# [ncl-talk] sub: significant test

Dennis Shea shea at ucar.edu
Sat Mar 7 08:11:32 MST 2020

```According to the documentation?
===
*r* : *Scalar or array of any dimensionality* containing the linear
correlation coefficients (-1 <= *r* <= +1)

*Nr :* *Array of the same dimensionality as r or else a scalar*. Contains
the number of observations used to determine the coefficients. If *Nr* is a
scalar and *r* is an array of any dimension, then the scalar *Nr* value
will be used for all tests. *Nr* must be at least three but should be at
least eight.
*============*
You can enter the following interactively:

nlat = 5
mlon = 7
r = random_uniform(-1,1,(/nlat,mlon/))
printMinMax(r,0)
n = 20
pr = rtest(r, n 0)
print(pr)
;----------------------
N = round( random_uniform(10,30,(/nlat,mlon/)), 3)
PR = rtest(r, N, 0)
print(PR)

On Fri, Mar 6, 2020 at 5:19 PM dale zuri <dalezuri at gmail.com> wrote:

> Hi,
> Yes. It contains a two-dimensional array (26 x 76) correlation
> coefficients.
>
>
> On Fri, Mar 6, 2020 at 2:58 PM Dennis Shea <shea at ucar.edu> wrote:
>
>> I am not sure what you are doing.
>>
>> Does 'dfcor' contain a correlation?
>>    printVarSummary(dfcor)
>>    printMinMax(dfcor,0)
>>
>> Why did you make it a one-dimensional array?
>>
>>
>>
>>
>> On Fri, Mar 6, 2020 at 2:51 PM dale zuri via ncl-talk <ncl-talk at ucar.edu>
>> wrote:
>>
>>> Hi Ncl user,
>>> I have been trying to test the significance of an array using the
>>> following steps. I don't see any stripes on the figure. I am not sure where
>>> I'm going wrong. I don't get any warning message. I would appreciate any
>>>
>>> Thanks
>>>
>>> ;TTest:;;;;;
>>>
>>> x1d=ndtooned(dfcor)
>>>
>>> n    = dimsizes(x1d)                ; n=11
>>>
>>> df   = n-2
>>>
>>> siglvl=0.05
>>>
>>>  ppr     = rtest(x1d, n, 0)
>>>
>>> printVarSummary(ppr)
>>>
>>>  x4d     = onedtond(ppr,(/26,76/))
>>>
>>> printVarSummary(x4d)
>>>
>>> x4d = where(x4d.lt.siglvl, 1, x4d)
>>>
>>> printMinMax(x4d,0)
>>>
>>> ;overlay plot::::::
>>>
>>> opt = True                                         ; set up parameters
>>> for pattern fill
>>>
>>>   opt at gsnShadeFillType = "pattern"                   ; specify pattern
>>> fill
>>>
>>>   opt at gsnShadeLow      = 17                         ; stipple pattern
>>>
>>> ;  opt at gsnShadeDotSizeF = 3                           ; make dots larger
>>>
>>>  plot2  = gsn_csm_contour(wks,x4d,res2)
>>>
>>>
>>>   plot2   = gsn_contour_shade(plot2,0.05,30, opt)        ; stipple all
>>> areas >= 99%  contour
>>>
>>>
>>>   overlay (plot, plot2)
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>>
>>
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```