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CEREO atmosphere

PCA and Atmospheric Research

Today Tsengel Nergui showed us how she used Principal Component Analysis in her atmospheric research. The script and data provided shows an excellent example of PCA application. Tsengel discusses not only int interpretation of the results, but also some of the standardization that one can do prior to PCA.

In the discussion portion of the session we talked about how a conceptual understanding of PCA can be broken into two philosophies: calculating the eigenvalues or focusing on the dissimilarity matrix. Both lead to the same place but some researchers may find one or the other strategy more compelling. PCA, and indeed other multivariate apraoches in R, are very clearly explained in Manly’s Multivariate Statistical Methods: A Primer. The 4th edition has a website that includes example data and script for R. Another good resource is the R package vegan.

In addition to discussin PCA, we also discussed loading jpegs in R. This is very simple to do with the jpeg package.

This talk will require the following packages:

library(stats)
library(plyr)  # plyr must be called before dplyr
library(dplyr) 
library(ggplot2) 
jpeg
rasterImage

Necessary script and data below:

Rsession_MixedBag2_tsengel

BEL116_hourly_O3_met_2012Summer

 

 

 

 

 

Atmospheric Research Profile

Today Tsengel demonstrated how she made interitive processes in her research to examine the distriubuton of various datasets. She used for loops as well as some specific packages to do this. In addition, we worked as a group to examine ways to recreate a figure Tsengel created in excel in ggplot.

For Tsengel’s presentation today we used the following packages:

library(stats)
library(fitdistrplus)
library(moments)

Script: atmospheric-research-profile

Please also download the following file to be used as data: lugroups_ndepspc_1997

Additional files, including scrip: re%3a_next_week_-_base_r_stats