removeoutlier

Understanding spatial association is essential for spatial
statistical inference, including factor exploration and spatial prediction.
Geographically optimal similarity (GOS) model is an effective method
for spatial prediction, as described in Yongze Song (2022)
<doi:10.1007/s11004-022-10036-8>. GOS was developed based on
the geographical similarity principle, as described in Axing Zhu (2018)
<doi:10.1080/19475683.2018.1534890>. GOS has advantages in
more accurate spatial prediction using fewer samples and
critically reduced prediction uncertainty.

Wenbo Lv

geosimilarity

Geographically Optimal Similarity

Yongze Song

removeoutlier function

<dl><dt>x</dt>
<dd>A vector of a variable</dd>
<dt>coef</dt>
<dd>A number of the times of standard deviation. Default is <code>2.5</code>.</dd></dl>

Arguments

removing outliers. — removeoutlier

<dl>

<dt>x</dt>
<dd>A vector of a variable</dd>


<dt>coef</dt>
<dd>A number of the times of standard deviation. Default is <code>2.5</code>.</dd>

</dl>

removeoutlier: removing outliers.

Description

Usage

Value

Arguments

Examples