zscore
Computes the zscore of a variable's given dimensions at all other dimensions and retains metadata.
Available in version 6.5.0 and later.
Prototype
load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" ; This library is automatically loaded
; from NCL V6.2.0 onward.
; No need for user to explicitly load.
function zscore (
x : numeric,
flag [1] : integer,
dims [*] : integer
)
return_val : float or double
Arguments
xA variable of numeric type and any dimensionality.
flagA flag to indicate the sample or population standard deviation.
- flag=0: use the sample standard deviation: use [N-1] where N is the sample size
- flag=1: use the population standard deviation: use N, the full sample size
The dimension(s) of x on which to calculate the Z-score.. Must be consecutive and monotonically increasing.
Description
Assuming a normal distribution, a Z-score (aka, standard score) is the number of population standard deviations from the population mean a data point is. But more technically, it's a measure of how many population standard deviations below or above the populatio n mean a raw score is. In practice, the sample mean and sample standard deviation are substituted for the population values.
Examples
Example 1: The following example matches the results at: scipy.stats.zscore
x = (/0.7972, 0.0767, 0.4383, 0.7866, 0.8091, \
0.1954, 0.6307, 0.6599, 0.1065, 0.0508 /)
z0 = zscore(x, 0, 0)
z1 = zscore(x, 1, 0)
print(z0)
print(z1)
print(sprintf("%6.4f" ,z0)+" "+sprintf("%6.4f" ,z1))
The (slightly) edited output is:
Variable: z0
Type: float
Total Size: 40 bytes
10 values
Number of Dimensions: 1
Dimensions and sizes: [10]
Coordinates:
Number Of Attributes: 3
long_name : Z-Score
method : Z=(x-xmean)/xstd ; sample std
NCL : zscore
Variable: z1
Type: float
Total Size: 40 bytes
10 values
Number of Dimensions: 1
Dimensions and sizes: [10]
Coordinates:
Number Of Attributes: 4
long_name : Z-Score
method : Z=(x-xmean)/xstd ; population std
NCL : zscore
---
z0 z1
(0) 1.0694 1.1272
(1) -1.1830 -1.2470
(2) -0.0526 -0.0554
(3) 1.0363 1.0923
(4) 1.1066 1.1665
(5) -0.8119 -0.8558
(6) 0.5489 0.5786
(7) 0.6402 0.6748
(8) -1.0898 -1.1488
(9) -1.2640 -1.3323