Wessa.net

Send output to:

Data X:
1801 159261 91 586 111 0 74 1717 189672 59 520 76 1 80 192 7215 18 72 1 0 0 2295 129098 95 645 155 0 84 3450 230632 136 1163 125 0 124 6861 515038 263 1945 278 1 140 1795 180745 56 585 89 1 88 1681 185559 59 470 59 0 115 1897 154581 44 612 87 0 109 2974 298001 96 992 129 1 104 1946 121844 75 634 158 2 63 2148 184039 69 677 120 0 118 1832 100324 98 665 87 0 71 3183 220269 119 1079 264 4 112 1476 168265 58 413 51 4 63 1567 154647 88 469 85 3 86 1756 142018 57 431 96 0 132 1247 79030 61 361 72 5 54 2779 167047 87 877 147 0 134 726 27997 24 221 49 0 57 1048 73019 59 366 40 0 59 2805 241082 100 846 99 0 113 1760 195820 72 642 127 0 96 2266 142001 54 689 164 1 96 1848 145433 86 576 41 1 78 1665 183744 32 610 160 0 80 2084 202357 163 673 92 0 93 1440 199532 93 361 59 0 109 2741 354924 118 907 89 0 115 2112 192399 44 882 90 0 79 1684 182286 44 490 76 0 103 1616 181590 45 548 116 2 71 2227 133801 105 723 92 4 66 3088 233686 123 918 344 0 100 2389 219428 53 787 84 1 96 1 0 1 0 0 0 0 2099 223044 63 983 61 0 109 1669 100129 51 539 138 3 51 2137 145864 49 515 270 9 119 2153 249965 64 795 64 0 136 2390 242379 71 753 96 2 84 1701 145794 59 635 62 0 136 983 96404 32 361 35 2 84 2161 195891 78 804 59 1 92 1276 117156 50 394 56 2 103 1190 157787 95 320 40 2 82 745 81293 32 212 49 1 106 2330 237435 101 772 121 0 96 2289 233155 89 740 113 1 124 2639 160344 59 938 172 8 97 658 48188 28 205 37 0 82 1917 161922 69 492 51 0 79 2557 307432 74 818 89 0 97 2026 235223 79 680 73 0 107 1911 195583 59 691 49 1 126 1716 146061 56 534 74 8 40 1852 208834 67 487 58 0 96 981 93764 24 301 72 1 100 1177 151985 66 421 32 0 91 2833 193222 96 947 59 10 136 1688 148922 60 492 70 6 124 2097 132856 80 790 85 0 79 1331 129561 61 362 87 11 74 1244 112718 37 430 48 3 96 1256 160930 35 416 56 0 97 1294 99184 41 409 41 0 122 2303 192535 70 498 86 8 144 2897 138708 65 887 152 2 90 1103 114408 38 267 48 0 93 340 31970 15 101 40 0 78 2791 225558 112 1000 135 3 72 1338 139220 72 416 83 1 45 1441 113612 68 480 62 2 120 1623 108641 71 454 91 1 59 2650 162203 67 671 91 0 133 1499 100098 44 413 82 2 117 2302 174768 60 677 112 1 123 2540 158459 97 820 69 0 110 1000 80934 30 316 78 0 75 1234 84971 71 395 105 0 114 927 80545 68 217 49 0 94 2176 287191 64 818 60 0 116 957 62974 28 292 49 1 86 1551 134091 40 513 132 0 90 1014 75555 46 345 49 0 87 1771 162154 54 557 71 0 99 2613 226638 227 645 100 0 132 1205 115367 112 284 74 0 96 1337 108749 62 424 49 7 91 1524 155537 52 614 72 0 77 1829 153133 41 672 59 5 104 2229 165618 78 649 90 1 97 1233 151517 57 415 68 0 94 1365 133686 58 505 81 0 60 950 61342 40 387 33 0 46 2319 245196 117 730 166 0 135 1857 195576 70 563 94 0 90 223 19349 12 67 15 0 2 2390 225371 105 812 104 3 96 1985 153213 78 811 61 0 109 700 59117 29 281 11 0 15 1062 91762 24 338 45 0 68 1311 136769 54 413 84 0 88 1157 114798 61 298 66 1 84 823 85338 40 223 27 1 46 596 27676 22 194 59 0 59 1545 153535 48 371 127 0 116 1130 122417 37 268 48 0 29 0 0 0 0 0 0 0 1082 91529 32 332 58 0 91 1135 107205 67 371 57 0 76 1367 144664 45 465 59 0 83 1506 146445 63 447 76 1 84 870 76656 60 295 71 0 65 78 3616 5 14 5 0 0 0 0 0 0 0 0 0 1130 183088 44 388 70 0 84 1582 144677 84 564 76 0 114 2034 159104 98 562 122 2 124 919 113273 38 288 56 0 92 778 43410 19 292 63 0 3 1752 175774 73 530 92 1 109 957 95401 42 256 54 0 74 2098 134837 55 602 64 8 121 731 60493 40 174 29 3 48 285 19764 12 75 19 1 8 1834 164062 56 565 64 3 80 1148 132696 33 377 79 0 107 1646 155367 54 544 97 0 116 256 11796 9 79 22 0 8 98 10674 9 33 7 0 0 1404 142261 57 479 37 0 56 41 6836 3 11 5 0 4 1824 162563 63 626 48 6 70 42 5118 3 6 1 0 0 528 40248 16 183 34 1 14 0 0 0 0 0 0 0 1073 122641 47 334 49 0 91 1305 88837 38 269 44 0 89 81 7131 4 27 0 1 0 261 9056 14 99 18 0 12 934 76611 24 260 48 1 60 1180 132697 51 290 54 0 80 1147 100681 19 414 50 1 88
Names of X columns:
page_views time_spent_seconds number_logins number_course_compenium_views number_compendium_views number_compediums_shared number_feedbackmessage_PR

Sample Range: (leave blank to include all observations)
From:
To:

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


Chart options



R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- 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'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } 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[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) 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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) 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, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) 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,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) 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,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) 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,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) 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,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) 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') }

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center