4.2; reshape 0.8.4; plyr 1.8; ggplot2 0.9.3; knitr 0.9 Please Note: The purpose of this page is to show how to use various data analysis commands. It does not cover all aspects of the research process which researchers are expected to do. In particular, it does not cover data cleaning and checking, verification of assumptions, model diagnostics and potential follow-up analyses.
Cara Menginstal Printer Canon M287 Tanpa Kaset. Examples of canonical correlation analysis Example 1. A researcher has collected data on three psychological variables, four academic variables (standardized test scores) and gender for 600 college freshman. She is interested in how the set of psychological variables relates to the academic variables and gender. In particular, the researcher is interested in how many dimensions (canonical variables) are necessary to understand the association between the two sets of variables. A researcher is interested in exploring associations among factors from two multidimensional personality tests, the MMPI and the NEO.
She is interested in what dimensions are common between the tests and how much variance is shared between them. She is specifically interested in finding whether the neuroticism dimension from the NEO can account for a substantial amount of shared variance between the two tests. Description of the data For our analysis example, we are going to expand example 1 about investigating the associations between psychological measures and academic achievement measures. We have a data file, mmreg.dta, with 600 observations on eight variables. The psychological variables are locus_of_control, self_concept and motivation. The academic variables are standardized tests in reading ( read), writing ( write), math ( math) and science ( science).
Additionally, the variable female is a zero-one indicator variable with the one indicating a female student. ## WilksL F df1 df2 p ## [1,] 0.7544 11.7 7.498e-28 ## [2,] 0.9614 2.944 8 1186 2.905e-03 ## [3,] 0.9892 2.165 3 594 9.109e-02 As shown in the table above, the first test of the canonical dimensions tests whether all three dimensions are significant (they are, F = 11.72), the next test tests whether dimensions 2 and 3 combined are significant (they are, F = 2.94). Finally, the last test tests whether dimension 3, by itself, is significant (it is not). Therefore dimensions 1 and 2 must each be significant while dimension three is not.