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×î½üͶ¸åµ½J. Comput.-Aided Mol. Des.µÄһƪÎÄÕ£¬Éó¸åÈ˶ÔÄâºÍÄ£Ð͵ÄÑéÖ¤Ìá³öÒ»¸öÎÊÌ⣬¾ÍÊÇÏ£ÍûÔö¼ÓÒ»¸öY£randomization test£¬ÒÔÔö¼Ó˵·þÁ¦¡£ÒÔǰ·¢µÄÎÄÕÂÒ»°ãÊǶÔÄ£ÐÍ×öÒ»¸öÄÚ²¿Êý¾Ý¼¯µÄ½»²æÑéÖ¤£¬ÔÙ×ö¸ö¶ÔÍⲿ²âÊÔ¼¯ÑéÖ¤¾Í¿ÉÒÔÁË£¬Y£randomization»¹ÕæÃ»×ö¹ý¡£²éÁËһϳ£ÓõÄͳ¼ÆÑ§Èí¼þÈçoriginºÍSPSS£¬ºÃÏñҲûÕÒµ½Ïà¹ØµÄÄ£¿é»ò¹¦ÄÜ¡£ÒòΪÔÀíºÜ¼òµ¥£¬¾Í¶¯ÊÖдÁ˸öRµÄ½Å±¾£¬ÏÖÔڰѱà¼ÎʵÄÎÊÌ⣬ÎҵĻشð£¬ÒÔ¼°Õâ¸ö½Å±¾ÄÃÀ´ºÍ´ó¼Ò·ÖÏí£¬Ï£Íû¶Ô¸÷λͬÐÐÓвο¼¼ÛÖµ¡£ ÎÊÌ⣺I advise the authors to perform additional validation for the models developed. For example, conduct the Y-randomization test (scramble stability test). Eighteen compounds in the test set is a low number for a training set of 108 molecules. »Ø´ð£ºThanks for the reviewer¡¯s advice. We have included the Y-randomization test results in Section 3.4, Results and Discussion. Except for the concern of the generalizability, the high internal validation performance of our xxxx models might be a result of chance correlation. To address this problem, these three models were validated by applying the Y-randomization of response test (in this work, the experimental activity values). It consists of repeating the calculation procedure several times after shuffling the Y vector randomly. If all models obtained by the Y-randomization test have relatively high values for both q2 and r2 statistics, this is due to a chance correlation and implies that the current modeling method cannot lead to an acceptable model using the available data set. This was not the case for the data set and methodology used in this work. Ten random shuffles of the Y vector were performed and the results are shown in Table 4. The low q2 and r2 values show that the good results in our original models are not due to a chance correlation or structural dependency of the training set. Table 4. Y-Randomization results of the three models. Iteration Model1 Model2 Model3 r2 q2 r2 q2 r2 q2 1 0.06087 0.02613 0.03359 0.00700 0.00004 0.27470 2 0.00050 0.06217 0.00011 0.05587 0.00675 0.01233 3 0.00287 0.07728 0.01106 0.00452 0.00673 0.01450 4 0.00309 0.06925 0.01152 0.00495 0.03866 0.00979 5 0.02495 0.00113 0.00021 0.37390 0.00426 0.04193 6 0.00080 0.27630 0.00003 0.43000 0.00200 0.15350 7 0.00424 0.04414 0.00728 0.01734 0.03321 0.00651 8 0.02441 0.00040 0.01375 0.00040 0.02045 0.00008 9 0.00199 0.09985 0.01244 0.00248 0.03296 0.00430 10 0.00795 0.01594 0.00122 0.2014 0.01232 0.00541 ½Å±¾£º d<-read.table('Êý¾ÝÎļþ1',header=TRUE) vp<-1:nrow(d) TIMES=100 for ( j in 1:TIMES){ x <- d$Ä£ÐÍ1 y <- sample(d$ÊÔÑéÖµ) print(summary(lm(y~x))) for ( i in 1:nrow(d)){ x1 <- x x0 <- x[-i] y1 <- y y0 <- y[-i] yp<-predict(lm(y0~x0),data.frame(x0 = x), se.fit = TRUE) vp=yp$fit } print(summary(lm(y~vp))) }×¢£º¡°Êý¾ÝÎļþ1¡±Îªtab·Ö¸ôµÄ£¬ÐбêΪÊÔÑéÖµ£¬ÁбêΪģÐÍÔ¤²âÖµµÄÎı¾Îļþ¡£ [ Last edited by alwens on 2006-9-6 at 15:42 ] |
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goldjay
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