This is for a hobby project so spending money is out. Thanks for the tip, tho.

Re: Steve McI’s reply

I’m a rank beginner in statistics (actually that overstates my capabilities) so I was hoping for something really easy to use. After all, the fate of the globe does not hang on this investigation. However, since you’ve given me a recipe, I’ll give R a try. Thanks for taking the time to respond. ]]>

Is it PC1 of the partially centered tree ring data?

No, PC1, along with other proxy data is fed to classical calibration algorithm (with some modifications), temperature PCs as targets. Proxy PC1 gets the second largest weight in AD1400 step, largest is assigned to Gaspe series. Without those two, you’ll get warm 15th century (#45).

Or is it a linear combination of PC’s from several data sets?

Linear combination of proxy PCs + raw proxy records.

Does it involve any time domain splicing of “reconstructions” or PC’s?

11 steps.

]]>“Also, as I’ve said on many occasions, the studies rely heavily on “data snooping

Isn’t this an accusation of fraud?

**Steve: ** “Data snooping” is a statistical concept used in econometrics and contains no such implication. IT occurs in other fields. Proponents typically believe (incorrectly) that their decisions are good ones. See posts on Greene and references there. If there has been data snooping, then normal statistical tests don’t apply.

The authors of these studies are hardly “independent” as this term is understood in public businesses. Bradley and/or Jones have coauthored with most of them. The proxies aren’t independent (See Wegman on this as well) nor are the authors.

]]>“There are 14 subsequent papers that take different approaches to the same question and data, using different statistical techniques and different selection criteria for data inclusion and so on – and arrive at very close to the same answers.”

I’d like someone to respond to this. Is it true?”

If you use proxies that are not really temperature proxies (i.e., your assumptions are wrong) you will get white noise out of your model, and the mean of the white noise will be some sort of fairly flat output over the simulated period (the shaft of the hockey stick). Then by picking out a few proxies that correlate with your temperature “signal” you get the blade, and you have your hockey stick. That is, a failed temperature reconstruction will look just like the spagetti graph in IPCC up to 1900. They never have shown that their approach works with out of sample data.

]]>I thought I knew the answer to this question, but now I’m not so sure.

What exactly is the **MBH98 Reconstruction**?

Is it PC1 of the partially centered tree ring data?

Or is it a linear combination of PC’s from several data sets?

Does it involve any time domain splicing of “reconstructions” or PC’s?

Or does the “reconstruction” not involve PC’s at all?

Thanks in advance.

]]>Are there any simple programs that will do PCA? I would guess that R would be able to do that but I’m hoping for something that is more beginner friendly. This is for a non-climate related problem.

EViews 6.0 has a newly expanded PCA capacity that is quite easy to use. It’s a little expensive, though, unless you’re at a university with a site licence.

]]>Steve:

pca0 =prcomp(X) for a covariance; the left is in pca0$x; the eigenvalues in pca0$sd and the right in pc0$rotation

pca1=prcomp(X,scale=TRUE) for correlation PCs

Or if you want to do a SVD, it’s just as easy”

svd0= svd(scale(X,scale=FALSE)) for covariance

svd1 = svd(scale(X)) for correlation

What could be easier?

IMHO no one should consider doing statistical work other than via R these days.

]]>and different selection criteria for data inclusion

LOL, I doubt that they are THAT much different. e.g., find me one of those papers that does not “select” BCPs.

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