Re: factor Analysis
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:a2u3sa$q3e$[EMAIL PROTECTED]... 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 = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: factor Analysis
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 http://jse.stat.ncsu.edu/ =
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Title: ÐÂÆ·Ô¤¶¨--ÇÀ£¡ÇÀ£¡ÇÀ£¡ ÄÐÊ¿ÄÚÒµêÐÂÆ·Ô¤¶¨ÖС¡ http://www.51neiku.com SHOW YOUR TALENTS SHOW YOURSELF ¿ãÐÍÑ¡Ôñ£ºÐÂÆ·Ô¤¶¨ º«¹ú¹Ý ̨Íå¹Ý(Looksee) Ç°ÎÀ¿ã Á¬Ò¿ã ÍøÑÛ¿ã ¶¡×Ö¿ã ¶Íοã ×Óµ¯¿ã ÒÔÏÂÉÌÆ·ÊýÁ¿ÓÐÏÞ£¬ÏÖ½ÓÊÜÔ¤¶©£¬±¾Ôµ×Ç°µ½»õ. »õ¡¡ºÅ: T501 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ: ̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª¡¡ »õ¡¡ºÅ: T502 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ: ̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª¡¡ »õ¡¡ºÅ: T503 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ: ̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª¡¡ »õ¡¡ºÅ: T504 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ:̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª¡¡ »õ¡¡ºÅ: T505 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ: ̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª¡¡ »õ¡¡ºÅ: T506 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ: ̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª »õ¡¡ºÅ: T507 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ: ̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª »õ¡¡ºÅ: T508 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ: ̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª »õ¡¡ºÅ: T509 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ: ̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª »õ¡¡ºÅ: T510 Ãæ¡¡ÁÏ: ¸ßµ¯ÏËά¡¡³ßÂë: ¾ùÂë ÑÕ¡¡É«: »¨É«¡¡ ²ú¡¡µØ: ̨ÍåÔ×°½ø¿Ú Êг¡¼Û: £¤158Ôª »áÔ±¼Û: £¤98Ôª ²é¿´¸ü¶à¡¡ SHOW YOUR TALENTS SHOW YOURSELF 1999-2001°æȨËùÓÐ ÅóÓÑÍø¹¤×÷ÊÒ 51ÄÚ¿ãÍø Õ¾³¤ÐÅÏ䣺 [EMAIL PROTECTED] µê³¤ÐÅÏ䣺[EMAIL PROTECTED]
Re: factor Analysis
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 = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: factor Analysis
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 http://jse.stat.ncsu.edu/ = = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Verba Volant 29-01-02
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 - u máme dost víry, která nás ucí nenávidet, ale takové, která by nás ucila lásce k blinímu, 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 tiei tik daudz dievticibas, lai ienistu, bet nepietiekoi, 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 _ To unsubscribe from Verba Volant, please follow this link: http://www.logos.net/owa-l/press.rol_ml.verbavolant1?lang=en and write in the empty field next to unsubscribe the email address that you find after TO: in the Verba Volant emails alternatively write to the following address: [EMAIL PROTECTED] always copying the EMAIL address written after X-RCPT-TO:
Re: factor Analysis
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. = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
imputation in the general location model
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 = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: factor Analysis
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 = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
views on the mcas?
http://askearth.com/go/view_request?request=5058 = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: cutting tails of samples
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 = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
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