Date: Thu, 23 Jun 2011 23:14:57 -0700
Reply-To: David Marso <david.marso@gmail.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: David Marso <david.marso@gmail.com>
Subject: Re: PCA: R-Matrix Determinant =0 and "not positive Definite"
In-Reply-To: <1308890922708-4519934.post@n5.nabble.com>
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Those are a LOT of questions and considering...
"This is the summery of my objectives and methodology. Since i don't have
stat background, in fact i am using SPSS for the very first time, so i am
having issues which i need to discuss with you guys. "
Not to seem rude, but...
Sounds like you are in way over your head!!
Your associated entity should perhaps consider hiring a consultant or
trainer?
"How can i calculate the scores of 6 PCs for each day(*)"
See /SAVE subcommand on FACTOR.
OTOH, the analysis over ALL of these data points is surely to be a complete
mess. Maybe group the data by WEEK? then get PCA and scores within each
week? SEE SPLIT FILE?
Re Missing values?
MEAN SUB from entire data file? Are you kidding? Maybe from same day from
closest other station?
That's about all I have to contribute at the moment.
You really have a 1343x15x41 matrix (or are these aggregated across station?
-in which case ignore the MEAN SUB idea-).
RE AVG LINKAGE? Please consult the DOCS on cluster analysis.
HTH,
Gun For Hire, David
---------
mzalikhan wrote:
>
> The data is missing because its observational data and either "some
> variable in some cases" or "some cases as whole" are not recorded.
> I am using SPSS 16 and it gives three options in Factor analysis regarding
> Missing Values.
> 1- Exclude cases list wise (which reduces my cases from 1343-810)
> 2- Exclude cases pairs wise. (gives not positive matrix)
> 3- Replace by means. (simply replaces the missing variables by mean values
> and thus valid N= total N)
>
> I am sorry for not been elaborate enough regarding data.
>
> Its daily meteorological data, recorded at around 15 stations across South
> East Asia with Bangkok as center. So meteorological parameters like daily
> temperature, humidity, cloud cover etc (a total of 41 parameters for all
> 15 stations) are the variables and thus set of all the variables for each
> day is a case. The data i am dealing with is for summer season
> (March-June) (2000-2010). So its initially a 1343 (days) x 41 (parameters)
> matrix. The Objective of my study is to find out the meteorological
> patterns prevailing the region. The methodology i am going to follow is
> 1- to find out the minimum number of PC's representing maximum variance in
> the data.(of course i can exclude some of the variables out of 41 to
> achieve this)
> 2- Once I get (for example six) PCs with respective loading of different
> variables, (by the way on the basis of my literature review, i am
> expecting not more than 6 PCs) *I have to calculate the scores of 6 PCs
> for each day (It should be a 1343 x 6 Matrix)
> 3- Days in this matrix are to be grouped by following a 2-stage clustering
> technique.
> i- Application of an average linkage clustering method** on this 1343
> x 6 matrix to determine the initial number of clusters and mean conditions
> with in each cluster mean component score.
> ii- Modify these initial clusters using K-means clustering technique
> with the initial number of clusters and their mean component scores as an
> initial seed value. This procedure is to classify the 1343 days in to a
> certain number of meteorologically homogeneous clusters.
>
> This is the summery of my objectives and methodology. Since i don't have
> stat background, in fact i am using SPSS for the very first time, so i am
> having issues which i need to discuss with you guys.
>
> i am rephrasing the questions that right now i have as;
>
> 1- Once i get 6 PCs and the variable loading (say 15 variables having
> loading greater than 0.4 are contributing in the 6 PCs Eigen value greater
> than 1), I get a 15 x 6 matrix. How can i calculate the scores of 6 PCs
> for each day(*)
>
> 2- How to Apply an average linkage clustering method on this 1343 x 6
> matrix**?
>
> Peace.
>
--
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