The goal of PCA is to explain most of the variability in the data with a smaller number of variables than the original data set. The important thing to remember is PCA is influenced by the magnitude of each variable therefore, the results obtained when we perform PCA will also depend on whether the variables have been individually scaled. But since UrbanPop is measured as a percentage of total population it wouldn’t make sense to compare the variability of UrbanPop to Murder, Assault, and Rape. Since Murder, Assault, and Rape are all measured on occurrences per 100,000 people this may be reasonable depending on how you want to interpret the results. An example would be if every variable in the data set had the same units and the analyst wished to capture this difference in variance for his or her results. However, keep in mind that there may be instances where scaling is not desirable. We use the head command to examine the first few rows of the data set to ensure proper upload. In addition to loading the set, we’ll also use a few packages that provide added functionality in graphical displays and data manipulation. The data set also contains the percentage of the population living in urban areas, UrbanPop. This is a set that contains four variables that represent the number of arrests per 100,000 residents for Assault, Murder, and Rape in each of the fifty US states in 1973. This tutorial primarily leverages the USArrests data set that is built into R. Other Uses for Principal Components: Application of PCA to other statistical techniques such as regression, classification, and clustering.Built-in PCA Functions: Using built-in R functions to perform PCA.Selecting the Number of Principal Components: Using Proportion of Variance Explained (PVE) to decide how many principal components to use.What are Principal Components?: Understanding and computing Principal Components for.Preparing Our Data: Cleaning up the data set to make it easy to work with.Replication Requirements: What you’ll need to reproduce the analysis in this tutorial.This tutorial serves as an introduction to Principal Component Analysis (PCA). You can use PCA to reduce the number of variables and avoid multicollinearity, or when you have too many predictors relative to the number of observations.
#Pca column 64bit 4shared series#
PCA is commonly used as one step in a series of analyses. PCA reduces the dimensionality of the data set, allowing most of the variability to be explained using fewer variables. PCA is an unsupervised approach, which means that it is performed on a set of variables, , …, with no associated response. The risk quotient value (RQ) results showed that the control of PFOS in the treatment of pollutants in the inland river basin of Longgang District deserves more attention.Principal Component Analysis (PCA) involves the process by which principal components are computed, and their role in understanding the data. In addition, PFOA and PFOS resulted from similar sources in sediment and surface water samples ( P < 0.05). Meanwhile, PFOS, perfluoroundecanoic acid (PFUdA), PFBS, and perfluorodecanoic acid (PFDA) were the major PFASs in the sediment samples, with maximum concentrations of 9.83 ng/g dw, 11.86 ng/g dw, 5.30 ng/g dw, and 5.23 ng/g dw, respectively. Perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA) and perfluorobutane sulfonate (PFBS) were the dominant pollutants in surface water, with maximum concentrations of 867.68 ng/L, 288.28 ng/L, and 245.09 ng/L, respectively. The ΣPFAS concentrations in surface water (drought and rainy periods) and sediment ranged from 15.17 to 948.50 ng/L, 11.56–561.14 ng/L, and 1.07–28.94 ng/g dw, respectively. In the present study, 18 PFASs were analyzed in surface water and sediment samples of the inland river basin in Longgang District in 2017. Previous studies of perfluorinated alkyl substances (PFASs) in receiving water bodies of typical industrial parks under the low-carbon development mode are scarce.
0 kommentar(er)
