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Data X:
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NA 0 0 0 NA 3 0 0 22000 6 3 1 20000 4 0 0 NA 4 2 0 64928 30 9 1 42150 4 6 1 15542 4 1 1 NA 7 9 1 69030 7 7 1 NA 5 5 1 NA 4 8 1 NA 0 0 1 174330 3 0 2 63119 2 1 2 NA 8 1 1 NA 3 5 1 40000 20 0 0 26073 5 5 1 NA 8 0 1 NA 8 0 1 25754 7 3 1 40021 0 1 0 NA 4 1 0 NA 5 0 0 100000 45 0 0 NA 59 0 0 18000 24 3 1 NA 0 0 0 40156 5 9 1 21405 63 0 0 NA 0 0 0 19024 0 2 0 NA 2 0 0 NA 0 0 0 232617 9 4 1 407464 0 0 0 NA 6 4 1 34500 150 0 0 200485 10 6 1 20000 2 0 0 50000 5 3 1 NA 10 0 0 52400 8 4 1 NA 0 0 1 NA 48 1 1 NA 50 0 0 30705 10 3 1 NA 0 1 1 125670 10 1 0 NA 0 0 0 51062 8 9 1 22200 0 4 1 18400 9 4 1 NA 6 3 2 NA 0 3 2 NA 5 1 1 NA 4 1 1 NA 5 0 2 297986 8 7 1 NA 0 0 1 NA 0 0 1 29000 8 3 1 16664 6 2 1 NA 2 4 1 148245 2 3 1 18000 0 2 1 17563 35 0 0 29213 0 0 2 NA 4 1 2 20062 6 6 1 NA 4 3 1 NA 10 1 1 NA 5 5 1 16300 6 0 0 NA 0 0 0 NA 7 0 0 NA 3 0 0 361200 5 10 1 59000 12 0 0 45740 5 11 1 NA 0 0 1 253875 10 7 1 33243 39 0 0 NA 1 6 1 NA 9 4 1 29200 1 4 1 39215 0 0 1 26338 4 2 1 NA 30 1 1 NA 9 0 1 NA 30 2 1 NA 4 2 1 61288 4 4 1 48000 50 0 0 21686 5 2 1 134897 1 10 1 NA 6 0 1 NA 4 1 1 58960 4 6 1 32000 4 1 1 NA 2 3 1 NA 4 0 0 NA 12 1 0 2595780 25 20 1 NA 24 3 1 NA 4 3 1 NA 8 8 1 NA 13 1 1 16225 10 2 1 NA 0 0 1 NA 3 1 1 NA 0 0 0 NA 5 0 0 NA 30 1 1 80000 2 16 1 NA 4 0 0 25758 7 3 1 819118 28 19 1 NA 0 0 1 NA 25 3 1 45000 0 16 1 60000 13 1 1 NA 5 1 1 50000 10 8 1 45602 4 14 1 2485296 49 40 1 383552 48 23 1 NA 25 0 0 NA 0 0 0 17241 50 0 0 NA 0 0 0 NA 7 2 1 25848 12 3 1 2412174 26 7 2 26061 8 5 2 24047 66 0 0 NA 0 0 0 NA 4 2 0 NA 3 0 0 30000 7 0 0 19946 78 0 0 47103 6 4 1 NA 9 0 1 NA 0 0 1 NA 7 11 1 104533 5 3 1 NA 4 3 1 NA 8 1 1 NA 3 1 1 NA 10 1 1 NA 9 7 1 1342565 54 9 1 61846 9 4 1 57766 7 8 1 15892 5 4 1 75045 6 1 1 NA 0 0 1 NA 8 2 1 NA 10 0 2 NA 10 0 2 NA 5 1 0 NA 8 1 0 17506 8 3 1 156000 25 6 1 NA 0 1 2 NA 5 0 2 21800 10 2 1 NA 7 0 0 NA 7 0 0 NA 7 2 1 NA 8 1 1 44500 8 6 1 NA 2 3 1 100491 15 0 0 23198 9 3 1 NA 7 3 1 NA 2 0 0 NA 4 1 1 NA 4 5 1 NA 0 0 1 NA 4 4 1 NA 5 1 1 135638 47 5 1 67068 8 14 1 42118 4 3 2 NA 0 0 2 NA 0 1 2 NA 4 0 2 NA 2 0 0 NA 0 0 0 NA 2 0 1 NA 8 0 1 NA 3 3 1 NA 3 0 1 37279 6 8 1 100000 38 0 0 39000 8 5 1 662076 25 13 1 35081 7 2 1 22253 2 0 0 NA 0 0 0 30000 2 0 0 23152 5 5 1 27205 7 0 0 NA 0 0 0 NA 18 0 0 15815 5 0 0 NA 2 2 1 123913 10 3 1 30000 48 0 0 40578 7 0 2 NA 4 0 2 42870 3 2 2 NA 5 2 2 22068 6 2 1 124293 1 4 2 42308 4 2 2 NA 4 4 1 NA 0 0 1 NA 0 0 1 NA 2 4 1 500000 6 1 1 17101 7 4 1 NA 6 0 2 NA 0 0 2 31000 5 0 1 NA 45 0 0 40291 77 0 0 NA 0 0 0 NA 6 3 1 20744 50 0 0 30000 3 0 1 NA 10 4 1 NA 5 2 1 42793 6 3 1 NA 0 0 2 NA 4 2 1 NA 5 3 2 NA 5 0 2 71300 38 2 1 40000 5 1 2 21369 3 0 2 NA 5 8 1 NA 7 2 1 105000 5 0 1 NA 29 0 0 NA 10 1 1 178591 1 9 1 NA 4 4 1 NA 8 2 1 NA 15 2 1 230874 11 0 1 55399 4 0 1 NA 13 4 1 NA 0 0 1 NA 20 0 0 NA 10 1 1 NA 35 0 0 NA 66 0 0 NA 5 2 1 NA 6 6 1 NA 0 0 1 1181910 14 9 2 454735 29 5 2 NA 2 1 0 NA 4 0 0 NA 5 5 1 111335 27 0 0 NA 11 2 1 NA 0 0 1 59388 10 5 1 16092 7 7 1 38569 10 5 1 52198 7 4 1 20372 9 0 1 NA 5 1 1 NA 0 1 0 NA 5 0 0 15663 55 0 0 24654 4 2 1 70000 5 5 1 NA 6 5 1 NA 9 11 1 464370 52 25 0 NA 3 1 1 88907 8 4 1 28100 9 2 1 86872 0 1 1 17644 6 3 1 18096 19 2 1 190000 10 8 1 NA 17 0 0 42548 16 3 1 79338 13 8 1 36000 40 0 0 20000 8 0 0 NA 0 0 0 NA 4 0 0 NA 0 0 0 NA 8 0 0 NA 3 0 0 NA 5 3 1 18415 37 0 0 35000 56 0 0 NA 21 2 1 62000 0 13 1 NA 0 0 0 NA 1 0 0 40447 8 5 1 NA 42 0 0 28200 7 6 1 43109 5 0 0 126845 0 3 0 59024 6 2 0 61463 8 1 1 NA 1 1 1 118000 1 0 1 20000 4 0 1 20255 23 0 0 NA 71 0 0 NA 0 0 0 84280 6 0 1 52184 40 5 1 1402005 28 23 1 NA 5 0 2 NA 5 3 2 30000 6 5 1 NA 0 0 1 NA 0 0 1 NA 1 0 0 NA 4 0 0 520905 9 12 1 16549 8 1 1 59801 4 3 1 NA 0 2 1 NA 79 0 0 NA 0 0 0 NA 22 1 1 NA 5 0 0 NA 4 2 1 180920 6 9 1 NA 16 0 1 NA 5 17 1 NA 0 0 1 NA 125 0 0 NA 7 4 1 NA 48 2 1 NA 0 0 1 26613 3 5 1 NA 14 0 2 NA 0 2 2 NA 8 3 1 NA 3 0 0 NA 4 1 0 NA 4 2 1 NA 5 2 1 115000 2 6 0 17000 110 0 0 NA 40 0 0 NA 0 0 0 NA 5 0 0 NA 1 0 0 NA 1 14 1 NA 28 1 1 NA 45 0 0 NA 4 2 1 881978 31 19 1 NA 10 1 1 15725 6 4 1 NA 2 0 0 30288 5 2 1 NA 9 2 1 NA 0 0 1 70000 46 2 1 17488 3 3 2 NA 0 2 2 NA 0 0 0 NA 4 0 0 116400 2 0 0 NA 4 2 1 70127 0 0 1 NA 57 2 1 NA 4 3 1 NA 4 2 1 16000 35 0 0 20983 60 0 0 NA 0 0 0 33000 2 1 0 76000 5 6 1 NA 0 0 1 22817 40 0 0 18464 3 0 1 15722 3 1 1 NA 7 2 1 NA 8 5 1 17000 60 0 0 NA 25 1 1 40000 0 0 0 NA 0 0 0 31000 4 3 1 NA 30 0 0 NA 0 0 0 NA 5 4 1 465923 18 4 0 50000 6 14 1 NA 0 0 1 NA 12 2 0 48514 5 8 1 24112 4 4 1 NA 7 0 0 351231 19 8 1 NA 67 0 0 NA 69 0 0 NA 0 0 0 NA 2 1 0 NA 2 0 0 26877 3 4 1 NA 50 0 0 NA 9 6 1 NA 0 0 0 NA 4 0 0 36000 58 0 0 19953 0 0 0 NA 2 0 0 50918 18 10 1 30602 9 9 1 30350 5 0 1 NA 2 1 1 2283887 53 40 1 NA 5 5 1 NA 0 0 1 63537 5 5 1 NA 4 3 1 NA 0 0 1 65000 4 1 2 NA 0 0 2 82892 11 7 1 90389 0 11 1 NA 49 1 1 NA 13 2 1 NA 0 0 1 59000 10 3 1 16800 6 4 1 NA 0 0 1 202000 32 13 1 NA 0 0 1 20000 10 0 0 NA 2 0 0 16958 13 0 0 17304 1 0 0 NA 9 6 1 NA 45 0 0 18950 3 2 1 175496 6 4 0 NA 6 1 0 20000 5 0 0 36000 2 4 1 NA 5 1 1 26460 9 1 1 NA 0 0 1 NA 4 1 0 20112 10 0 0 NA 5 0 0 NA 10 0 0 20000 3 4 1 17800 1 4 1 NA 0 3 1 NA 6 1 0 NA 0 1 0 95149 5 4 1 44211 3 2 1 NA 6 3 1 44805 5 0 1 NA 0 0 1 71136 7 0 2 43217 3 1 2 23100 10 9 1 17500 40 0 0 NA 5 3 1 45970 25 5 1 60100 8 0 1 NA 7 0 1 NA 8 1 1 60000 8 1 1 18150 7 2 0 NA 4 2 0 49770 5 16 1 20000 7 2 1 74658 9 3 1 NA 0 0 1 145420 47 16 1 NA 5 5 1 60000 20 1 0 43540 3 5 1 1617133 34 24 1 NA 5 1 1 17522 4 0 0 NA 0 0 0 NA 48 0 0 NA 0 1 1 NA 5 0 1 19900 5 2 1 17000 17 4 1 NA 4 2 1 NA 0 0 1 21341 4 6 1 NA 5 1 1 NA 3 0 2 NA 0 0 2 NA 5 3 1 85546 7 2 1 23695 36 0 0 75000 6 0 0 35000 0 0 0 NA 3 0 0 NA 5 1 0 NA 5 1 0 NA 9 1 1 77382 5 2 1 NA 0 0 1 NA 53 0 0 72946 4 4 1 365332 12 4 2 NA 5 0 2 371977 33 18 1 NA 10 3 1 17929 75 0 0 16390 75 0 0 17929 75 0 0 100000 0 6 1 NA 4 4 1 61969 4 0 2 15578 1 0 2 20694 5 0 0 NA 3 0 0 NA 3 6 1 NA 0 0 1 18217 4 0 1 NA 4 1 1 44138 4 1 0 NA 0 0 0 643434 4 14 1 26650 44 7 1 NA 2 0 0 NA 48 0 0 350000 28 0 2 254069 14 7 2 18000 0 0 0 NA 2 0 0 119112 10 4 1 82000 0 1 1 NA 37 0 0 NA 5 1 1 25134 2 1 1 60000 1 0 0 NA 2 0 0 45000 7 5 1 21900 0 0 0 NA 0 0 0 35000 7 6 1 NA 0 0 1 NA 5 0 0 NA 0 0 0 27433 6 3 1 NA 0 0 1 NA 30 0 0 NA 5 4 1 30305 10 0 2 27082 0 2 2 NA 5 0 0 NA 5 0 0 16681 7 0 0 NA 0 0 0 NA 3 2 1 NA 0 0 1 NA 30 0 0 NA 0 0 0 NA 3 4 1 NA 7 10 1 40000 0 0 0 NA 10 0 0 27200 5 10 1 NA 4 2 1 NA 0 0 1 NA 4 0 1 NA 5 3 1 NA 0 0 1 25000 0 0 2 NA 16 0 2 NA 0 0 1 NA 6 5 1 NA 7 5 1 NA 4 5 1 NA 0 0 1 NA 3 0 1 16370 4 0 2 NA 0 0 2 21350 3 4 1 NA 0 0 1 NA 3 1 1 110000 6 4 1 61756 22 10 1 65895 8 2 1 72705 5 18 1 NA 3 3 1 NA 0 0 0 17200 6 3 1 95612 4 8 1 NA 4 0 0 NA 7 6 1 NA 7 0 2 NA 5 0 2 433698 41 26 1 NA 4 4 1 NA 0 0 1 40350 8 0 1 38581 3 1 1 20000 3 1 1 30000 10 0 0 NA 10 4 1 NA 5 3 1 NA 1 4 1 NA 4 1 1 78226 50 0 0 NA 0 0 0 36348 11 0 0 NA 43 0 0 NA 20 1 1 NA 3 0 1 103700 3 6 1 58231 4 16 1 35500 2 0 0 NA 5 0 0 225000 23 9 1 NA 6 5 1 NA 5 0 1 NA 43 0 0 NA 5 1 1 NA 7 6 1 2050800 48 38 1 NA 4 0 2 NA 3 1 2 50000 75 0 0 NA 40 0 0 65000 10 1 1 41620 8 10 1 500000 34 8 1 NA 0 1 1 68598 7 11 1 346079 23 12 1 33532 9 6 1 107585 5 4 1 NA 0 0 1 NA 0 0 0 266050 14 11 1 28000 9 0 0 15500 7 0 0 NA 5 5 1 NA 3 3 1 102265 52 19 0 23301 9 0 0 NA 0 0 0 110000 43 5 1 NA 7 1 2 NA 0 0 2 NA 6 0 0 1835742 41 50 1 26400 9 6 1 NA 1 0 1 17000 135 0 0 24500 10 1 0 24500 0 1 0 NA 45 0 0 67150 25 6 1 52884 5 14 1 NA 4 4 1 NA 4 1 1 25000 10 7 1 NA 0 0 1 15568 5 5 1 NA 0 0 1 NA 5 1 1 30560 20 0 0 NA 0 0 0 40352 60 0 0 NA 0 0 0 NA 30 0 0 NA 0 0 0 30000 50 0 0 NA 0 0 0 NA 42 1 1 500000 42 1 1 NA 22 0 2 NA 12 0 2 NA 0 0 0 32399 9 11 1 41145 7 8 1 301540 20 4 1 NA 3 5 1 41300 9 4 1 NA 0 0 1 NA 4 6 1 NA 5 1 1 NA 10 4 1 NA 79 0 0 NA 30 0 0 NA 60 0 0 NA 0 0 0 NA 19 0 0 NA 0 0 0 NA 40 1 1 NA 8 1 1 NA 0 1 0 NA 7 0 0 NA 7 0 0 96303 15 0 0 17583 2 7 1 25000 10 0 0 NA 0 0 0 NA 50 0 0 28335 1 1 0 NA 1 1 0 84143 50 2 0 35958 2 0 1 15759 0 0 2 NA 40 0 2 171192 4 5 0 43556 8 1 0 48336 15 3 1 NA 0 0 0 NA 0 0 0 NA 10 2 0 19000 75 0 0 47360 6 0 2 NA 5 1 1 26864 2 1 2 16476 2 2 2 17750 7 1 0 42000 9 4 1 NA 0 0 1 NA 4 8 1 NA 3 0 1 171000 21 3 1 NA 0 0 1 18000 35 0 0 19000 25 0 0 NA 0 0 0 41626 60 0 0 NA 52 0 0 NA 5 0 2 NA 5 2 2 60028 4 0 2 NA 0 0 2 NA 125 0 0 NA 20 0 0 NA 0 0 0 NA 4 19 1 NA 0 0 1 NA 5 3 1 35111 45 0 0 NA 5 2 1 20647 3 1 0 NA 0 2 0 219988 18 9 1 29072 40 0 0 38000 32 0 0 42997 7 3 1 NA 0 0 1 NA 66 0 0 NA 8 2 2 NA 0 0 2 NA 24 1 1 NA 8 1 1 NA 26 0 0 895947 27 21 1 NA 28 0 0 120400 11 5 1 NA 5 0 0 15767 9 0 1 68435 9 8 1 95000 5 2 1 NA 20 0 0 NA 0 0 0 20331 7 12 1 NA 6 3 1 NA 4 0 1 84035 10 5 1 32000 8 5 1 31038 8 5 1 NA 0 1 1 119000 26 9 1 23316 4 6 1 NA 4 6 1 506000 0 0 0 NA 0 0 0 NA 7 4 1 524656 40 17 1 NA 5 0 2 NA 0 0 2 NA 5 2 1 16355 41 1 1 NA 54 0 0 NA 0 0 0 52947 3 4 1 17000 3 3 1 15357 48 0 0 NA 0 0 0 25000 34 0 0 16714 10 7 1 82995 3 2 1 NA 0 0 1 361100 10 7 1 22336 0 0 2 NA 8 0 2 81582 5 1 0 NA 3 1 0 19250 0 0 0 NA 5 0 0 24725 8 1 2 NA 17 0 2 117851 9 8 1 NA 0 0 1 23361 8 8 1 524112 21 13 1 62599 8 4 1 20000 9 7 1 NA 21 1 1 15230 13 9 1 29505 0 0 0 NA 8 0 0 32000 5 2 1 NA 0 1 1 17000 3 5 1 170963 14 2 0 NA 0 0 0 NA 7 2 1 NA 8 2 1 NA 40 0 0 20148 8 2 2 15790 6 4 2 102565 10 5 1 36000 25 0 0 18000 1 5 1 95000 44 0 0 NA 2 3 1 30000 60 0 0 NA 2 1 1 25572 3 3 1 130746 21 11 1 NA 7 1 1 30787 4 3 1 49000 0 5 1 994355 20 14 1 NA 0 0 1 NA 60 1 1 NA 4 1 1 NA 70 0 0 NA 0 0 0 NA 6 5 1 NA 0 0 1 NA 10 1 1 2671079 48 59 1 81829 2 2 2 20792 5 2 2 100000 14 10 1 NA 2 8 1 50000 3 5 1 20949 8 7 1 NA 3 0 1 NA 0 0 0 215000 7 0 1 24736 5 2 1 NA 5 1 1 NA 0 0 1 21913 8 4 1 15586 10 2 1 NA 10 0 0 NA 2 0 0 NA 0 0 0 NA 2 1 0 NA 8 0 1 45000 5 3 1 135200 9 11 1 55656 7 4 1 35000 2 2 1 NA 10 3 1 17237 0 0 2 NA 6 1 2 21502 4 3 1 23000 10 0 0 66607 8 7 1 27450 4 0 0 NA 4 0 0 51052 4 0 1 NA 18 0 0 142069 24 16 1 115000 39 0 0
Names of X columns:
EqpDamgadap Speed CarsDer AccTypeDummy
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
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
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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