# Minimum significant correlation for a sample size

`minSigCor.Rd`

`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)).

## 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>.

## See also

`HiClimR`

, `HiClimR2nc`

, `validClimR`

,
`geogMask`

, `coarseR`

, `fastCor`

,
`grid2D`

and `minSigCor`

.