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ȸ±ÍºÐ¼®À» ÅëÇÑ ÅͳΠºØ±«µµ ¹× ÇÔ¼ö ¸ðµ¨¸µ ÇÁ·ÎÁ§Æ®.

¿ÀÀü 3:08 2004-06-14, by Kenial

[Upload]sample.zip »ùÇà µ¥ÀÌÅÍ
[Upload]20040613_r_script.txt nls ½ºÅ©¸³Æ®

¿ÀÈÄ 9:55 2004-06-11, by Kenial

ÆĶó¸ÞÅÍ ¹üÀ§(¿¹»ó)

¿ÀÀü 12:55 2004-06-10, by Kenial

Å×½ºÆ®¿ë ÆĶó¸ÞÅÍ
1-1 
  C0          .107785647 
  A          1.305090740 
  B           .074960389 
1-2 
  C0          .191568085 
  A          1.412937532 
  B           .094437384 
2(ÀÌ°Ç Á» ³ªÁß¿¡) 
  C0         2.255114380 
  A           .259494577 
  B         8646.0555381 
3 
  C0          .167183448 
  CX         1.393850202 
  XX        19.733595112  
4 
  C0        -1.580096488 
  A         1.471346848 
  B          .088694718 
  C        -1.858608651 
  D         2.493052423 
5 
  C0        .430609326 
  CX        3.93449954 
  XX        8.83168773 
  T         2.25797694 
  M        -.58709922 

¿ÀÀü 10:51 2004-06-08, by Kenial

°ÅÀÇ ¸¶Âù°¡Áö °á·ÐÀÌÁö¸¸, r-project¿¡¼­µµ ¸ð¼ö¸¦ ÀûÀýÈ÷ Á¶ÀýÇÏ¸é °°Àº(!) °á°ú¸¦ ¾òÀ» ¼ö ÀÖÀ½.
´ë½Å, ¸ð¼öÀÇ °ª¿¡ ÀÌ»óÀÌ ÀÖÀ» ¶§ ±× ÇØ°áÁ¡À» ã´Â ¹üÀ§¿¡¼­ spss°¡ Á» ´õ ¿ì¼ö.

try to do : r-project¿¡¼­ ¸ð¼öÀÇ °ªÀÇ ¹üÀ§¸¦ Á¤ÇÑ ´ÙÀ½ °¢°¢ÀÇ °ª¿¡ ´ëÇÑ divide-and-conquer ½Ãµµ.

¿ÀÀü 1:22 2004-06-08, by Kenial

¸ð¼öparameter Á¶Á¤ ¼º°ø!
[Upload]spsssyntax_parametered.txt

´ë·« ¾î¹ö¹öÇÏÁö¸¸ À¢°£ÇÑ °á°úÄ¡¿¡¼­´Â 4°³ ÀÌ»óÀÇ ¹æÁ¤½ÄÀÌ 80% ÀÌ»óÀÇ r^2 °ªÀ» º¸ÀÓ.

¿ÀÀü 1:03 2004-06-07, by Kenial

r-project¿¡¼­ÀÇ nonlinear regression ÇÔ¼ö Å×½ºÆ® Áß.

tunnel <- read.table("d:tunnel.txt", TRUE)
ft1 <- nls( c ~ A * ( 1 - exp( -B * x ) ) - c0, data = tunnel,

ft1 <- nls( c ~ (( 1 - exp( -B * x ) ) * A ) - c0, data = tunnel,
ft1 <- nls( c ~ (( 1 - exp( -B * t ) ) * A ) - c0, data = tunnel,
ft1 <- nls( c ~ ( A * log(1 + (B*t)) ) - c0, data = tunnel,
ft1 <- nls( c ~ ( 1 - ( ( XX / ( XX + x ) ) ** 2 ) ) - c0, data = tunnel,
ft1 <- nls( c ~ pa * (1 - exp(-pb * x)) + pc * (1 - exp(-pd * t)) - c0, data = tunnel,
ft1 <- nls( c ~ PCX*(1- ( (PX/(PX+x)) **2 )) * (1+ PM*(1-((PT/(PT+t))**0.3))) - c0,

°á±¹ ¶Ç »ðÁú ÀÛ¾÷À» ÇßÀ½ÀÌ ¹àÇôÁü.
±âÃÊ parameter 0À¸·Î Á¶Á¤ ¾øÀÌ
3¹ø ¸ðµ¨¿¡¼­ R^2 = .98826, 5¹ø ¸ðµ¨¿¡¼­ R^2 = .91620 ¼öÄ¡ ³ª¿È
(5¹ø ¸ðµ¨ÀÇ °æ¿ì Ç¥ÁØ¿ÀÂ÷°¡ »ó´çÇÏ¿© º° Àǹ̰¡ ¾ø´Â °ª)

³»°øº¯À§ÀÇ °ªÀº ÃøÁ¤Ä¡¿¡ -1À» °öÇØÁÖ¾î¾ß ÇßÀ½.

3¹ø ¸ðµ¨ÀÇ °á°ú :

Nonlinear Regression Summary Statistics     Dependent Variable C 
 
  Source                 DF  Sum of Squares  Mean Square 
 
  Regression              3       10.27649        3.42550 
  Residual               10         .01351   1.350632E-03 
  Uncorrected Total      13       10.29000 
 
  (Corrected Total)      12        1.15077 
 
  R squared = 1 - Residual SS / Corrected SS =     .98826 
 
                                           Asymptotic 95 % 
                          Asymptotic     Confidence Interval 
  Parameter   Estimate    Std. Error     Lower         Upper 
 
  C0          .129819687   .050150412   .018077605   .241561769 
  CX         1.139746965   .049305817  1.029886758  1.249607172 
  XX        15.949666320  1.886976217 11.745221299 20.154111341 

stableÇÑ °ªÀº ¾Æ´ÏÁö¸¸, ÃʱâÄ¡ÀÇ ¼³Á¤¿¡ µû¶ó Á¦´ë·Î µÈ °ªÀ» ¾ò¾î³¾ ¼ö ÀÖÀ» µí ÇÔ.

¿ÀÀü 2:20 2004-06-06, by Kenial

* A * ( 1 - EXP(-B*x) ).
* NonLinear
? Regression.
MODEL PROGRAM c0=0 A=0 B=0 .
COMPUTE PRED_ = A * ( 1 - EXP(-B*x) ) - c0.
NLR c
* A * ( 1 - EXP(-B*t) ).
* NonLinear? Regression.
MODEL PROGRAM c0=0 A=0 B=0 .
COMPUTE PRED_ = A * ( 1 - EXP(-B*t) ) - c0.
NLR c
* A * ln(1 + (B*t))
* NonLinear? Regression.
MODEL PROGRAM c0=0 A=0 B=0 .
COMPUTE PRED_ = A * ln(1 + (B*t)) - c0.
NLR c
* Cx * ( 1 - ( ( XX / ( XX + x ) ) ** 2 ) ).
* NonLinear? Regression.
MODEL PROGRAM c0=0 Cx=-1.20 XX=0 .
COMPUTE PRED_ = Cx * ( 1 - ( ( XX / ( XX + x ) ) ** 2 ) ) - c0.
NLR c
* pa * (1-EXP(-pb * x)) + pc * (1-EXP(-pd * t)).
* NonLinear? Regression.
MODEL PROGRAM c0=0 PA=0 PB=0 PC=0 PD=0 .
COMPUTE PRED_ = pa * (1-EXP(-pb * x)) + pc * (1-EXP(-pd * t)) - c0.
NLR c
* PCX * ( 1 - ( ( PX / ( PX + x ) ) **2 ) ) * ( 1 + PM * ( 1- (( PT / (PT + t ) ) **0.3 ) )).
* NonLinear? Regression.
MODEL PROGRAM c0=0 PCX=0 PX=0 PT=0 PM=0 .
COMPUTE PRED_ = PCX * ( 1 - ( ( PX / ( PX + x ) ) **2 ) ) * ( 1 + PM * ( 1- (( PT / (PT + t ) ) **0.3 ) )) - c0.
NLR c

¿ÀÀü 1:42 2004-06-04, by Kenial

¹º°¡ µÇ´Â °Í °°±âµµ ÇÏ°í ¾Æ´Ñ °Í °°±âµµ ÇÏ°í.. ȯÀåÇÏ°Ú³×.~
Cm = C(x,t) - C0 ... ¾Æ Á¨Àå ¼ö½Äµµ Á¦´ë·Î üũ¸¦ ¾ÈÇÏ°í »¹ÁþÇÏ´Ù´Ï ...

Levenberg-Marquardt MethodÀÌ ¾Æ´Ï¶ó¸é... sequential quadratic programmingÀÌ´Ù!

¿ÀÈÄ 4:18 2004-06-03, by Kenial

http://groups.google.co.kr/groups?hl=ko&lr=&ie=UTF-8&newwindow=1&threadm=8kjf16%24mnf%241%40b5nntp2.channeli.net&rnum=3&prev=/groups%3Fq%3Dlevenverg-marquardt%2520algorithm%26hl%3Dko%26lr%3D%26ie%3DUTF-8%26newwindow%3D1%26sa%3DN%26tab%3Dwg

http://groups.google.co.kr/groups?q=SNLS1&btnG=%EA%B5%AC%EA%B8%80+%EA%B2%80%EC%83%89&hl=ko&lr=&ie=UTF-8&newwindow=1

http://www-fp.mcs.anl.gov/otc/Guide/OptWeb/index.html

¿ÀÀü 12:17 2004-06-03, kenial

  1. µ¥ÀÌÅÍ ÀÔ·Â.
  2. ¸ðµ¨ 1~5¸¦ ÅëÇØ ¸ð¼öÀÇ °ªÀ» ÃßÁ¤.
  3. ÃßÁ¤µÈ ¸ð¼öÀÇ °ªÀ¸·Î ºñ¼±Çü ȸ±ÍºÐ¼® ½Ãµµ.
  4. ȸ±ÍºÐ¼®ÀÇ Squared r°ªÀ» ÀÌ¿ëÇØ ÀûÀýÇÑ ¸ðµ¨ °áÁ¤.
  5. ´ÙÁß°ø¼±¼º üũ.
  6. plot Ãâ·Â.

    analyze - regression - curve estimation ÈÄ ÇÔ¼öÀÇ Å¸ÀÔÀ» Å×½ºÆ®...!
    ±×´ÙÀ½ ³ª¿Â b1 °ªÀ» Åä´ë·Î nonlinear regressionÀ» ½Ø¿î´Ù
    »ó¼öÇ× Æ÷ÇÔ/ºñÆ÷ÇÔÀ» Âü°í
    (ÇÑ±Û spss 10.0¿¡ ÀÇÇÑ ¾Ë±â ½¬¿î ´Ùº¯·®ºÐ¼®, ³ëÇüÁø Àú, Çü¼³ÃâÆÇ»ç)

¿ÀÈÄ 11:45 2004-05-27, kenial

»ùÇà µ¥ÀÌÅ͸¦ ÀÌ¿ë, c = PCX * ( 1- ((PX / (PX + x ))**2)) * ( 1 + PM * (1- (PT / (PT + t))**0.3)) ¹æÁ¤½ÄÀÇ È¸±ÍºÐ¼®À» ½ÃµµÇßÀ¸³ª, PT + t °ªÀÇ 0ÀÏ ¶§°¡ ÀÖ¾î divide by zero ¿¡·¯¸¦ ³»°í »ç¸Á.
PT > a ÀÇ ÀÏÁ¤°ªÀ» ÁöÁ¤ÇÑ ÈÄ Squared R °ªÀÇ º¯È­ :

a Squared R
1E-20 .04616
1E-19 .04616
1E-18 .56504
1E-17 .56458
1E-16 .56465
1E-15 .56478
1E-14 .56420
1E-13 .56447
1E-12 .56293
1E-11 .56529
1E-10 .56818
1E-09 .59836
1E-08 .62288
1E-07 .77039
1E-06 .63823
1E-05 .74458
1E-04 .89697
1E-03 .89542
1E-02 .89231
1E-01 .89485
1 .89521
1E+01 .85480
1E+02 .62513

µµ´ëü ÀÌ °á°ú¸¦ ¾î¶»°Ô ¹Þ¾Æµé¿©¾ß ÇÒÁö ¾Ë ¼ö ¾øÀ½.

¿ÀÈÄ 11:45 2004-05-27, kenial

2Â÷ ¹ÌÆÃ

¿ÀÀü 10:00 2004-05-27, kenial

¿ÀÈÄ 6:35 2004-05-26, kenial

1Â÷ ¹ÌÆÃ

±âŸºÐ·ù


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