Data analytics, statistics, and more
Combining principal component analysis (PCA) and clustering methods are useful for reducing the dimension of a data set into a few continuous variables containing the most important information in the data. This post illustrates how to combine PCA and clustering methods to identify patterns in a data set using the R language for statistical computing and visualization.
This post presents and demonstrates several methods for exploratory spatial data analysis using the R language for statistical computing and visualization. These methods can be used for identifying spatial dependence patterns and spatial heterogeneity, which are critical components of variogram development and the kriging procedure.
This post presents and demonstrates several methods for natural neighbor interpolation using the R language for statistical computing and visualization. The results are compared to those obtain using ordinary kriging.
This post presents methods that can be used to calculate a 95% upper confidence limit on the mean of an unknown population for left-censored data sets (i.e., containing a mixture of detects and non-detects). The preferred approach depends on many factors, including the number of samples and the distributional shape of the data.
Determination of an appropriate sample size when performing a correlation sudy is usually based on achieving sufficient power that the test can reject the null hypothesis that the correlation is zero. Sample sizes found using this method can yield confidence intervals that are so wide that they provide very little useful information about the magnitude of the correlation. An alternative approach is to choose a sample size that achieves a sufficiently narrow confidence interval for measuring the smallest correlation of potential interest.