logging in or signing up 13 9 00 A Hannah Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 41 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: December 29, 2007 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript GRAPHICAL REPRESENTATIONSOF ADATA MATRIX: GRAPHICAL REPRESENTATIONS OF A DATA MATRIXSlide2: SYSTEM CHARCTERISATION SYSTEM Numbers MeasureSlide3: CHARACTERISATION UV,IR,NMR, MS,GC,GC-MS ..................... .................... . .................... Sample Instrument + Computer Instrumental Profiles Data matrixSlide4: Numbers Measure Information (Graphics) Latent Projections ModellingSlide5: X Data matrix Variable vectors (column vectors) Object vectors (row vectors) x’k xiSlide6: DATA MATRIX / DATA TABLE Slide10: i j k 1 5 l 3 1 m 8 6 Object Variable Column-centred data matrix i j k -3 1 l -1 -3 m 4 2 Object Variable Original data matrixSlide11: VARIABLE SPACE x’l Shows relationships between objects (angle kl measures similarity). cos kl = x’k xl/|| x’k || || xl || i j k -3 1 l -1 -3 m 4 2 Slide12: OBJECT SPACE Shows relationships (correlation/covariance) between variables (correlation structure) The angle ij represents the correlation between variable i and j. i j k -3 1 l -1 -3 m 4 2 cos ij = x’i xj/|| x’i || || xj ||Slide13: Object space shows common variation in a suite of variables! common variation underlying factor! Slide14: VARIABLE SPACE AND OBJECT SPACE CONTAIN TOGETHER ALL AVAILABLE INFORMATION IN A DATA MATRIXSlide15: WHAT TO DO IF THE NUMBER OF VARIABLES IS GREATER THAN 2-3? PROJECT ONTO LATENT VARIABLES (LV)!Slide16: PROJECTING ONTO LATENT VARIABLES Projection (in variable space) of object vector xk (object k) on latent variable wa : tka = x’kwa , k=1,2,..,N (score)Slide17: LATENT VARIABLE PROJECTIONSSlide18: Successive orthogonal projections (SOP) i) Select wa ii) Project objects (sample, experiment) on wa: ta = Xawa iii) Project variable vectors on t: p’a = t’aXa/t’ata iv) Remove the latent-variable a from preditor space, i.r. substitute Xa with xa - tap’a. Repeat i) - iv) for a= 1,2,..A, where A is the dimension of the modelSlide19: PCA/SVD wa = pa/||pa|| PLS wa = u’aXa/|| u’aXa || MVP wa = ei MOP wa = xk/||xk|| TP wa = bk/||bk|| METHOD OVERVIEWSlide20: METHOD OVERVIEWLATENT PROJECTION: IS AN INSTRUMENT TO CREATE ORDER (MODEL) OUT OF CHAOS (DATA) LATENT PROJECTIONSlide24: LATENT VARIABLE MODELSlide25: PCA/PLS (orthogonal scores)Slide26: Visual Interface Score plot - variable space Loading plot - object space Biplot plot - Scores and loadings in one plot!Slide27: EXTENDING THE LATENT VARIABLE MODEL You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
13 9 00 A Hannah Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 41 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: December 29, 2007 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript GRAPHICAL REPRESENTATIONSOF ADATA MATRIX: GRAPHICAL REPRESENTATIONS OF A DATA MATRIXSlide2: SYSTEM CHARCTERISATION SYSTEM Numbers MeasureSlide3: CHARACTERISATION UV,IR,NMR, MS,GC,GC-MS ..................... .................... . .................... Sample Instrument + Computer Instrumental Profiles Data matrixSlide4: Numbers Measure Information (Graphics) Latent Projections ModellingSlide5: X Data matrix Variable vectors (column vectors) Object vectors (row vectors) x’k xiSlide6: DATA MATRIX / DATA TABLE Slide10: i j k 1 5 l 3 1 m 8 6 Object Variable Column-centred data matrix i j k -3 1 l -1 -3 m 4 2 Object Variable Original data matrixSlide11: VARIABLE SPACE x’l Shows relationships between objects (angle kl measures similarity). cos kl = x’k xl/|| x’k || || xl || i j k -3 1 l -1 -3 m 4 2 Slide12: OBJECT SPACE Shows relationships (correlation/covariance) between variables (correlation structure) The angle ij represents the correlation between variable i and j. i j k -3 1 l -1 -3 m 4 2 cos ij = x’i xj/|| x’i || || xj ||Slide13: Object space shows common variation in a suite of variables! common variation underlying factor! Slide14: VARIABLE SPACE AND OBJECT SPACE CONTAIN TOGETHER ALL AVAILABLE INFORMATION IN A DATA MATRIXSlide15: WHAT TO DO IF THE NUMBER OF VARIABLES IS GREATER THAN 2-3? PROJECT ONTO LATENT VARIABLES (LV)!Slide16: PROJECTING ONTO LATENT VARIABLES Projection (in variable space) of object vector xk (object k) on latent variable wa : tka = x’kwa , k=1,2,..,N (score)Slide17: LATENT VARIABLE PROJECTIONSSlide18: Successive orthogonal projections (SOP) i) Select wa ii) Project objects (sample, experiment) on wa: ta = Xawa iii) Project variable vectors on t: p’a = t’aXa/t’ata iv) Remove the latent-variable a from preditor space, i.r. substitute Xa with xa - tap’a. Repeat i) - iv) for a= 1,2,..A, where A is the dimension of the modelSlide19: PCA/SVD wa = pa/||pa|| PLS wa = u’aXa/|| u’aXa || MVP wa = ei MOP wa = xk/||xk|| TP wa = bk/||bk|| METHOD OVERVIEWSlide20: METHOD OVERVIEWLATENT PROJECTION: IS AN INSTRUMENT TO CREATE ORDER (MODEL) OUT OF CHAOS (DATA) LATENT PROJECTIONSlide24: LATENT VARIABLE MODELSlide25: PCA/PLS (orthogonal scores)Slide26: Visual Interface Score plot - variable space Loading plot - object space Biplot plot - Scores and loadings in one plot!Slide27: EXTENDING THE LATENT VARIABLE MODEL