Minimum significant correlation for a sample size

minSigCor is a helper function that estimates the minimum significant correlation for a sample size n at a confidence level defined by the argument alpha.

Usage

minSigCor(n = 41, alpha = 0.05, r = seq(0, 1, by = 1e-6))

Arguments

n: sample size or the length of a timeseries vector.
alpha: confidence level: the default is alpha = 0.05 for 95% confidence level.
r: a vector of values from 0 to 1 to search for the minimum significant correlation for the user-specified sample size n at confidence level alpha. This should be a subset of the valid positive correlation range 0-1. The default is to search for the minimum significant correlation in the complete range 0-1 with a very fine step of 1e-6. For faster computations, the user may set a shorter range with larger step (e.g., seq(0.1, 0.5, by=1e-3)).

Value

A positive value between 0 and 1 for the estimated the minimum significant correlation.

Details

minSigCor function estimates the minimum significant correlation for a sample size (number of observations or temporal points in a timeseries) at a certain confidence level selected by the argument alpha and an optional search range r. It is called by validClimR function objective tree cut based on the specified confidence level.

References

Hamada S. Badr, Zaitchik, B. F. and Dezfuli, A. K. (2015): A Tool for Hierarchical Climate Regionalization, Earth Science Informatics, 8(4), 949-958, doi:10.1007/s12145-015-0221-7 .

Hamada S. Badr, Zaitchik, B. F. and Dezfuli, A. K. (2014): Hierarchical Climate Regionalization, Comprehensive R Archive Network (CRAN), https://cran.r-project.org/package=HiClimR.

Author

Hamada S. Badr <badr@jhu.edu>, Benjamin F. Zaitchik <zaitchik@jhu.edu>, and Amin K. Dezfuli <amin.dezfuli@nasa.gov>.

Examples

require(HiClimR)

## Find minimum significant correlation at 95% confidence level
rMin <- minSigCor(n = 41, alpha = 0.05, r = seq(0, 1, by = 1e-06))