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Data X:
28375 16 60.5 30.5 5.6 14678 27 53.8 36.2 0.5 25286 26 73.8 7.7 16.8 17194 25 1.2 0.6 98.8 33954 29 92.5 1.4 1.7 15523 29 7 7.7 79 25949 22 50.8 0.3 44.2 25043 35 8.6 0.2 84.1 16778 44 14.6 17.7 66.3 22005 31 63.6 7.7 26.5 26916 76 51.4 0.9 44.9 51186 40 67.3 0.6 12.9 25563 31 83.7 7.6 3.1 25998 23 64 23.5 7.5 20027 39 37.5 27 24.7 23205 25 30.5 58.4 6.4 29881 54 52.2 16.2 15.7 41676 24 44.8 6.9 16.5 37455 57 31.3 7.5 15.2 27384 21 28.3 5.1 58.1 23342 42 92.3 2.3 2.9 22719 21 40.3 10.7 41.4 18376 36 17.8 4.4 76.4 17695 26 77.7 0.9 9.7 21610 49 78.1 2.2 15.1 32921 54 94 1.8 1.9 22441 26 7.7 6 77.9 23926 48 86.2 7.4 6.5 13231 33 6 3.4 89.9 19599 21 56.6 1.2 35.9 14995 41 1.6 66.4 35.2 22968 48 0.6 98.6 0 34698 36 89.9 4.3 1.4 22085 41 93.7 0.6 5.1 21868 29 42 12.3 37.1 32142 27 60.1 9.5 13.7 21769 45 65.1 23.7 6.3 15373 32 4.3 3.4 89.6 16558 35 16.5 0.9 78.7 17853 36 53.7 44.4 1.8 20248 35 94.1 2.4 2.6 17225 40 33.9 2.6 56.6 26667 18 83.9 4 5.5 15488 34 72.1 14.5 6.8 19978 39 4.2 0.2 93.9 18995 21 3.3 11.7 84.7 14256 62 5.1 85.1 6.2 18843 43 59.9 31.7 2.9 21170 44 15.8 0.3 81.5 20901 29 39.3 0.1 1.7 86023 35 60.6 7.8 10.5 22105 50 57 17.7 19.8 14560 18 0 98.9 0.1 27813 25 74.9 0.8 22.8 28125 31 89 0 8.1 42931 29 70.7 4.5 15.5 20407 49 79.7 1.4 14.5 18047 23 2 1.1 96.6 35827 45 69.7 0.1 28 17893 26 1.9 47.3 45.9 31080 35 94.9 0.6 3.2 25130 34 93.2 0.3 2.2 35788 46 43 7.7 1 28556 29 86.2 6.4 3.9 27031 39 73.2 6 14.4 11805 28 12.2 18.9 48.3 20428 51 46 0.9 51.3 12840 67 95.9 0 0 31274 53 94.4 0.5 2.9 14177 25 0 98.4 1 13805 30 18.1 67.5 11.5 16689 24 6.5 31.8 58.6 14746 35 40.7 1.4 35.8 8206 43 18 54.9 23 22409 24 71.5 20.2 3.9 37579 29 41 1.7 53.4 33294 38 88.7 3 1.7 22080 31 67.2 21.7 10 21049 36 61.8 6.4 15.4 37721 23 52.5 36.5 4.4 21845 38 78.2 5.4 14.2 23583 53 94.9 3.7 0.3 14816 24 62.7 0.3 28.3 22235 26 32.9 15 48.3 21264 28 82.8 4.9 5.3 21667 34 63.6 7.7 21.4 22472 28 31.8 51 12 25799 40 3.8 81.2 6.1 21344 51 1.5 83.1 15.6 30465 44 87.8 0 10.3 19146 25 8.5 0.4 89.6 38593 56 69.3 2.8 17.8 21626 37 51.6 11 18.4 24749 58 62.5 15.8 15.9 16218 39 55.5 6.4 36 22833 37 49.2 0.1 49.8 39122 35 23.7 54.6 7 11157 26 17.4 75.7 0 22857 47 43.6 4.9 36 12109 31 41.5 52.6 3.1 22385 60 65.4 32.1 1.5 20610 32 40.4 44 10.4 10987 45 88.9 0.1 6.4 21474 42 75.3 13.7 10.5 21109 17 74 1.3 20.3 18438 22 20.4 9.8 70.7 12741 33 5.7 6.1 88.5 30061 18 88.2 8.5 1.4 29488 39 94.1 3.2 1.5 30491 59 92.2 2.9 5.9 18370 33 44.7 25 1.9 24348 58 94.3 3.9 1.1 23883 58 95.7 0 2 15373 47 51.3 47.8 0.9 41609 41 76.8 2.1 8.8 21130 64 22.5 1.8 64.6 19598 45 9.5 0 33.9 33869 53 87.1 1.8 3.4 16983 24 54.9 22 19.1 23944 48 10.8 86.4 2.6 20254 29 3.2 94.4 5 15656 40 0.9 19.5 71.9 26928 34 47.3 6.7 29 30784 21 28.9 22.8 39.6 19921 26 52.2 5.1 41.5 14256 45 0.4 84.8 14 18257 25 19 46.1 28.8 32088 17 96.1 0.1 3.1 26846 24 23.6 3 49.1 36099 42 86.8 0.3 10.5 22193 29 37.5 5 54.3 19031 42 54.9 42.7 0.6 28490 30 55 22.1 14.9 21858 29 76.2 0 19.9 14520 39 9.3 81.6 13.5 25135 63 83.5 9 1.1 21837 49 94.3 4.7 0.1 19755 41 70.6 24.1 1.5 20623 27 21.5 53 20.9 51071 30 91.1 2.1 3.4 21342 60 25.2 17.5 53.5 13968 77 22.2 77.8 1.5 16415 19 65 0.9 30.3 24026 37 15.6 5.8 66 37922 54 11.3 86.4 0 17632 29 48.3 8.1 38.4 31009 30 34.7 16.3 14.6 35008 24 43 18.6 23.5 16415 21 65 0.9 30.3 16953 44 52.2 23.8 10.1 20644 40 68.6 0 23.6 31930 32 84.1 0.3 3.8 27255 22 7.9 2.1 83.7 19343 43 20.6 5.9 71.2 22177 52 12.6 3.5 77 16620 27 0.5 0.2 96.3 27273 34 67.9 0 27.1 26675 20 84.7 4.4 7.5 18976 25 11.1 75.1 11.3 37028 24 63.1 0.5 6.5 31776 20 46.4 6.1 24.4 18363 46 66.2 30.9 0.1 23716 42 70.2 1.3 23.3 23444 43 39.3 27.1 22.1 30261 41 21.2 19 57.8 25030 59 34.2 57.3 1.1 14227 25 0.1 99.4 0 23467 42 56.7 9.1 24 21261 64 66.2 28.5 3.8 31641 22 90 9.3 0 41377 24 86.9 0.7 2.9 18397 63 69.1 0.8 2.6 40529 56 80.6 0.6 10.5 22384 60 96.3 0.3 0.2 23683 54 82.8 15 0.4 20405 37 57.8 7.4 20.3 27745 22 74.2 10.8 9.1 36727 39 61.4 24.2 10.7 21693 45 84.3 4.6 6 20760 57 89 8.6 1.9 26643 42 99.6 0.2 0 17223 19 4 9.5 68.2 14932 26 33.1 3 52.5 18243 34 97.3 0.8 0.3 16615 69 94.2 3.4 1.1 27432 64 44.1 18.9 32.7 21306 35 4.6 35.9 35.9 20836 40 33.2 62.2 2.8 14048 19 0.2 99.8 0 18727 27 86.2 9.8 1 17433 37 16 10.6 72.4 14504 17 69.6 6.6 19.9 11338 39 56.7 34.3 7.1 15489 74 33.3 43.7 22.3 30391 42 87.2 0.1 5 18238 47 42 27.1 29.9 38810 43 85.5 11 0.7 39179 46 94.4 0 4.2 30489 44 96.5 0.7 1.6 22255 31 36.5 57.6 0.3 24705 47 96.8 0.2 0.8 41719 41 73.8 2.5 17.7 20213 43 63.2 21.4 4.7 22947 40 89.6 0 4.1 30906 32 62.2 1.6 28.7 10219 31 5.4 93.1 1.3 22021 20 79.4 0.3 17.1 11184 20 8.4 88 5.8 19808 33 70.9 19.6 7.1 18029 22 9.9 68.6 20.6 18975 41 58.6 0 40.4 37476 41 35.3 19.7 29.2 19868 32 62.5 27.2 8.4 25865 16 38 53 3.2 28397 29 72.5 1.2 19.3 22750 42 62.8 32.1 1.9 19032 29 14 60 26.6 12297 47 28.7 17.7 48.7 19423 53 39.3 59.4 0 26314 18 1 95 0.7 15289 47 99.4 0.2 0 30625 34 83.3 2.2 8.7 29063 36 56.8 34 4.4 36696 63 51.1 0.4 6.1 35194 36 64.1 8.1 24.7 17493 27 59.9 34 2 19218 28 56.5 35.1 2.9 16616 33 71 21 3.6 37967 32 85.7 9.4 2.7 26001 42 60.3 8.8 25.7 27355 31 87.3 1.2 9.2 32602 17 38.8 43.2 13.7 20860 28 87.4 0 10.4 20396 24 50.9 31.2 7.9 18750 71 67.9 29.8 1.3 21988 51 40.2 12.6 44.2 23243 28 30.6 11.5 44.4 48922 53 97.1 0.5 0.9 23940 54 90 0 6.7 22681 45 20.6 74.2 2.2 27385 33 90.5 5.1 2.2 24076 48 75.4 1.6 6.5 34326 34 63.9 3.7 26.6 22016 23 45 34.2 15.7 23378 35 71.2 0 23.6 32902 33 50.4 11.7 10.2 45333 32 59.2 1.4 24.3 32466 52 89.3 4.9 0.5 16670 30 1.5 16.6 36.3 26030 23 32.9 18.3 39.2 22158 35 79.9 4.4 10.4 14765 42 41.5 9.1 37.7 20769 37 67.9 30 0.1 15511 56 24.3 4.9 67.2 28062 36 34.1 24.5 26 15480 27 5.1 2.7 80 23633 30 76.9 8.5 4 27172 31 79.9 3.8 10.3 17213 46 29.6 13.1 56 19316 51 76.4 1.7 3.5 24332 72 14.9 24.9 34.2 22628 28 31.8 9.3 48.6 30661 63 63.1 9.4 18.8 30908 28 27.6 0.7 26.2 22995 33 52.8 1.8 41.3 22734 24 46.9 1 51.7 19474 27 82.7 8.9 3.2 13130 24 4.3 0.3 2.7 23280 28 37.7 22.7 10.9 40021 28 79.1 4.6 9.2 21955 17 26.6 40.7 26.2 33918 46 83.3 1.5 10.9 19136 52 45.7 21.4 28.2 25279 39 37.7 17.2 42.1 23974 49 38.6 9.1 52.8 29893 30 61.9 20.5 18.4 23250 51 75.3 3.5 14.3 14754 16 5.2 93.6 0.7 16463 18 68 17.4 6.4 32211 22 60.2 4 30.3 25418 40 67.6 26.3 4.3 18760 61 97.2 0.3 0.6 34176 52 64.4 24.6 5.2 25226 51 61.8 29.3 6 36305 36 75.3 1.1 17.3 31491 36 79.9 0 10.8 41145 59 71 17.6 2.7 14332 17 44.8 34.1 19.7 26583 18 92.3 1.3 5.7 23750 41 90.5 0.6 7.6 20075 33 25.5 11.1 13.6 26067 25 5.9 83 9.5 17693 23 28.5 59.4 6.1 18978 47 34.5 59.4 1.4 19645 58 91.8 5.1 1.5 29959 47 69.3 0 19.8 35089 34 1.6 92.7 5.1 28125 28 87.5 0.1 3.3 17901 37 75.4 8.6 4.9 27077 87 91 0.6 0.8 54036 39 92.7 0.1 1.6 28730 27 44.7 0 41.7 12376 35 2.4 2 72 20072 36 1.4 87.2 4.5 11425 24 18.9 42.3 5.4 22537 26 56.4 31.8 4.4 26119 34 19.2 75.3 4.8 5457 51 69.4 4.4 3.5 17474 49 62 0.4 31.7 15542 41 49.7 16.5 26.3 24858 54 80.7 0.7 1.4 19029 36 39.8 7 50.1 13143 26 5.8 92.8 2.5 17026 35 32.8 7.8 35.5 25190 22 47.5 28.2 22.6 19400 27 88.5 9.9 0.7 30332 42 62.1 7.1 15.5 64657 32 73.5 0.9 11.8 33875 32 94.7 0.9 3.4 25839 25 71.7 2.3 9.8 29511 26 16.1 2.6 70.9 21506 53 97.2 1.3 0 20296 26 74.9 2.3 12 17696 40 90 1.8 2.3 13939 55 9.7 89.1 0 27917 29 97.1 1.1 0.3 13929 31 93.4 2 1.3 30533 19 15 0.4 11.1 18472 57 87.2 0.2 3.6 20297 40 77.2 17.8 3.7 30723 35 40.1 45.6 3.1 23011 35 65.6 11.1 19.8 18022 39 33.3 0.7 61.8 49269 37 78 15.7 2.6 14457 36 3.2 0 96.8 11405 62 58.2 9.8 24.8 21008 43 23.3 0.9 68 15892 32 79 4.1 9.4 31009 34 34.7 16.3 14.6 21082 37 26.2 24 41 5688 37 58.6 17.8 7.9 21831 33 95.9 2 0 25231 35 73.5 5.6 4.5 21131 40 83.3 2.2 6.6 42774 21 14.5 4.5 7.9 16071 30 49.7 13.6 32.2 45795 23 37 45.7 10.6 16012 26 16.3 12 55.4 42519 39 94.4 0.9 3.1 40693 33 46.4 5.3 43.2 37476 34 35.3 19.7 29.2 9353 37 2.1 84.2 5.4 22057 26 42.6 3.2 52.9 39531 24 41.8 8.7 8.8 15628 25 5.9 14.5 62.7 25755 31 15.7 1.6 69.1 36062 49 94.5 0.2 0.2 37453 59 28.1 7.1 32.9 17402 50 23.5 1.3 72.3 34516 37 10.6 23 7.8 23844 28 69.7 16.6 3.3 40387 26 29.8 25.3 34.5 22132 23 7.2 1.9 83.4 17401 32 5.1 21.3 77.8 43458 24 86.7 3.3 3.1 14964 42 76.8 8.5 7.3 15556 34 1.1 60.3 37.2 28690 68 61.7 9.8 16.3 18174 31 19.6 3.5 74.1 25034 83 82.8 1 5.2 20761 35 37.4 10.7 47.7 26047 29 24.2 5.6 70.2 11558 50 0.6 95.6 3.9 26741 56 72.8 0.3 26.3 26101 43 47.6 0.3 50.6 28781 38 89.6 3.4 4.5 20995 27 58.6 25.5 10 22348 36 73.6 0 24.2 23281 55 85.8 0.5 6 21115 36 36.3 2.3 59.8 27875 68 62.3 6.1 30 31969 61 53.3 6.4 34.6 20153 46 82.5 10.5 0.4 30540 47 86.8 1.5 10.6 18133 26 95.5 0 0 32242 37 27.6 5.5 46.4 30472 22 49 2.5 22.5 25977 18 75.2 1.5 21.7 14162 39 1.7 0 94.3 12930 49 9.9 85.3 4.8 16953 23 52.2 23.8 10.1 21130 47 30.1 1.4 66.5 24236 32 47.3 8.8 43.7 19151 45 9.5 67 21.7 20900 51 2.3 0.8 97.1 43062 31 68.9 10.7 10.7 16558 54 16.5 0.9 78.7 21374 31 80.9 0.2 11.9 15632 23 13 10.4 61.1 19746 29 8.5 6.3 84.5 23243 28 30.6 11.5 44.4 23143 31 57.8 15.5 2.2 15812 24 4.8 0.4 94.8 44430 27 67 1.5 19.7 17467 57 8.5 0 27.3 34053 39 86.7 0.8 9.2 33110 38 81.1 8.6 2.7 44585 34 58.6 2.8 19.2 19149 39 86.3 2.1 0 16065 20 14.3 38.8 44.9 24352 35 54.5 0 42.3 29056 36 65.8 11.3 21.1 21509 38 26.1 13.4 2 20164 33 49.5 10.9 36.1 26143 57 37.8 28.2 8.9 21908 38 44.4 22.4 28.9 19044 72 66.6 8 21 15686 37 45.5 3.1 40.8 21658 47 58 11.3 15.6 20685 43 77.3 16.9 2.9 20423 37 71.5 2.6 18.7 29741 75 42.2 20.6 24.9 15252 21 36.9 43.4 1.5 12961 20 5.7 92.4 1.3 11576 29 49.4 40 4.7 31524 37 77.4 14.6 7.6 26566 41 68.7 5.5 5.6 16745 22 1.8 79.2 20.8 18371 23 7.5 92.5 0 15871 64 49.8 43.7 3.5 11656 34 1 98.6 0.2 36216 49 89.4 2.2 7.5 17367 32 5.7 0 80.8 14989 25 19.9 0.6 79 36394 39 88 2.6 4 18999 53 68.7 5.3 18.1 26934 27 72.1 0.9 19.5 17632 36 48.3 8.1 38.4 15498 20 41.3 0.6 55.3 27719 39 78.5 0.4 20.9 35028 19 90.7 1.3 5.5 19921 34 56.9 28.6 8.5 22213 36 72.2 25.9 0.3 25110 34 72.5 0 1.1 27347 31 89.5 0 8.4 35316 45 77.5 3.1 15 26597 34 74.8 8.8 4.2 29695 31 25 31.9 40.1 16940 28 0.3 37 60.3 52896 57 78.5 6.5 4.8 27535 29 87.4 10 0 14963 50 15.8 65.2 18.6 54494 40 89.2 0.9 6 31761 35 94.8 0 2.5 42312 53 84.7 1.4 7.7 31470 59 64.3 16.9 4.9 25262 18 40.9 53.8 0 18470 28 74.5 0.4 20.2 21175 52 83.2 10.1 0.3 26971 38 21.7 24.9 37.1 33590 48 23.1 4 12.3
Names of X columns:
PrInc Age Sh_White Sh_Black Sh_Hisp
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
par6 <- '12' par5 <- '1' par4 <- '1' par3 <- 'No Linear Trend' par2 <- 'Do not include Seasonal Dummies' par1 <- '1' library(lattice) library(lmtest) library(car) library(MASS) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test mywarning <- '' par6 <- as.numeric(par6) if(is.na(par6)) { par6 <- 12 mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.' } par1 <- as.numeric(par1) if(is.na(par1)) { par1 <- 1 mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.' } if (par4=='') par4 <- 0 par4 <- as.numeric(par4) if (!is.numeric(par4)) par4 <- 0 if (par5=='') par5 <- 0 par5 <- as.numeric(par5) if (!is.numeric(par5)) par5 <- 0 x <- na.omit(t(y)) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par3 == 'Seasonal Differences (s)'){ (n <- n - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s)'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 (n <- n - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,j] - x[i,j] } } x <- x2 } if(par4 > 0) { x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep=''))) for (i in 1:(n-par4)) { for (j in 1:par4) { x2[i,j] <- x[i+par4-j,par1] } } x <- cbind(x[(par4+1):n,], x2) n <- n - par4 } if(par5 > 0) { x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*par6)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*par6-j*par6,par1] } } x <- cbind(x[(par5*par6+1):n,], x2) n <- n - par5*par6 } if (par2 == 'Include Seasonal Dummies'){ x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep =''))) for (i in 1:(par6-1)){ x2[seq(i,n,par6),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } (k <- length(x[n,])) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } print(x) (k <- length(x[n,])) head(x) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') sresid <- studres(mylm) hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals') xfit<-seq(min(sresid),max(sresid),length=40) yfit<-dnorm(xfit) lines(xfit, yfit) grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqPlot(mylm, main='QQ Plot') grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) print(z) plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, mywarning) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') myr <- as.numeric(mysum$resid) myr a <-table.start() a <- table.row.start(a) a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Description',1,TRUE) a <- table.element(a,'Link',1,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Histogram',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'QQ Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Kernel Density Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Spectral Analysis',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Summary Statistics',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a<-table.end(a) table.save(a,file='mytable7.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant1,6)) a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' ')) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') } } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_fitted <- resettest(mylm,power=2:3,type='fitted') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_fitted'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_regressors <- resettest(mylm,power=2:3,type='regressor') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_regressors'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_principal_components'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable8.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) vif <- vif(mylm) a<-table.element(a,paste('<pre>',RC.texteval('vif'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable9.tab')
Compute
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R Server
Big Analytics Cloud Computing Center
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