h2(#description). Description In this template Rapporter will present you Principal Component Analysis. h3(#introduction). Introduction "Principal Component Analysis":https://en.wikipedia.org/wiki/Principal_component_analysis is a dimension reduction method. It produces linearly independent principal components using the variances of the observations in a set of variables. h3(#results). Results
PC1 PC2 PC3
*Standard deviation* 6.298 1.35 0.9088
*Proportion of Variance* 0.9354 0.04298 0.01947
*Cumulative Proportion* 0.9354 0.9783 0.9978
From the table above one can see that the first _3_ Principal Components contains the _93.535 %_, _4.298 %_ and _1.947 %_ of the variances, so together the 99.78 % of that. h5(#visual-representation). Visual representation It could be informative to see visually how the observations lies on these components. On that two dimensional plot below, where the axes are the components which contains the two most variances, you can see (the red vectors) the effect of the variables as well. "!plots/PCA.tpl-1.png!":plots/PCA.tpl-1-hires.png h4(#rotation). Rotation As you wanted to check the Rotation matrix let us present that for you:
PC1 PC2 PC3
*carb* -0.1486 *0.9728* -0.08587
*mpg* *0.9557* 0.1614 0.2433
*cyl* -0.2476 0.07389 *0.9502*
*drat* 0.05777 0.1488 -0.1745
The cells written in bold shows which components explain the most variances of the variables, with the help of them we can draw the following conclusion: * PC1 is a principal component of mpg * PC2 is a principal component of carb * PC3 is a principal component of cyl We can say that none of these impacts are negative. h2(#description-1). Description In this template Rapporter will present you Principal Component Analysis. h3(#introduction-1). Introduction "Principal Component Analysis":https://en.wikipedia.org/wiki/Principal_component_analysis is a dimension reduction method. It produces linearly independent principal components using the variances of the observations in a set of variables. h3(#results-1). Results
PC1 PC2 PC3
*Standard deviation* 6.298 1.35 0.9088
*Proportion of Variance* 0.9354 0.04298 0.01947
*Cumulative Proportion* 0.9354 0.9783 0.9978
From the table above one can see that the first _3_ Principal Components contains the _93.535 %_, _4.298 %_ and _1.947 %_ of the variances, so together the 99.78 % of that. h5(#visual-representation-1). Visual representation It could be informative to see visually how the observations lies on these components. On that two dimensional plot below, where the axes are the components which contains the two most variances, you can see (the red vectors) the effect of the variables as well. "!plots/PCA.tpl-1.png!":plots/PCA.tpl-1-hires.png h4(#rotation-1). Rotation As you wanted to check the Rotation matrix let us present that for you:
PC1 PC2 PC3
*carb* -0.1486 *0.9728* -0.08587
*mpg* *0.9557* 0.1614 0.2433
*cyl* -0.2476 0.07389 *0.9502*
*drat* 0.05777 0.1488 -0.1745
The cells written in bold shows which components explain the most variances of the variables, with the help of them we can draw the following conclusion: * PC1 is a principal component of mpg * PC2 is a principal component of carb * PC3 is a principal component of cyl We can say that none of these impacts are negative.
This report was generated with "R":http://www.r-project.org/ (3.0.1) and "rapport":http://rapport-package.info/ (0.51) in _0.891_ sec on x86_64-unknown-linux-gnu platform. !images/logo.png!