Re: factor Analysis

2002-01-29 Thread Huxley 

Thank you for explanation. Bu my question was unclear therefore let me ask
again. I invented an exapmle.

I have 10 questions in a questionnaire. These questions are my 10 variables.
A consumers fill this questionnaire for each 15 products e.g cars. Because
10 variables (X1, X2, ...,X10) are correlated with each other I use factor
analysis and (for convinence I ordered it) I get
Factor1: X1,X2,X3,X4,X5,X6,X7
Factor2: X8,X9,X10

I can  e.g put X1 into 2-D space, because I know that
X1= -1*F1+ (-1*F2). It means that X1 has co-ordinates X1=(-1,-1).
It's simple. But I'm not interested in positioning X1. For me it's important
where there are products (cars) in 2-D space. Therefore my question is how
to do it. I heard (but I do not know) that using e.g variable X1,...X10
mean and factor loadings I can do it i.e. for car1: I multiple  factor
loadings and variables mean (suitable) and I get this position
Could you help me verify this?
I would be very appreciate

Regards
Huxley

Uzytkownik "John Uebersax" <[EMAIL PROTECTED]> napisal w wiadomosci
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> A program like SAS or SPSS will calculate factor scores for you.  A
> factor score is an estimated location of an object (not a variable)
> relative to a factor.  If your factors are orthogonal, then you can
> plot each case using that case's score on Factor 1 and the score on
> Factor 2 as the X- and Y- coordinates of in a 2-dimensional space.
>
> I believe the formula for estimating factor scores of a common-factor
> model is not trvial (unless all communalities are 1).  Therefore one
> might as well let the software calculate factor scores.  The topic is
> well explained in the SAS manual (PROC FACTOR)--perhaps also in the
> SPSS manual.
>
> --
--
> John Uebersax, PhD (805) 384-7688
> Thousand Oaks, California  (805) 383-1726 (fax)
> email: [EMAIL PROTECTED]
>
> Agreement Stats:
http://ourworld.compuserve.com/homepages/jsuebersax/agree.htm
> Latent Structure:  http://ourworld.compuserve.com/homepages/jsuebersax
> Existential Psych: http://members.aol.com/spiritualpsych
> Diet & Fitness:http://members.aol.com/WeightControl101
> --
--
>
> "Huxley" <[EMAIL PROTECTED]> wrote in message
news:...
> > Hi,
> > I've got a question. Does anyone know how to set object in 2-factor
> > dimensional space ...
> > I heard that factor score for a product is equal to product of the
suitable
> > factor loadings and variables mean. i.e.
> > f(m,p)=a(1,m)u(1,p) +a(2,m)u(2,p)+ ...+a(j,m)u(j,p)
> > where: f(m,d) - factor score for m-factor,  p-th - consumer product ,
u(*) -
> > mean for variable j and product p.
> > Could you tell me is this true? How to proof this formally




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Re: factor Analysis

2002-01-29 Thread Gottfried Helms 

It's not so simple. You have to do matrix-inversion for
that. 

If your statistical program is able to spit out "factor scores",
you just take these as your coordinates. For each of your objects
you get values in each factor, which you can use as coordinates 
in the factorspace. 

Regards -

Gottfried.


Huxley schrieb:
> 
> Thank you for explanation. Bu my question was unclear therefore let me ask
> again. I invented an exapmle.
> 
> I have 10 questions in a questionnaire. These questions are my 10 variables.
> A consumers fill this questionnaire for each 15 products e.g cars. Because
> 10 variables (X1, X2, ...,X10) are correlated with each other I use factor
> analysis and (for convinence I ordered it) I get
> Factor1: X1,X2,X3,X4,X5,X6,X7
> Factor2: X8,X9,X10
> 
> I can  e.g put X1 into 2-D space, because I know that
> X1= -1*F1+ (-1*F2). It means that X1 has co-ordinates X1=(-1,-1).
> It's simple. But I'm not interested in positioning X1. For me it's important
> where there are products (cars) in 2-D space. Therefore my question is how
> to do it. I heard (but I do not know) that using e.g variable X1,...X10
> mean and factor loadings I can do it i.e. for car1: I multiple  factor
> loadings and variables mean (suitable) and I get this position
> Could you help me verify this?
> I would be very appreciate
> 
> Regards
> Huxley
>


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台湾原装进口-内衣新品上市（预定中……）

2002-01-29 Thread lemon008 
Title: ÐÂÆ·Ô¤¶¨--ÇÀ£¡ÇÀ£¡ÇÀ£¡





  
 
  
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Re: factor Analysis

2002-01-29 Thread Huxley 


Uzytkownik "Gottfried Helms" <[EMAIL PROTECTED]> napisal w wiadomosci
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> It's not so simple. You have to do matrix-inversion for
> that.
>
Not simple? I heard that taking suitable factor loadings and every variable
mean I can obtain this space. e.g. (I do not know is it true)
Let mean for car1 and questions 10 (variables):
mean X1=1
mean X2=2
..
mean X10=10
I have 2 factor score.
factor loadins (aij) I have, therefore for first factor score, co-odrinate
for car1 is
F1(for car1)=1*a(1,1)+2*a(2,1)+3*a(3,1)+...+10*a(10,1)
is it true?

Huxley




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Re: factor Analysis

2002-01-29 Thread Pedro . Valero-Mora 


What you need is a program that makes biplots for principal 
components. ViSta, a freeware program, will do it for you. In facti, 
it includes examples of data about cars and the goal of the analysis 
is to visualize them in the space of the variables.

Pedro 

> It's not so simple. You have to do matrix-inversion for
> that. 
> 
> If your statistical program is able to spit out "factor scores",
> you just take these as your coordinates. For each of your objects
> you get values in each factor, which you can use as coordinates 
> in the factorspace. 
> 
> Regards -
> 
> Gottfried.
> 
> 
> Huxley schrieb:
> > 
> > Thank you for explanation. Bu my question was unclear therefore 
let me ask
> > again. I invented an exapmle.
> > 
> > I have 10 questions in a questionnaire. These questions are my 10 
variables.
> > A consumers fill this questionnaire for each 15 products e.g 
cars. Because
> > 10 variables (X1, X2, ...,X10) are correlated with each other I 
use factor
> > analysis and (for convinence I ordered it) I get
> > Factor1: X1,X2,X3,X4,X5,X6,X7
> > Factor2: X8,X9,X10
> > 
> > I can  e.g put X1 into 2-D space, because I know that
> > X1= -1*F1+ (-1*F2). It means that X1 has co-ordinates X1=(-1,-1).
> > It's simple. But I'm not interested in positioning X1. For me 
it's important
> > where there are products (cars) in 2-D space. Therefore my 
question is how
> > to do it. I heard (but I do not know) that using e.g variable 
X1,...X10
> > mean and factor loadings I can do it i.e. for car1: I multiple  
factor
> > loadings and variables mean (suitable) and I get this position
> > Could you help me verify this?
> > I would be very appreciate
> > 
> > Regards
> > Huxley
> >
> 
> 
> =
> Instructions for joining and leaving this list, remarks about the
> problem of INAPPROPRIATE MESSAGES, and archives are available at
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> 





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Verba Volant 29-01-02

2002-01-29 Thread Verba Volant 

Verba Volant 29-01-02,
Every day a new quotation translated into many languages. 

_ 
Quotation of the day:
Author - Jonathan
Swift
English - we have just enough
religion to make us hate, but not enough to make us love one 
another
Italian -
abbiamo abbastanza religione per
odiarci, ma non abbastanza per volerci bene
Spanish -
tenemos bastante religión para
odiarnos, pero no suficiente para amarnos
French -
nous avons tout juste assez de
religion pour nous haïr, mais pas assez pour nous aimer les uns les
autres
German -
wir haben gerade genug Religion um zu
hassen, aber nicht genug um einander zu lieben
Basque -
badugu elkar gorrotatzeko beste
erlijio, baina ez elkar maitatzeko behar adina
Bolognese -
avän asè religiån par vlairs dal mèl,
mo brîSa asè par vlairs bän
Bresciano -
èn góm abastànsa religiù per ùlìs del
mal, ma mja a sé per ùlìs del bè
Calabrese -
avimu abbastanza religgiuni pi 'ni
odiari,ma nun abbastanza pi ni vuliri beni
Catalan -
tenim bastant religió per a
odiar-nos, però no pas bastant per a estimar-nos
Croatian -
dovoljno smo religiozni da bi se
mrzili a premalo da bi voljeli jedni druge
Czech -
už máme dost víry, která nás ucí
nenávidet, ale takové, která by nás ucila lásce k bližnímu, máme
málo
Dutch -
we hebben net genoeg religie om
elkaar te haten, maar niet genoeg om van elkaar te houden
Emiliano Romagnolo -
avem abastenza religioun par svarders
l'un l'eltr a la manarra; non par vulers ben
Esperanto -
ni havas sufican religion por nin
malami, sed ne sufican por nin ami
Ferrarese -
ag avén bastansa religion par
udiárass, ma brisa bastansa par vuleraz bén
Finnish -
meillä on tarpeeksi uskontoa toisten
vihaamiseksi, mutta ei toisten rakastamiseksi
Flemish -
we hebben net genoeg religie om
elkaar te haten, maar niet genoeg om van elkaar te houden
Furlan -
'o vin vonde religjon par odeâsi, ma
no vonde par volêisi ben
Galician -
temos relixión de máis para odiarnos,
mais non suficiente para amarnos
Hungarian -
ahhoz elég a vallásunk, hogy
gyulöljük egymást, de ahhoz már nem, hogy szeretni tudjuk
embertársunkat
Latin -
nobis satis religionis est ut nos
oderimus, at non satis ut nos amemus
Latvian; Lettish -
mums ir tieši tik daudz dievticibas,
lai ienistu, bet nepietiekoši, lai miletu cits citu
Leonese -
tenemos relixón asgaya pa odianos,
peru non abonda pa querenos enforma
Mantuan -
gh ema bastansa religion par odiaras,
ma mia asè par voleras ben
Neapolitan -
'a religgione ce abbasta pe nce odià
ll'uno cu ll'autro, ma nun ce abbasta pe nce vulé bbene
Occitan -
avem pro de religion per nos
detestar, mas pas pro per nos aimar
Parmigiano -
a gh'emma bastansa religión pär
odiärnos, pero no bastansa pära vrernos ben
Piemontese -
i l'oma a basta ëd religion për
avèjse an ghignon, ma nen a basta për vorèjse bin
Polish -
mamy wystarczajaco religii dla
nienawidzenia sie, ale nie wystarczajaco dla kochania sie
Portuguese -
temos bastante religião para nos
odiarmos, mas não a suficiente para nos amarmos
Reggiano -
gh'om asèe religiòun per odieres, mo
mia asèe per vrèires bein
Roman -
c'avemo abbastanza religgione pe'
odiacce, ma nun abbasta pe' volesse bbene
Romanian -
avem suficienta religie pentru a ne
urî unii pe altii, dar nu destula pentru a ne iubi
Sardinian (Limba Sarda Unificada) -
tenimus bastante relizione pro nos
odiare, ma no nde tenimus bastante pro nos cherrer bene
Sicilian -
avemu religgiuni 'bbastanti ppi'
udiarini l'unu 'ccu' ll'autru, ma no ppi' vulirini bbeni
Slovak -
máme dostatok viery na to, aby sme sa
nenávideli, ale málo na to, aby sme sa mali radi 
_ 
All languages, please click on this link
http://www.logos.net/owa-l/press.frasiproc.carica?code=506
_ 
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Re: factor Analysis

2002-01-29 Thread Gottfried Helms 

Huxley schrieb:
> 
> Uzytkownik "Gottfried Helms" <[EMAIL PROTECTED]> napisal w wiadomosci
> [EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> > It's not so simple. You have to do matrix-inversion for
> > that.
> >
> Not simple? I heard that taking suitable factor loadings and every variable
> mean I can obtain this space. e.g. (I do not know is it true)
> Let mean for car1 and questions 10 (variables):
> mean X1=1
> mean X2=2
> ..
> mean X10=10
> I have 2 factor score.
> factor loadins (aij) I have, therefore for first factor score, co-odrinate
> for car1 is
> F1(for car1)=1*a(1,1)+2*a(2,1)+3*a(3,1)+...+10*a(10,1)
> is it true?
> 
> Huxley

Loadings of factor f1,f2 for items x1,x2,x3,x4... 
 f1f2
 x1  0.4   0.6
 x2  0.3   0.9
 x3  0.2  -0.1
 x4 -0.8  -0.4
 ...
Call this loadingsmatrix A, your correlation-matrix R 
That means, that A*A' = R
Call your empical datamatrix   (x1,x2,x3,...) X 
Call the unknow factorscores  SC
Then it is assumed that

A*SC = X 

Then you must find inv(A) to be able to find SC:

inv(A)*A*SC = inv(A) *X
SC = inv(A)*X

If the shape of A is not square and/or the rank is lower
then its dimension, then you have to find a workaround to
compute the general_inverse of A. 

I don't find it so simple ;-) 

Gottfried.


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imputation in the general location model

2002-01-29 Thread Vumani Dlamini 

I am trying to use the S-Plus library, MIX (by Schafer) to create
multiple data sets (the original data set has missing values). I have
a problem in understanding the use of the "margins" vector which has
to be submitted  to the program when using the restricted general
location model.

I "seem" to understand its use when operating under logistic or
proportional odds type models (as they have a dependent variable).
Given that I supply the data matrix and and design matrix, the same
way I do in GLM and have no "margins" vector. Is there any way one can
explain the use of this particular vector approaching the problem as
if I have no missing values.

Thanks.

Vumani Dlamini
Swaziland


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Re: factor Analysis

2002-01-29 Thread Rich Ulrich 

On Tue, 29 Jan 2002 10:52:30 +0100, "Huxley" <[EMAIL PROTECTED]> wrote:

> 
> Uzytkownik "Gottfried Helms" <[EMAIL PROTECTED]> napisal w wiadomosci
> [EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> > It's not so simple. You have to do matrix-inversion for
> > that.
> >
> Not simple? I heard that taking suitable factor loadings and every variable
> mean I can obtain this space. e.g. (I do not know is it true)
> Let mean for car1 and questions 10 (variables):
> mean X1=1
> mean X2=2
> ..
> mean X10=10
> I have 2 factor score.
> factor loadins (aij) I have, therefore for first factor score, co-odrinate
> for car1 is
> F1(for car1)=1*a(1,1)+2*a(2,1)+3*a(3,1)+...+10*a(10,1)
> is it true?

No, that is not true.  
Please believe them.

Factor loadings are *correlations*  and serve as descriptors.  
They were neither scaled nor computed as regression coefficients -
which is what you are trying to use them as.


Now, in clinical research, we don't usually bother to create the
actual, real, true factor, for our practical purposes.   For practical
purposes, it is important to have some face-validity for what 
the factor means.  And it is handy for replication, as well as 
for understanding, if we construct a factor as the summed score
(or average score) of a set of the items.

So I look at the high loadings.  For a good set of items, it
can be realistic and appropriate to 'assign'  each item to the
factor where its loading is greatest, thus using each item just
once in the overall set of several derived factors.  (For a set 
of items where many items were new and untested, it can 
be appropriate to discard some of items -- where the loadings
were split, or were always small.)  Each factor is scored as 
the average score for of a subset of items.

-- 
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html


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views on the mcas?

2002-01-29 Thread ydf 

http://askearth.com/go/view_request?request=5058


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Re: cutting tails of samples

2002-01-29 Thread Rich Ulrich 

On 17 Jan 2002 00:05:02 -0800, [EMAIL PROTECTED] (Håkon) wrote:

> I have noticed a practice among some people dealing with enterprise
> data to cut the left and right tails off their samples (including
> census data) in both dependent and independent variables. The reason
> is that outliers tend to be extreme. The effects can be stunning. How
> is this practice to be understood statistically - as some form of
> truncation? References that deal formally with such a practice?

This is called "trimming" - 5% trimming, 25% trimming.
The median is what is left when you have done "50% trimming."

Trimming by 5% or 10% reportedly works well for your 
measures of 'central tendency', so long as you *know*  
that the extremes are not important.

I don't know what it is that you refer to as 'enterprise data.'

-- 
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html


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Re: Definition of "Relationship Between Variables" (was Re: Eight Features of an Ideal Intro Stat Course)

2002-01-29 Thread G Robin Edwards 

In article ,
   Donald Macnaughton <[EMAIL PROTECTED]> wrote:

At great length, and with many quotes, on a very interesting topic.  I
fear I may have missed the original postings on this thread, though.

There is however one area that seems not to have been addressed.  This is
the field of statistical design of experiments.  The word "design"
appears just once in the article, in connections with the t test.

I am really disappointed that there was not some emphasis on the value of
correctly designed experiments at all levels in the sciences, both "hard"
and "soft".

As a non-statistician, non-mathematician and non-academic (merely a
practical chemist who spent his entire working life in industry) I
introduced myself to statistics via the experiment design route using
Brownlee's "Industrial Experimentation" in 1956. 

The elegance of simple ANOVA became apparent even to me, but the
introduction to the ideas of "design" were even more exciting.  Many
practical scientists at "bench" level can I feel readily appreciate many
of the concepts of "design" and thus the notion of constructing a model
which their experimental work will address and hence "prove" or "fail to
support" the underlying hypotheses.

This I feel is the way to get otherwise sceptical scientists and
engineers into the way of considering their practical real-life problems
as ones that require an "holistic" approach.  Few industrial
investigations are single variable problems.

My belief and experience is that too much emphasis on the formal
mathematical exposition of statistical ideas - however relevant they are
to statistics majors - serve only to distance the experimental scientist
from the huge advantages to be gained from making use of designed
experiments in a complex world.

Quite simple examples can serve to generate acceptance and even
enthusiasm for what we might regard as a rational approach but which
might otherwise be discouraging for the newcomer to statistical design of
experiments.  I've proved this to myself time and again in an industrial
context.

-- 
  Robin Edwards  ZFC  Ta  Serious Statistical Software
REAL Statistics with Graphics for RISC OS machines
   Please email [EMAIL PROTECTED] for details of our loan software. 



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