minSigCor {HiClimR} | R Documentation |
Minimum significant correlation for a sample size
Description
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 |
r |
a vector of values from |
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.
Value
A positive value between 0
and 1
for the estimated the minimum
significant correlation.
Author(s)
Hamada S. Badr <badr@jhu.edu>, Benjamin F. Zaitchik <zaitchik@jhu.edu>, and Amin K. Dezfuli <amin.dezfuli@nasa.gov>.
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.
See Also
HiClimR
, HiClimR2nc
, validClimR
,
geogMask
, coarseR
, fastCor
,
grid2D
and minSigCor
.
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))