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Data X:
13 14 13 3 25 55 147 12 8 13 5 158 7 71 10 12 16 6 0 0 0 9 7 12 6 143 10 0 10 10 11 5 67 74 43 12 7 12 3 0 0 0 13 16 18 8 148 138 8 12 11 11 4 28 0 0 12 14 14 4 114 113 34 6 6 9 4 0 0 0 5 16 14 6 123 115 103 12 11 12 6 145 9 0 11 16 11 5 113 114 73 14 12 12 4 152 59 159 14 7 13 6 0 0 0 12 13 11 4 36 114 113 12 11 12 6 0 0 0 11 15 16 6 8 102 44 11 7 9 4 108 0 0 7 9 11 4 112 86 0 9 7 13 2 51 17 41 11 14 15 7 43 45 74 11 15 10 5 120 123 0 12 7 11 4 13 24 0 12 15 13 6 55 5 0 11 17 16 6 103 123 32 11 15 15 7 127 136 126 8 14 14 5 14 4 154 9 14 14 6 135 76 129 12 8 14 4 38 99 98 10 8 8 4 11 98 82 10 14 13 7 43 67 45 12 14 15 7 141 92 8 8 8 13 4 62 13 0 12 11 11 4 62 24 129 11 16 15 6 135 129 31 12 10 15 6 117 117 117 7 8 9 5 82 11 99 11 14 13 6 145 20 55 11 16 16 7 87 91 132 12 13 13 6 76 111 58 9 5 11 3 124 0 0 15 8 12 3 151 58 0 11 10 12 4 131 0 0 11 8 12 6 127 146 101 11 13 14 7 76 129 31 11 15 14 5 25 48 147 15 6 8 4 0 0 0 11 12 13 5 58 111 132 12 16 16 6 115 32 123 12 5 13 6 130 112 39 9 15 11 6 17 51 136 12 12 14 5 102 53 141 12 8 13 4 21 131 0 13 13 13 5 0 0 0 11 14 13 5 14 76 135 9 12 12 4 110 106 118 9 16 16 6 133 26 154 11 10 15 2 83 44 0 11 15 15 8 56 63 116 12 8 12 3 0 0 0 12 16 14 6 44 116 88 9 19 12 6 70 119 25 11 14 15 6 36 18 113 9 6 12 5 5 134 157 12 13 13 5 118 138 26 12 15 12 6 17 41 38 12 7 12 5 79 0 0 12 13 13 6 122 57 53 14 4 5 2 119 101 0 11 14 13 5 36 114 106 12 13 13 5 36 113 106 11 11 14 5 141 122 102 6 14 17 6 0 14 138 10 12 13 6 37 10 142 12 15 13 6 110 27 73 13 14 12 5 10 39 130 8 13 13 5 14 133 86 12 8 14 4 157 42 78 12 6 11 2 59 0 0 12 7 12 4 77 58 0 6 13 12 6 129 133 4 11 13 16 6 125 151 91 10 11 12 5 87 111 132 12 5 12 3 61 139 0 13 12 12 6 146 126 0 11 8 10 4 96 139 0 7 11 15 5 133 138 14 11 14 15 8 47 52 97 11 9 12 4 74 67 45 11 10 16 6 109 97 0 11 13 15 6 30 137 149 12 16 16 7 116 56 57 10 16 13 6 149 3 105 11 11 12 5 19 78 0 12 8 11 4 96 0 0 7 4 13 6 0 0 0 13 7 10 3 21 0 0 8 14 15 5 26 118 128 12 11 13 6 156 39 29 11 17 16 7 53 63 148 12 15 15 7 72 78 93 14 17 18 6 27 26 4 10 5 13 3 66 50 0 10 4 10 2 71 104 158 13 10 16 8 66 54 144 10 11 13 3 40 104 0 11 15 15 8 57 148 122 10 10 14 3 3 30 149 7 9 15 4 12 38 17 10 12 14 5 107 132 91 8 15 13 7 80 132 111 12 7 13 6 98 84 99 12 13 15 6 155 71 40 12 12 16 7 111 125 132 11 14 14 6 81 25 123 12 14 14 6 50 66 54 12 8 16 6 49 86 90 12 15 14 6 96 61 86 11 12 12 4 2 60 152 12 12 13 4 1 144 152 11 16 12 5 22 120 123 11 9 12 4 64 139 100 13 15 14 6 56 131 116 12 15 14 6 144 159 59 12 6 14 5 0 0 0 12 14 16 8 94 18 5 12 15 13 6 25 123 147 8 10 14 5 93 18 139 8 6 4 4 0 0 0 12 14 16 8 48 123 81 11 12 13 6 30 105 3 12 8 16 4 19 0 0 13 11 15 6 0 0 0 12 13 14 6 10 68 37 12 9 13 4 78 157 5 11 15 14 6 93 94 69 12 13 12 3 0 0 0 12 15 15 6 95 87 0 10 14 14 5 50 156 142 11 16 13 4 86 139 17 12 14 14 6 33 145 100 12 14 16 4 152 55 70 10 10 6 4 51 41 0 12 10 13 4 48 25 123 13 4 13 6 97 47 109 12 8 14 5 77 0 0 15 15 15 6 130 143 37 11 16 14 6 8 102 44 12 12 15 8 84 148 98 11 12 13 7 51 153 11 12 15 16 7 33 32 9 11 9 12 4 6 106 0 10 12 15 6 116 63 57 11 14 12 6 88 56 63 11 11 14 2 142 39 66
Names of X columns:
findingfriends knowingPeople liked celebrity friend secondbestfriend thirdbestfriend

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Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


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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') }

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Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center