Two references that may be some help, William Cleveland, The Elements of Graphing Data, Hobart Press, NJ, pgs 143 to 149 Lawrence C Hamilton, Regression with Graphics, Duxbury Press, CA, pgs 11 through 17
Peter
Allin Cottrell wrote:
> On Sat, 2 Jan 2010, Sven Schreiber wrote:
>
>
>> Allin Cottrell schrieb:
>>
>>> I'm starting to implement the normal Q-Q plot as a built-in
>>> command. Question: should we standardize the series to be plotted
>>> against the normal?
>>>
>>> I notice that R does not standardize, but neither does it draw a
>>> 45-degree line. If you don't standardize but do draw the
>>> 45-degree line, then you get quite odd-looking results for a
>>> series that has a substantially non-zero mean and/or a variance
>>> that differs substantially from 1.
>>>
>> I don't know what R does, but in general I'm afraid I don't quite
>> understand the question; when you check whether a variable is normally
>> distributed, it seems clear to me that of course you compare its
>> empirical distribution to the closest member of the normal family/class.
>> So you estimate mu and sigma. If that's what is meant, then sure go
>> ahead and standardize...
>>
>
> There are two options here, which differ only in respect of what
> values are shown on the axes: (1) convert the data to z-scores, or
> (2) for the x-axis use quantiles of the normal distribution with
> mean \hat{mu} and standard deviation \hat{sigma}. I'm not sure if
> one of these is more "standard" than the other.
>
> R does neither of these things: the command "qqnorm" plots the
> "raw" quantiles of the data against the quantiles of N(0,1).
>
> Allin
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Two references that may be some help, William Cleveland, The Elements of Graphing Data, Hobart Press, NJ, pgs 143 to 149 Lawrence C Hamilton, Regression with Graphics, Duxbury Press, CA, pgs 11 through 17 Peter Allin Cottrell wrote: On Sat, 2 Jan 2010, Sven Schreiber wrote: |
