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Data X:
2.75 0.06455399 1 102750 2.73 0.06363636 1 95276 2.82 0.06512702 1 112053 2.83 0.06490826 1 98841 2.9 0.06605923 1 123102 3.05 0.06900452 1 118152 3.15 0.07110609 1 101752 3.26 0.07228381 1 148219 3.38 0.07477876 1 124966 3.54 0.07763158 1 134741 3.81 0.08300654 1 132168 5.27 0.11406926 1 100950 6.71 0.14399142 1 96418 9.09 0.19258475 1 86891 11.08 0.23179916 1 89796 11.91 0.248125 1 119663 11.81 0.24300412 1 130539 11.81 0.24102041 1 120851 12.09 0.24473684 1 145422 11.95 0.239 1 150583 11.67 0.23063241 1 127054 11.6 0.22700587 1 137473 11.71 0.22737864 1 127094 11.62 0.2238921 1 132080 11.64 0.22341651 1 188311 11.66 0.22209524 1 107487 11.67 0.22144213 1 84669 11.69 0.22098299 1 149184 11.58 0.21766917 1 121026 11.4 0.21268657 1 81073 11.44 0.21107011 1 132947 11.38 0.20957643 1 141294 11.31 0.20714286 1 155077 11.45 0.20856102 1 145154 11.73 0.21211573 1 127094 12.11 0.2181982 1 151414 12.23 0.21996403 1 167858 12.39 0.22204301 1 127070 12.34 0.22075134 1 154692 12.42 0.22139037 1 170905 12.37 0.21893805 1 127751 12.37 0.21778169 1 173795 12.39 0.21698774 1 190181 12.43 0.21655052 1 198417 12.48 0.21666667 1 183018 12.45 0.21502591 1 171608 12.58 0.21689655 1 188087 12.59 0.21632302 1 197042 12.54 0.21435897 1 208788 13.01 0.22013536 1 178111 13.31 0.22369748 1 236455 13.45 0.22416667 1 233219 13.28 0.22023217 1 188106 13.38 0.22042834 1 238876 13.36 0.21901639 1 205148 13.4 0.21895425 1 214727 13.49 0.21970684 1 213428 13.47 0.21866883 1 195128 13.62 0.22003231 1 206047 13.57 0.21851852 1 201773 13.59 0.21744 1 192772 13.48 0.21430843 1 198230 13.47 0.21246057 1 181172 13.47 0.21079812 1 189079 13.36 0.20713178 1 179073 13.37 0.20506135 1 197421 13.4 0.20395738 1 195244 13.41 0.20318182 1 219826 13.37 0.20105263 1 211793 13.42 0.2 1 203394 13.41 0.19896142 1 209578 13.46 0.19881832 1 214769 13.64 0.19970717 1 226177 13.93 0.2015919 1 191449 14.46 0.20716332 1 200989 14.92 0.21133144 1 216707 16.27 0.22755245 1 192882 17.36 0.24011065 1 199736 19.07 0.26087551 1 202349 21.1 0.28590786 1 204137 22.39 0.30013405 1 215588 23.13 0.30757979 1 229454 23.27 0.30658762 1 175048 24.57 0.32033898 1 212799 26.32 0.33830334 1 181727 28.57 0.36210393 1 211607 30.44 0.38002497 1 185853 31.4 0.38765432 1 158277 31.84 0.38924205 1 180695 31.86 0.38524788 1 175959 32.3 0.39056832 1 139550 32.93 0.39531813 1 155810 32.73 0.38964286 1 138305 33.1 0.39033019 1 147014 33.23 0.38865497 1 135994 33.94 0.39327926 1 166455 34.27 0.39390805 1 177737 35.96 0.40910125 1 167021 36.25 0.40960452 1 132134 36.92 0.41436588 1 169834 36.16 0.40267261 1 130599 36.59 0.40386313 1 156836 35.05 0.38264192 1 119749 34.53 0.37410618 1 148996 34.07 0.36555794 1 147491 33.65 0.36027837 1 147216 33.84 0.36115261 1 153455 33.99 0.36159574 1 112004 35.41 0.37550371 1 158512 35.53 0.3755814 1 104139 34.71 0.36730159 1 102536 33.2 0.34984194 1 93017 32.25 0.33663883 1 91988 32.92 0.33938144 1 123616 33.27 0.34123077 1 134498 32.91 0.33684749 1 149812 32.39 0.3308478 1 110334 32.44 0.33034623 1 136639 32.84 0.33510204 1 102712 32.44 0.33237705 1 112951 32.5 0.33231084 1 107897 31.12 0.31787538 1 73242 30.28 0.3092952 1 72800 28.76 0.29168357 1 78767 28.59 0.28820565 1 114791 28.83 0.28974874 1 109351 28.93 0.28958959 1 122520 29.31 0.29251497 1 137338 29.27 0.29066534 1 132061 29.36 0.29069307 1 130607 29.05 0.28705534 1 118570 29 0.28627838 1 95873 27.65 0.27134446 1 103116 27.64 0.26992187 1 98619 27.8 0.27095517 1 104178 27.84 0.2700291 1 123468 27.85 0.26934236 1 99651 27.76 0.26769527 1 120264 28.05 0.26945245 1 122795 27.66 0.264689 1 108524 27.39 0.26085714 1 105760 27.56 0.2617284 1 117191 27.55 0.26163343 1 122882 27.3 0.25925926 1 93275 27.38 0.25952607 1 99842 26.91 0.25386792 1 83803 26.05 0.24483083 1 61132 26.52 0.24808232 1 118563 26.79 0.24967381 1 106993 26.52 0.2464684 1 118108 25.91 0.2403525 1 99017 25.76 0.23851852 1 99852 25.42 0.23471837 1 112720 25.65 0.23597056 1 113636 25.69 0.23568807 1 118220 26.04 0.23824337 1 128854 25.8 0.23540146 1 123898 23.13 0.2116194 1 100823 18.1 0.16636029 1 115107 12.78 0.11767956 1 90624 12.24 0.11239669 0 132001 12.04 0.10995434 0 157969 11.03 0.10073059 0 169333 10.09 0.09197812 0 144907 11.08 0.10054446 0 169346 11.79 0.1068903 0 144666 12.23 0.11077899 0 158829 12.4 0.11221719 0 127286 13.86 0.12464029 0 120578 15.47 0.13862007 0 129293 15.87 0.14157003 0 122371 16.57 0.14702751 0 115176 16.92 0.14960212 0 142168 17.31 0.15251101 0 153260 17.77 0.15615114 0 173906 18.07 0.15795455 0 178446 17.49 0.15208696 0 155962 17.21 0.14926279 0 168257 17.12 0.14835355 0 149456 16.46 0.14263432 0 136105 22.4 0.19360415 0 141507 15.2 0.13103448 0 152084 14.24 0.12223176 0 145138 14.21 0.12134927 0 146548 14.69 0.12502128 0 173098 14.68 0.12440678 0 165471 14.02 0.11831224 0 152271 13.38 0.11243697 0 163201 13.08 0.10918197 0 157823 11.92 0.09916805 0 166167 11.52 0.0957606 0 154253 12.34 0.10240664 0 170299 13.91 0.11486375 0 166388 14.84 0.12203947 0 141051 15.54 0.1270646 0 160254 17.33 0.14077985 0 164995 17.97 0.14515347 0 195971 17.27 0.13916197 0 182635 16.93 0.13609325 0 189829 15.95 0.12800963 0 209476 16.14 0.12912 0 189848 16.61 0.13224522 0 183746 17.08 0.13566322 0 192682 17.72 0.14052339 0 169677 18.85 0.14795918 0 201823 18.79 0.14679687 0 172643 17.75 0.13791764 0 202931 16.02 0.12428239 0 175863 14.61 0.1130805 0 222061 13.83 0.10646651 0 199797 13.92 0.10674847 0 214638 19.57 0.14870821 0 200106 25.63 0.19314243 0 166077 30.08 0.22531835 0 160586 29.51 0.22055306 0 158330 25.75 0.19245142 0 141749 22.98 0.17072808 0 170795 18.39 0.13642433 0 153286 16.75 0.12407407 0 163426 16.39 0.12122781 0 172562 16.57 0.12219764 0 197474 16.4 0.12058824 0 189822 16.15 0.11857562 0 188511 16.8 0.12298682 0 207437 17.14 0.12492711 0 192128 17.97 0.13078603 0 175716 18.06 0.13105951 0 159108 16.6 0.12037708 0 175801 14.87 0.1076756 0 186723 14.42 0.1040404 0 154970 14.48 0.10394831 0 172446 15.5 0.11111111 0 185965 16.74 0.1198282 0 195525 18.27 0.13031384 0 193156 18.2 0.12953737 0 212705 18.03 0.12796309 0 201357 17.86 0.12639774 0 189971 18.22 0.12849083 0 216523 17.63 0.12415493 0 193233 16.22 0.11430585 0 191996 15.5 0.10869565 0 211974 15.71 0.10978337 0 175907 16.49 0.11483287 0 206109 16.69 0.11590278 0 220275 16.71 0.11588072 0 211342 16.07 0.11128809 0 222528 14.96 0.10360111 0 229523 14.51 0.10020718 0 204153 14.37 0.09903515 0 206735 14.59 0.10013727 0 223416 13.72 0.09410151 0 228292 12.2 0.08367627 0 203121 11.64 0.07961696 0 205957 12.09 0.08241309 0 176918 11.76 0.0798913 0 219839 12.85 0.08717775 0 217213 14.05 0.09525424 0 216618 15.18 0.10256757 0 248057 16.09 0.10842318 0 245642 15.97 0.10718121 0 242485 15 0.10040161 0 260423 14.8 0.09899666 0 221030 15.31 0.10227121 0 229157 14.7 0.09819639 0 220858 15.06 0.1001996 0 212270 15.53 0.10291584 0 195944 15.78 0.10422721 0 239741 16.76 0.11033575 0 212013 17.4 0.11432326 0 240514 16.78 0.11003279 0 241982 15.51 0.10170492 0 245447 15.22 0.09954218 0 240839 15.44 0.10078329 0 244875 15.25 0.09921926 0 226375 15.1 0.09830729 0 231567 15.82 0.10306189 0 235746 16.43 0.10641192 0 238990 16.1 0.10393802 0 198120 17.31 0.11117534 0 201663 19.27 0.12328855 0 238198 18.9 0.12068966 0 261641 17.96 0.11461391 0 253014 18.16 0.11566879 0 275225 18.65 0.11856325 0 250957 19.97 0.1265526 0 260375 21.41 0.13524953 0 250694 21.38 0.13480454 0 216953 21.63 0.13638083 0 247816 21.86 0.13739786 0 224135 20.48 0.1283208 0 211073 18.76 0.11725 0 245623 17.13 0.10692884 0 250947 17.06 0.1065584 0 278223 16.85 0.10511541 0 254232 16.41 0.10224299 0 266293 16.95 0.10541045 0 280897 16.73 0.10378412 0 274565 17.71 0.10959158 0 280555 17.25 0.10681115 0 252757 16.05 0.09950403 0 250131 14.31 0.08855198 0 271208 13.02 0.08042001 0 230593 11.88 0.07324291 0 263407 11.77 0.07243077 0 289968 11.8 0.07248157 0 282846 11.12 0.06822086 0 271314 10.78 0.06605392 0 289718 10.55 0.06456548 0 300227 10.99 0.06717604 0 259951 11.66 0.07109756 0 263149 10.79 0.06579268 0 267953 9.38 0.05723002 0 252378 9.21 0.056056 0 280356 9.48 0.05762918 0 234298 10.5 0.06363636 0 271574 12.88 0.07749699 0 262378 14.6 0.08784597 0 289457 14.52 0.08736462 0 278274 16.11 0.09664067 0 288932 17.88 0.1070018 0 283813 19.69 0.11727219 0 267600 20.76 0.12342449 0 267574 21.05 0.12507427 0 254862 22.79 0.13541295 0 248974 23.31 0.13809242 0 256840 25.14 0.14805654 0 250914 26.41 0.15426402 0 279334 24.41 0.14249854 0 286549 24.28 0.14157434 0 302266 26.78 0.15533643 0 298205 27.73 0.16047454 0 300843 26.59 0.15387731 0 312955 29.03 0.16712723 0 275962 28.57 0.1641954 0 299561 28.34 0.16278001 0 260975 26.4 0.15172414 0 274836 23.19 0.13243861 0 284112 23.85 0.13566553 0 247331 22.75 0.12911464 0 298120 21.66 0.12244206 0 306008 22.65 0.12746201 0 306813 23.09 0.1297191 0 288550 22.33 0.12580282 0 301636 22.14 0.12473239 0 293215 23.02 0.12910824 0 270713 19.88 0.11187394 0 311803 17 0.09582864 0 281316 15.46 0.08749293 0 281450 16.29 0.09198193 0 295494 16.58 0.09325084 0 246411 19.27 0.10777405 0 267037 22.53 0.1253059 0 296134 23.75 0.13209121 0 296505 23.35 0.12979433 0 270677 23.73 0.13176013 0 290855 24.58 0.13602656 0 296068 25.49 0.14082873 0 272653 26.25 0.14478764 0 315720 24.19 0.13342526 0 286298 24.15 0.13349917 0 284170 27.76 0.15277931 0 273338 30.37 0.16586565 0 250262 30.39 0.16498371 0 294768 26.01 0.14151251 0 318088 24.05 0.13106267 0 319111 25.5 0.13881328 0 312982 26.75 0.14545949 0 335511 27.56 0.14929577 0 319674 26.43 0.14271058 0 316796 26.28 0.14205405 0 329992 26.54 0.14384824 0 291352 27.17 0.14742268 0 314131 28.57 0.15426566 0 309876 29.17 0.15665951 0 288494 30.66 0.16360726 0 329991 31 0.16489362 0 311663 33.14 0.17525119 0 317854 33.74 0.17785978 0 344729 33.38 0.17624076 0 324108 36.54 0.19282322 0 333756 37.52 0.19757767 0 297013 41.84 0.21917234 0 313249 41.19 0.21565445 0 329660 36.46 0.19159222 0 320586 35.27 0.18495018 0 325786 36.93 0.19254432 0 293425 41.28 0.21355406 0 324180 44.78 0.23011305 0 315528 43.04 0.22139918 0 319982 44.41 0.22832905 0 327865 49.07 0.2511259 0 312106 52.85 0.26909369 0 329039 57.42 0.288833 0 277589 56.21 0.28217871 0 300884 52.16 0.26396761 0 314028 49.79 0.25299797 0 314259 51.8 0.26122037 0 303472 53.86 0.2710619 0 290744 52.32 0.26186186 0 313340 56.65 0.28114144 0 294281 62.04 0.30637037 0 325796 62.12 0.30616067 0 329839 64.93 0.31906634 0 322588 66.13 0.32432565 0 336528 62.4 0.30754066 0 316381 55.47 0.27487611 0 308602 52.22 0.25915633 0 299010 53.84 0.26679881 0 293645 52.23 0.25805336 0 320108 50.71 0.24918919 0 252869 53 0.25803311 0 324248 57.28 0.27711659 0 304775 59.36 0.28552189 0 320208 60.95 0.29246641 0 321260 65.56 0.31473836 0 310320 68.21 0.32809043 0 319197 68.51 0.32858513 0 297503 72.49 0.34700814 0 316184 79.65 0.37892483 0 303411 82.76 0.39409524 0 300841
Names of X columns:
unit_price defl_price dum barrels_purchased
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
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
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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