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Type 'q()' to quit R. > x <- array(list(5 + ,1 + ,6 + ,5 + ,7 + ,2 + ,1 + ,6 + ,2 + ,3 + ,6 + ,3 + ,6 + ,6 + ,3 + ,6 + ,2 + ,4 + ,4 + ,6 + ,6 + ,3 + ,2 + ,6 + ,2 + ,5 + ,2 + ,7 + ,3 + ,3 + ,5 + ,2 + ,6 + ,5 + ,1 + ,6 + ,2 + ,5 + ,3 + ,2 + ,6 + ,4 + ,6 + ,5 + ,5 + ,5 + ,4 + ,7 + ,4 + ,1 + ,5 + ,1 + ,7 + ,1 + ,6 + ,5 + ,2 + ,4 + ,6 + ,1 + ,6 + ,1 + ,1 + ,6 + ,1 + ,5 + ,3 + ,6 + ,6 + ,2 + ,5 + ,2 + ,4 + ,4 + ,1 + ,6 + ,5 + ,5 + ,6 + ,3 + ,6 + ,2 + ,5 + ,5 + ,2 + ,4 + ,5 + ,6 + ,3 + ,2 + ,5 + ,4 + ,4 + ,5 + ,2 + ,5 + ,2 + ,6 + ,4 + ,2 + ,5 + ,2 + ,3 + ,5 + ,2 + ,6 + ,5 + ,3 + ,6 + ,2 + ,5 + ,1 + ,5 + ,3 + ,1 + ,7 + ,4 + ,5 + ,4 + ,3 + ,6 + ,1 + ,5 + ,5 + ,2 + ,6 + ,3 + ,5 + ,4 + ,3 + ,6 + ,2 + ,5 + ,5 + ,4 + ,6 + ,2 + ,2 + ,6 + ,5 + ,4 + ,1 + ,6 + ,7 + ,2 + ,5 + ,3 + ,7 + ,2 + ,5 + ,6 + ,4 + ,2 + ,4 + ,5 + ,4 + ,3 + ,3 + ,6 + ,1 + ,5 + ,5 + ,6 + ,5 + ,6 + ,5 + ,2 + ,5 + ,5 + ,3 + ,5 + ,1 + ,7 + ,5 + ,4 + ,7 + ,2 + ,5 + ,6 + ,6 + ,7 + ,5 + ,6 + ,6 + ,5 + ,6 + ,1 + ,5 + ,1 + ,5 + ,7 + ,3 + ,3 + ,4 + ,3 + ,6 + ,2 + ,7 + ,2 + ,3 + ,5 + ,3 + ,5 + ,3 + ,5 + ,6 + ,2 + ,5 + ,4 + ,2 + ,4 + ,2 + ,6 + ,5 + ,2 + ,6 + ,3 + ,2 + ,4 + ,3 + ,5 + ,3 + ,7 + ,4 + ,5 + ,5 + ,5 + ,3 + ,3 + ,2 + ,6 + ,3 + ,6 + ,4 + ,4 + ,6 + ,2 + ,7 + ,6 + ,5 + ,5 + ,1 + ,5 + ,4 + ,2 + ,6 + ,6 + ,4 + ,5 + ,2 + ,5 + ,6 + ,6 + ,4 + ,5 + ,5 + ,3 + ,7 + ,5 + ,6 + ,5 + ,5 + ,2 + ,6 + ,6 + ,6 + ,4 + ,2 + ,6 + ,5 + ,6 + ,3 + ,2 + ,4 + ,4 + ,5 + ,2 + ,5 + ,4 + ,3 + ,7 + ,7 + ,2 + ,6 + ,7 + ,6 + ,2 + ,5 + ,4 + ,7 + ,5 + ,2 + ,6 + ,2 + ,5 + ,5 + ,2 + ,2 + ,6 + ,2 + ,6 + ,2 + ,4 + ,5 + ,6 + ,5 + ,3 + ,6 + ,6 + ,6 + ,5 + ,5 + ,4 + ,6 + ,4 + ,6 + ,2 + ,3 + ,5 + ,5 + ,6 + ,5 + ,3 + ,5 + ,2 + ,3 + ,2 + ,3 + ,5 + ,6 + ,5 + ,1 + ,6 + ,5 + ,3 + ,5 + ,3 + ,6 + ,3 + ,2 + ,6 + ,4 + ,5 + ,4 + ,2 + ,5 + ,2 + ,3 + ,1 + ,5 + ,5 + ,4 + ,3 + ,5 + ,3 + ,4 + ,4 + ,2 + ,2 + ,4 + ,5 + ,3 + ,3 + ,6 + ,5 + ,5 + ,2 + ,3 + ,5 + ,7 + ,2 + ,1 + ,5 + ,2 + ,2 + ,6 + ,5 + ,3 + ,6 + ,5 + ,6 + ,2 + ,5 + ,5 + ,6 + ,6 + ,4 + ,2 + ,6 + ,4 + ,6 + ,4 + ,5 + ,3 + ,3 + ,5 + ,4 + ,6 + ,4 + ,6 + ,5 + ,2 + ,6 + ,4 + ,5 + ,6 + ,2 + ,5 + ,4 + ,2 + ,5 + ,2 + ,2 + ,4 + ,5 + ,5 + ,2 + ,6 + ,5 + ,3 + ,6 + ,3 + ,7 + ,2 + ,6 + ,3 + ,5 + ,5 + ,3 + ,5 + ,6 + ,1 + ,5 + ,5 + ,1 + ,3 + ,2 + ,2 + ,6 + ,5 + ,5 + ,2 + ,5 + ,5 + ,2 + ,5 + ,2 + ,6 + ,6 + ,1 + ,6 + ,5 + ,5 + ,3 + ,4 + ,5 + ,2 + ,5 + ,4 + ,2 + ,6 + ,1 + ,4 + ,4 + ,3 + ,6 + ,2 + ,5 + ,3 + ,5 + ,6 + ,1 + ,4 + ,4 + ,6 + ,7 + ,6 + ,2 + ,4 + ,4 + ,5 + ,2 + ,3 + ,4 + ,4 + ,3 + ,1 + ,5 + ,2 + ,5 + ,4 + ,1 + ,2 + ,6 + ,1 + ,7 + ,6 + ,2 + ,3 + ,6 + ,6 + ,1 + ,4 + ,5 + ,2 + ,6 + ,2 + ,3 + ,5 + ,3 + ,5 + ,2 + ,5 + ,5 + ,5 + ,4 + ,1 + ,5 + ,5 + ,2 + ,6 + ,2 + ,2 + ,4 + ,2 + ,6 + ,1 + ,5 + ,2 + ,3 + ,6 + ,1 + ,2 + ,5 + ,2 + ,5 + ,3 + ,6 + ,3 + ,6 + ,6 + ,5 + ,2 + ,6 + ,3 + ,6 + ,2 + ,1 + ,6 + ,2 + ,2 + ,1 + ,6 + ,1 + ,1 + ,6 + ,3 + ,2 + ,7 + ,1 + ,5 + ,2 + ,3 + ,5 + ,1 + ,5 + ,4 + ,5 + ,6 + ,4 + ,3 + ,2 + ,4 + ,6 + ,1 + ,4 + ,5 + ,4 + ,6 + ,1 + ,6 + ,1 + ,6 + ,3 + ,1 + ,5 + ,2 + ,2 + ,6 + ,5 + ,6 + ,2 + ,7 + ,7 + ,5 + ,4 + ,1 + ,2 + ,6 + ,2 + ,6 + ,2 + ,5 + ,5 + ,3 + ,4 + ,2 + ,3 + ,5 + ,5 + ,3 + ,5 + ,3 + ,5 + ,2 + ,6 + ,2 + ,5 + ,5 + ,2 + ,5 + ,5 + ,5 + ,4 + ,4 + ,5 + ,2 + ,2 + ,6 + ,1 + ,7 + ,5 + ,4 + ,4 + ,5 + ,6 + ,1 + ,3 + ,6 + ,1 + ,6 + ,3 + ,2 + ,6 + ,2 + ,5 + ,2 + ,6 + ,4 + ,2 + ,6 + ,1 + ,6 + ,3 + ,6 + ,6 + ,2 + ,3 + ,5 + ,2 + ,5 + ,1 + ,2 + ,7 + ,1 + ,2 + ,2 + ,6 + ,3 + ,3 + ,5 + ,2 + ,6 + ,4 + ,4 + ,3 + ,2 + ,2 + ,2 + ,6 + ,6 + ,4 + ,5 + ,4 + ,5 + ,5 + ,5 + ,6 + ,4 + ,6 + ,5 + ,2 + ,5 + ,3 + ,6 + ,5 + ,3 + ,3 + ,2 + ,1 + ,2 + ,2 + ,7 + ,5 + ,6 + ,5 + ,1 + ,5 + ,5 + ,2 + ,5 + ,2 + ,4 + ,4 + ,2 + ,6 + ,2 + ,5 + ,6 + ,7 + ,6 + ,2 + ,3 + ,5 + ,2 + ,5 + ,2 + ,2 + ,1 + ,2 + ,5 + ,2 + ,5 + ,5 + ,6 + ,3 + ,4 + ,6 + ,6 + ,1 + ,5 + ,2 + ,5 + ,5 + ,2 + ,6 + ,4 + ,2 + ,5 + ,3 + ,6 + ,5 + ,3 + ,5 + ,4 + ,6 + ,3 + ,2 + ,5 + ,5 + ,6 + ,4 + ,6 + ,4 + ,5 + ,6 + ,4 + ,6 + ,7 + ,6 + ,5 + ,2 + ,2 + ,6 + ,3 + ,7 + ,3 + ,2 + ,5 + ,1 + ,6 + ,2 + ,3 + ,6 + ,3 + ,6 + ,1 + ,4 + ,3 + ,2 + ,6 + ,2 + ,6 + ,5 + ,3 + ,7 + ,3 + ,2 + ,6 + ,7 + ,1 + ,3 + ,7 + ,1 + ,3 + ,6 + ,2 + ,2 + ,6 + ,4 + ,5 + ,5 + ,2 + ,4 + ,6 + ,6 + ,1 + ,4 + ,5 + ,2) + ,dim=c(5 + ,164) + ,dimnames=list(c('Use_hands' + ,'Hand_on_hips' + ,'Quiet' + ,'Outgoing_individual' + ,'Cry') + ,1:164)) > y <- array(NA,dim=c(5,164),dimnames=list(c('Use_hands','Hand_on_hips','Quiet','Outgoing_individual','Cry'),1:164)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Include Monthly Dummies' > par1 = '4' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > 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 Outgoing_individual Use_hands Hand_on_hips Quiet Cry M1 M2 M3 M4 M5 M6 M7 1 5 5 1 6 7 1 0 0 0 0 0 0 2 2 2 1 6 3 0 1 0 0 0 0 0 3 6 6 3 6 3 0 0 1 0 0 0 0 4 4 6 2 4 6 0 0 0 1 0 0 0 5 6 6 3 2 2 0 0 0 0 1 0 0 6 3 5 2 7 3 0 0 0 0 0 1 0 7 5 5 2 6 1 0 0 0 0 0 0 1 8 3 6 2 5 2 0 0 0 0 0 0 0 9 5 6 4 6 5 0 0 0 0 0 0 0 10 4 5 4 7 1 0 0 0 0 0 0 0 11 1 5 1 7 6 0 0 0 0 0 0 0 12 6 5 2 4 1 0 0 0 0 0 0 0 13 6 6 1 1 1 1 0 0 0 0 0 0 14 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62 63 0 0 0 0 63 64 0 0 0 0 64 65 0 0 0 0 65 66 0 0 0 0 66 67 0 0 0 0 67 68 1 0 0 0 68 69 0 1 0 0 69 70 0 0 1 0 70 71 0 0 0 1 71 72 0 0 0 0 72 73 0 0 0 0 73 74 0 0 0 0 74 75 0 0 0 0 75 76 0 0 0 0 76 77 0 0 0 0 77 78 0 0 0 0 78 79 0 0 0 0 79 80 1 0 0 0 80 81 0 1 0 0 81 82 0 0 1 0 82 83 0 0 0 1 83 84 0 0 0 0 84 85 0 0 0 0 85 86 0 0 0 0 86 87 0 0 0 0 87 88 0 0 0 0 88 89 0 0 0 0 89 90 0 0 0 0 90 91 0 0 0 0 91 92 1 0 0 0 92 93 0 1 0 0 93 94 0 0 1 0 94 95 0 0 0 1 95 96 0 0 0 0 96 97 0 0 0 0 97 98 0 0 0 0 98 99 0 0 0 0 99 100 0 0 0 0 100 101 0 0 0 0 101 102 0 0 0 0 102 103 0 0 0 0 103 104 1 0 0 0 104 105 0 1 0 0 105 106 0 0 1 0 106 107 0 0 0 1 107 108 0 0 0 0 108 109 0 0 0 0 109 110 0 0 0 0 110 111 0 0 0 0 111 112 0 0 0 0 112 113 0 0 0 0 113 114 0 0 0 0 114 115 0 0 0 0 115 116 1 0 0 0 116 117 0 1 0 0 117 118 0 0 1 0 118 119 0 0 0 1 119 120 0 0 0 0 120 121 0 0 0 0 121 122 0 0 0 0 122 123 0 0 0 0 123 124 0 0 0 0 124 125 0 0 0 0 125 126 0 0 0 0 126 127 0 0 0 0 127 128 1 0 0 0 128 129 0 1 0 0 129 130 0 0 1 0 130 131 0 0 0 1 131 132 0 0 0 0 132 133 0 0 0 0 133 134 0 0 0 0 134 135 0 0 0 0 135 136 0 0 0 0 136 137 0 0 0 0 137 138 0 0 0 0 138 139 0 0 0 0 139 140 1 0 0 0 140 141 0 1 0 0 141 142 0 0 1 0 142 143 0 0 0 1 143 144 0 0 0 0 144 145 0 0 0 0 145 146 0 0 0 0 146 147 0 0 0 0 147 148 0 0 0 0 148 149 0 0 0 0 149 150 0 0 0 0 150 151 0 0 0 0 151 152 1 0 0 0 152 153 0 1 0 0 153 154 0 0 1 0 154 155 0 0 0 1 155 156 0 0 0 0 156 157 0 0 0 0 157 158 0 0 0 0 158 159 0 0 0 0 159 160 0 0 0 0 160 161 0 0 0 0 161 162 0 0 0 0 162 163 0 0 0 0 163 164 1 0 0 0 164 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Use_hands Hand_on_hips Quiet Cry 4.6380430 0.1982770 0.0495029 -0.1818638 -0.0854799 M1 M2 M3 M4 M5 0.2452268 -0.8776618 -0.2217177 0.0719584 -0.0352654 M6 M7 M8 M9 M10 -0.0246671 -0.4532720 -0.4964190 -0.1697430 -0.5994069 M11 t -0.5596384 0.0004647 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -3.38393 -0.81614 0.09927 0.91287 3.27807 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.6380430 0.8590930 5.399 2.63e-07 *** Use_hands 0.1982770 0.1046593 1.894 0.06012 . Hand_on_hips 0.0495029 0.0829987 0.596 0.55181 Quiet -0.1818638 0.0693883 -2.621 0.00969 ** Cry -0.0854799 0.0641694 -1.332 0.18489 M1 0.2452268 0.5210226 0.471 0.63858 M2 -0.8776618 0.5319688 -1.650 0.10111 M3 -0.2217177 0.5300120 -0.418 0.67632 M4 0.0719584 0.5240041 0.137 0.89096 M5 -0.0352654 0.5248656 -0.067 0.94652 M6 -0.0246671 0.5230956 -0.047 0.96245 M7 -0.4532720 0.5241019 -0.865 0.38853 M8 -0.4964190 0.5270134 -0.942 0.34776 M9 -0.1697430 0.5333386 -0.318 0.75074 M10 -0.5994069 0.5309703 -1.129 0.26078 M11 -0.5596384 0.5369328 -1.042 0.29899 t 0.0004647 0.0023194 0.200 0.84147 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.348 on 147 degrees of freedom Multiple R-squared: 0.1779, Adjusted R-squared: 0.08842 F-statistic: 1.988 on 16 and 147 DF, p-value: 0.01733 > 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 + } [,1] [,2] [,3] [1,] 0.2674014 0.5348027 0.7325986 [2,] 0.2456511 0.4913022 0.7543489 [3,] 0.1676649 0.3353298 0.8323351 [4,] 0.1326959 0.2653917 0.8673041 [5,] 0.4264423 0.8528845 0.5735577 [6,] 0.3384713 0.6769426 0.6615287 [7,] 0.2607998 0.5215995 0.7392002 [8,] 0.2216062 0.4432124 0.7783938 [9,] 0.2127423 0.4254846 0.7872577 [10,] 0.3047993 0.6095987 0.6952007 [11,] 0.2667471 0.5334942 0.7332529 [12,] 0.2142878 0.4285756 0.7857122 [13,] 0.2335793 0.4671587 0.7664207 [14,] 0.1795332 0.3590665 0.8204668 [15,] 0.1411090 0.2822180 0.8588910 [16,] 0.2312182 0.4624365 0.7687818 [17,] 0.2225372 0.4450745 0.7774628 [18,] 0.1805989 0.3611978 0.8194011 [19,] 0.3297671 0.6595341 0.6702329 [20,] 0.3144672 0.6289345 0.6855328 [21,] 0.5366869 0.9266261 0.4633131 [22,] 0.6437522 0.7124956 0.3562478 [23,] 0.6348681 0.7302637 0.3651319 [24,] 0.5959233 0.8081534 0.4040767 [25,] 0.5436697 0.9126606 0.4563303 [26,] 0.4866258 0.9732516 0.5133742 [27,] 0.5435522 0.9128956 0.4564478 [28,] 0.4979349 0.9958698 0.5020651 [29,] 0.5009926 0.9980149 0.4990074 [30,] 0.4854165 0.9708329 0.5145835 [31,] 0.4425004 0.8850009 0.5574996 [32,] 0.3949470 0.7898940 0.6050530 [33,] 0.3711873 0.7423747 0.6288127 [34,] 0.3419568 0.6839137 0.6580432 [35,] 0.4068974 0.8137948 0.5931026 [36,] 0.3698235 0.7396470 0.6301765 [37,] 0.3197441 0.6394883 0.6802559 [38,] 0.2859274 0.5718548 0.7140726 [39,] 0.2481083 0.4962167 0.7518917 [40,] 0.2502100 0.5004201 0.7497900 [41,] 0.2202337 0.4404674 0.7797663 [42,] 0.1827461 0.3654921 0.8172539 [43,] 0.3044576 0.6089153 0.6955424 [44,] 0.3030538 0.6061076 0.6969462 [45,] 0.2593950 0.5187901 0.7406050 [46,] 0.2378988 0.4757977 0.7621012 [47,] 0.2164080 0.4328161 0.7835920 [48,] 0.2002663 0.4005325 0.7997337 [49,] 0.1846054 0.3692108 0.8153946 [50,] 0.1630378 0.3260756 0.8369622 [51,] 0.3223564 0.6447128 0.6776436 [52,] 0.2918979 0.5837957 0.7081021 [53,] 0.5272454 0.9455093 0.4727546 [54,] 0.5010678 0.9978644 0.4989322 [55,] 0.5066465 0.9867070 0.4933535 [56,] 0.5351196 0.9297609 0.4648804 [57,] 0.5113718 0.9772565 0.4886282 [58,] 0.4800388 0.9600776 0.5199612 [59,] 0.4622651 0.9245302 0.5377349 [60,] 0.4630329 0.9260658 0.5369671 [61,] 0.4180924 0.8361849 0.5819076 [62,] 0.3722217 0.7444435 0.6277783 [63,] 0.3315443 0.6630886 0.6684557 [64,] 0.2946552 0.5893103 0.7053448 [65,] 0.2642039 0.5284079 0.7357961 [66,] 0.3435700 0.6871400 0.6564300 [67,] 0.3082451 0.6164902 0.6917549 [68,] 0.2753237 0.5506475 0.7246763 [69,] 0.2797690 0.5595381 0.7202310 [70,] 0.2515559 0.5031118 0.7484441 [71,] 0.2667826 0.5335652 0.7332174 [72,] 0.2563919 0.5127839 0.7436081 [73,] 0.2185325 0.4370651 0.7814675 [74,] 0.1882446 0.3764892 0.8117554 [75,] 0.1735242 0.3470485 0.8264758 [76,] 0.1489527 0.2979054 0.8510473 [77,] 0.1516813 0.3033626 0.8483187 [78,] 0.1418067 0.2836133 0.8581933 [79,] 0.1279703 0.2559406 0.8720297 [80,] 0.1246914 0.2493827 0.8753086 [81,] 0.1963430 0.3926859 0.8036570 [82,] 0.1679323 0.3358646 0.8320677 [83,] 0.1409683 0.2819366 0.8590317 [84,] 0.1297827 0.2595653 0.8702173 [85,] 0.1187778 0.2375555 0.8812222 [86,] 0.1103887 0.2207775 0.8896113 [87,] 0.1962603 0.3925207 0.8037397 [88,] 0.1751474 0.3502947 0.8248526 [89,] 0.1922939 0.3845878 0.8077061 [90,] 0.1685962 0.3371924 0.8314038 [91,] 0.1696324 0.3392647 0.8303676 [92,] 0.3138691 0.6277382 0.6861309 [93,] 0.3095159 0.6190318 0.6904841 [94,] 0.2713042 0.5426085 0.7286958 [95,] 0.2543434 0.5086867 0.7456566 [96,] 0.3212857 0.6425714 0.6787143 [97,] 0.3230560 0.6461119 0.6769440 [98,] 0.3578832 0.7157663 0.6421168 [99,] 0.3390182 0.6780365 0.6609818 [100,] 0.5141702 0.9716596 0.4858298 [101,] 0.4779896 0.9559792 0.5220104 [102,] 0.4287369 0.8574739 0.5712631 [103,] 0.5307363 0.9385274 0.4692637 [104,] 0.4876492 0.9752985 0.5123508 [105,] 0.4235823 0.8471646 0.5764177 [106,] 0.3790818 0.7581636 0.6209182 [107,] 0.3509323 0.7018646 0.6490677 [108,] 0.2933214 0.5866428 0.7066786 [109,] 0.3501628 0.7003256 0.6498372 [110,] 0.3403470 0.6806940 0.6596530 [111,] 0.3057969 0.6115938 0.6942031 [112,] 0.3378097 0.6756195 0.6621903 [113,] 0.2730916 0.5461833 0.7269084 [114,] 0.3801707 0.7603413 0.6198293 [115,] 0.4903970 0.9807939 0.5096030 [116,] 0.4058560 0.8117121 0.5941440 [117,] 0.5109928 0.9780143 0.4890072 [118,] 0.4328373 0.8656746 0.5671627 [119,] 0.3581856 0.7163712 0.6418144 [120,] 0.4047950 0.8095900 0.5952050 [121,] 0.4564389 0.9128777 0.5435611 [122,] 0.5509769 0.8980462 0.4490231 [123,] 0.5033655 0.9932690 0.4966345 [124,] 0.7123824 0.5752353 0.2876176 [125,] 0.5605378 0.8789243 0.4394622 > postscript(file="/var/www/html/rcomp/tmp/1hjyo1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2rsf91290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3rsf91290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4rsf91290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5rsf91290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 164 Frequency = 1 1 2 3 4 5 6 0.764919603 -0.859745153 1.591732356 -0.760193436 0.591415614 -1.177068765 7 8 9 10 11 12 0.898247841 -1.353730706 0.658426180 0.125846523 -2.338478501 1.078924738 13 14 15 16 17 18 0.138867857 2.355361633 -0.700751689 1.011145294 0.180933224 -1.400220824 19 20 21 22 23 24 0.515417756 0.020833413 0.148101396 1.230514864 -1.135182098 -1.069388076 25 26 27 28 29 30 -0.053773794 0.055124191 0.533698185 0.779445926 2.803277222 -2.066765114 31 32 33 34 35 36 -0.795723669 1.532959657 0.881526899 1.020931396 1.479408502 1.280480828 37 38 39 40 41 42 0.982664407 -2.680486797 -1.168865724 -2.487771633 -1.425006274 -0.841282969 43 44 45 46 47 48 1.165274871 -0.880074853 0.071340004 -1.582361390 -0.005314667 1.751428822 49 50 51 52 53 54 -0.866650048 0.628118565 -0.209846084 0.911865476 1.010299876 0.764982963 55 56 57 58 59 60 -0.842853856 -0.092280283 0.732838756 0.153420788 -1.677631505 0.778370669 61 62 63 64 65 66 0.040049267 2.674974771 1.384872803 -0.055420129 -0.353609491 0.720586730 67 68 69 70 71 72 1.090827507 -1.050975933 -0.807760295 -3.188566174 0.501234970 -2.956975032 73 74 75 76 77 78 0.915903044 1.258789675 -1.816957268 0.790494510 0.494969755 0.668349857 79 80 81 82 83 84 -1.443398580 0.235864108 -0.077750665 -0.285131883 -0.416239784 0.580152412 85 86 87 88 89 90 -2.475015566 -0.305974018 0.298878394 1.346393771 0.345750543 1.431071438 91 92 93 94 95 96 -1.412998214 -0.194489995 -0.766788629 -1.034268816 -0.121383008 -1.661964962 97 98 99 100 101 102 -1.131227143 -1.113538951 1.144264408 -2.564822457 0.009536128 -0.147413645 103 104 105 106 107 108 1.013690785 1.047713276 -1.271075578 -2.161302287 0.167393287 -1.224064046 109 110 111 112 113 114 0.249066878 1.252655904 -2.737303214 1.348987194 -0.114610095 1.395488295 115 116 117 118 119 120 1.880884766 1.576781339 -1.585174093 1.607263632 3.278072267 0.998267513 121 122 123 124 125 126 -0.062409676 1.263800424 0.400720139 0.023984695 -0.648528250 0.686886552 127 128 129 130 131 132 -0.724388870 1.190798719 0.668268290 0.072702415 -0.774385296 -0.271502166 133 134 135 136 137 138 1.463242514 0.029409253 -0.136350551 -2.590432765 -0.717399007 -0.312344287 139 140 141 142 143 144 -0.917559144 -2.715506765 1.756540838 0.934764961 -0.336834960 1.514048217 145 146 147 148 149 150 -0.522770314 -3.383933190 0.847168859 1.605040399 0.317867508 -0.450589675 151 152 153 154 155 156 0.195391219 0.240695468 -0.408493103 3.106185971 1.379340792 -0.797778916 157 158 159 160 161 162 0.557132971 -1.174556306 0.568739384 0.641283155 -2.494896754 0.728319446 163 164 -0.622812412 0.441412556 > postscript(file="/var/www/html/rcomp/tmp/621ec1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 164 Frequency = 1 lag(myerror, k = 1) myerror 0 0.764919603 NA 1 -0.859745153 0.764919603 2 1.591732356 -0.859745153 3 -0.760193436 1.591732356 4 0.591415614 -0.760193436 5 -1.177068765 0.591415614 6 0.898247841 -1.177068765 7 -1.353730706 0.898247841 8 0.658426180 -1.353730706 9 0.125846523 0.658426180 10 -2.338478501 0.125846523 11 1.078924738 -2.338478501 12 0.138867857 1.078924738 13 2.355361633 0.138867857 14 -0.700751689 2.355361633 15 1.011145294 -0.700751689 16 0.180933224 1.011145294 17 -1.400220824 0.180933224 18 0.515417756 -1.400220824 19 0.020833413 0.515417756 20 0.148101396 0.020833413 21 1.230514864 0.148101396 22 -1.135182098 1.230514864 23 -1.069388076 -1.135182098 24 -0.053773794 -1.069388076 25 0.055124191 -0.053773794 26 0.533698185 0.055124191 27 0.779445926 0.533698185 28 2.803277222 0.779445926 29 -2.066765114 2.803277222 30 -0.795723669 -2.066765114 31 1.532959657 -0.795723669 32 0.881526899 1.532959657 33 1.020931396 0.881526899 34 1.479408502 1.020931396 35 1.280480828 1.479408502 36 0.982664407 1.280480828 37 -2.680486797 0.982664407 38 -1.168865724 -2.680486797 39 -2.487771633 -1.168865724 40 -1.425006274 -2.487771633 41 -0.841282969 -1.425006274 42 1.165274871 -0.841282969 43 -0.880074853 1.165274871 44 0.071340004 -0.880074853 45 -1.582361390 0.071340004 46 -0.005314667 -1.582361390 47 1.751428822 -0.005314667 48 -0.866650048 1.751428822 49 0.628118565 -0.866650048 50 -0.209846084 0.628118565 51 0.911865476 -0.209846084 52 1.010299876 0.911865476 53 0.764982963 1.010299876 54 -0.842853856 0.764982963 55 -0.092280283 -0.842853856 56 0.732838756 -0.092280283 57 0.153420788 0.732838756 58 -1.677631505 0.153420788 59 0.778370669 -1.677631505 60 0.040049267 0.778370669 61 2.674974771 0.040049267 62 1.384872803 2.674974771 63 -0.055420129 1.384872803 64 -0.353609491 -0.055420129 65 0.720586730 -0.353609491 66 1.090827507 0.720586730 67 -1.050975933 1.090827507 68 -0.807760295 -1.050975933 69 -3.188566174 -0.807760295 70 0.501234970 -3.188566174 71 -2.956975032 0.501234970 72 0.915903044 -2.956975032 73 1.258789675 0.915903044 74 -1.816957268 1.258789675 75 0.790494510 -1.816957268 76 0.494969755 0.790494510 77 0.668349857 0.494969755 78 -1.443398580 0.668349857 79 0.235864108 -1.443398580 80 -0.077750665 0.235864108 81 -0.285131883 -0.077750665 82 -0.416239784 -0.285131883 83 0.580152412 -0.416239784 84 -2.475015566 0.580152412 85 -0.305974018 -2.475015566 86 0.298878394 -0.305974018 87 1.346393771 0.298878394 88 0.345750543 1.346393771 89 1.431071438 0.345750543 90 -1.412998214 1.431071438 91 -0.194489995 -1.412998214 92 -0.766788629 -0.194489995 93 -1.034268816 -0.766788629 94 -0.121383008 -1.034268816 95 -1.661964962 -0.121383008 96 -1.131227143 -1.661964962 97 -1.113538951 -1.131227143 98 1.144264408 -1.113538951 99 -2.564822457 1.144264408 100 0.009536128 -2.564822457 101 -0.147413645 0.009536128 102 1.013690785 -0.147413645 103 1.047713276 1.013690785 104 -1.271075578 1.047713276 105 -2.161302287 -1.271075578 106 0.167393287 -2.161302287 107 -1.224064046 0.167393287 108 0.249066878 -1.224064046 109 1.252655904 0.249066878 110 -2.737303214 1.252655904 111 1.348987194 -2.737303214 112 -0.114610095 1.348987194 113 1.395488295 -0.114610095 114 1.880884766 1.395488295 115 1.576781339 1.880884766 116 -1.585174093 1.576781339 117 1.607263632 -1.585174093 118 3.278072267 1.607263632 119 0.998267513 3.278072267 120 -0.062409676 0.998267513 121 1.263800424 -0.062409676 122 0.400720139 1.263800424 123 0.023984695 0.400720139 124 -0.648528250 0.023984695 125 0.686886552 -0.648528250 126 -0.724388870 0.686886552 127 1.190798719 -0.724388870 128 0.668268290 1.190798719 129 0.072702415 0.668268290 130 -0.774385296 0.072702415 131 -0.271502166 -0.774385296 132 1.463242514 -0.271502166 133 0.029409253 1.463242514 134 -0.136350551 0.029409253 135 -2.590432765 -0.136350551 136 -0.717399007 -2.590432765 137 -0.312344287 -0.717399007 138 -0.917559144 -0.312344287 139 -2.715506765 -0.917559144 140 1.756540838 -2.715506765 141 0.934764961 1.756540838 142 -0.336834960 0.934764961 143 1.514048217 -0.336834960 144 -0.522770314 1.514048217 145 -3.383933190 -0.522770314 146 0.847168859 -3.383933190 147 1.605040399 0.847168859 148 0.317867508 1.605040399 149 -0.450589675 0.317867508 150 0.195391219 -0.450589675 151 0.240695468 0.195391219 152 -0.408493103 0.240695468 153 3.106185971 -0.408493103 154 1.379340792 3.106185971 155 -0.797778916 1.379340792 156 0.557132971 -0.797778916 157 -1.174556306 0.557132971 158 0.568739384 -1.174556306 159 0.641283155 0.568739384 160 -2.494896754 0.641283155 161 0.728319446 -2.494896754 162 -0.622812412 0.728319446 163 0.441412556 -0.622812412 164 NA 0.441412556 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.859745153 0.764919603 [2,] 1.591732356 -0.859745153 [3,] -0.760193436 1.591732356 [4,] 0.591415614 -0.760193436 [5,] -1.177068765 0.591415614 [6,] 0.898247841 -1.177068765 [7,] -1.353730706 0.898247841 [8,] 0.658426180 -1.353730706 [9,] 0.125846523 0.658426180 [10,] -2.338478501 0.125846523 [11,] 1.078924738 -2.338478501 [12,] 0.138867857 1.078924738 [13,] 2.355361633 0.138867857 [14,] -0.700751689 2.355361633 [15,] 1.011145294 -0.700751689 [16,] 0.180933224 1.011145294 [17,] -1.400220824 0.180933224 [18,] 0.515417756 -1.400220824 [19,] 0.020833413 0.515417756 [20,] 0.148101396 0.020833413 [21,] 1.230514864 0.148101396 [22,] -1.135182098 1.230514864 [23,] -1.069388076 -1.135182098 [24,] -0.053773794 -1.069388076 [25,] 0.055124191 -0.053773794 [26,] 0.533698185 0.055124191 [27,] 0.779445926 0.533698185 [28,] 2.803277222 0.779445926 [29,] -2.066765114 2.803277222 [30,] -0.795723669 -2.066765114 [31,] 1.532959657 -0.795723669 [32,] 0.881526899 1.532959657 [33,] 1.020931396 0.881526899 [34,] 1.479408502 1.020931396 [35,] 1.280480828 1.479408502 [36,] 0.982664407 1.280480828 [37,] -2.680486797 0.982664407 [38,] -1.168865724 -2.680486797 [39,] -2.487771633 -1.168865724 [40,] -1.425006274 -2.487771633 [41,] -0.841282969 -1.425006274 [42,] 1.165274871 -0.841282969 [43,] -0.880074853 1.165274871 [44,] 0.071340004 -0.880074853 [45,] -1.582361390 0.071340004 [46,] -0.005314667 -1.582361390 [47,] 1.751428822 -0.005314667 [48,] -0.866650048 1.751428822 [49,] 0.628118565 -0.866650048 [50,] -0.209846084 0.628118565 [51,] 0.911865476 -0.209846084 [52,] 1.010299876 0.911865476 [53,] 0.764982963 1.010299876 [54,] -0.842853856 0.764982963 [55,] -0.092280283 -0.842853856 [56,] 0.732838756 -0.092280283 [57,] 0.153420788 0.732838756 [58,] -1.677631505 0.153420788 [59,] 0.778370669 -1.677631505 [60,] 0.040049267 0.778370669 [61,] 2.674974771 0.040049267 [62,] 1.384872803 2.674974771 [63,] -0.055420129 1.384872803 [64,] -0.353609491 -0.055420129 [65,] 0.720586730 -0.353609491 [66,] 1.090827507 0.720586730 [67,] -1.050975933 1.090827507 [68,] -0.807760295 -1.050975933 [69,] -3.188566174 -0.807760295 [70,] 0.501234970 -3.188566174 [71,] -2.956975032 0.501234970 [72,] 0.915903044 -2.956975032 [73,] 1.258789675 0.915903044 [74,] -1.816957268 1.258789675 [75,] 0.790494510 -1.816957268 [76,] 0.494969755 0.790494510 [77,] 0.668349857 0.494969755 [78,] -1.443398580 0.668349857 [79,] 0.235864108 -1.443398580 [80,] -0.077750665 0.235864108 [81,] -0.285131883 -0.077750665 [82,] -0.416239784 -0.285131883 [83,] 0.580152412 -0.416239784 [84,] -2.475015566 0.580152412 [85,] -0.305974018 -2.475015566 [86,] 0.298878394 -0.305974018 [87,] 1.346393771 0.298878394 [88,] 0.345750543 1.346393771 [89,] 1.431071438 0.345750543 [90,] -1.412998214 1.431071438 [91,] -0.194489995 -1.412998214 [92,] -0.766788629 -0.194489995 [93,] -1.034268816 -0.766788629 [94,] -0.121383008 -1.034268816 [95,] -1.661964962 -0.121383008 [96,] -1.131227143 -1.661964962 [97,] -1.113538951 -1.131227143 [98,] 1.144264408 -1.113538951 [99,] -2.564822457 1.144264408 [100,] 0.009536128 -2.564822457 [101,] -0.147413645 0.009536128 [102,] 1.013690785 -0.147413645 [103,] 1.047713276 1.013690785 [104,] -1.271075578 1.047713276 [105,] -2.161302287 -1.271075578 [106,] 0.167393287 -2.161302287 [107,] -1.224064046 0.167393287 [108,] 0.249066878 -1.224064046 [109,] 1.252655904 0.249066878 [110,] -2.737303214 1.252655904 [111,] 1.348987194 -2.737303214 [112,] -0.114610095 1.348987194 [113,] 1.395488295 -0.114610095 [114,] 1.880884766 1.395488295 [115,] 1.576781339 1.880884766 [116,] -1.585174093 1.576781339 [117,] 1.607263632 -1.585174093 [118,] 3.278072267 1.607263632 [119,] 0.998267513 3.278072267 [120,] -0.062409676 0.998267513 [121,] 1.263800424 -0.062409676 [122,] 0.400720139 1.263800424 [123,] 0.023984695 0.400720139 [124,] -0.648528250 0.023984695 [125,] 0.686886552 -0.648528250 [126,] -0.724388870 0.686886552 [127,] 1.190798719 -0.724388870 [128,] 0.668268290 1.190798719 [129,] 0.072702415 0.668268290 [130,] -0.774385296 0.072702415 [131,] -0.271502166 -0.774385296 [132,] 1.463242514 -0.271502166 [133,] 0.029409253 1.463242514 [134,] -0.136350551 0.029409253 [135,] -2.590432765 -0.136350551 [136,] -0.717399007 -2.590432765 [137,] -0.312344287 -0.717399007 [138,] -0.917559144 -0.312344287 [139,] -2.715506765 -0.917559144 [140,] 1.756540838 -2.715506765 [141,] 0.934764961 1.756540838 [142,] -0.336834960 0.934764961 [143,] 1.514048217 -0.336834960 [144,] -0.522770314 1.514048217 [145,] -3.383933190 -0.522770314 [146,] 0.847168859 -3.383933190 [147,] 1.605040399 0.847168859 [148,] 0.317867508 1.605040399 [149,] -0.450589675 0.317867508 [150,] 0.195391219 -0.450589675 [151,] 0.240695468 0.195391219 [152,] -0.408493103 0.240695468 [153,] 3.106185971 -0.408493103 [154,] 1.379340792 3.106185971 [155,] -0.797778916 1.379340792 [156,] 0.557132971 -0.797778916 [157,] -1.174556306 0.557132971 [158,] 0.568739384 -1.174556306 [159,] 0.641283155 0.568739384 [160,] -2.494896754 0.641283155 [161,] 0.728319446 -2.494896754 [162,] -0.622812412 0.728319446 [163,] 0.441412556 -0.622812412 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.859745153 0.764919603 2 1.591732356 -0.859745153 3 -0.760193436 1.591732356 4 0.591415614 -0.760193436 5 -1.177068765 0.591415614 6 0.898247841 -1.177068765 7 -1.353730706 0.898247841 8 0.658426180 -1.353730706 9 0.125846523 0.658426180 10 -2.338478501 0.125846523 11 1.078924738 -2.338478501 12 0.138867857 1.078924738 13 2.355361633 0.138867857 14 -0.700751689 2.355361633 15 1.011145294 -0.700751689 16 0.180933224 1.011145294 17 -1.400220824 0.180933224 18 0.515417756 -1.400220824 19 0.020833413 0.515417756 20 0.148101396 0.020833413 21 1.230514864 0.148101396 22 -1.135182098 1.230514864 23 -1.069388076 -1.135182098 24 -0.053773794 -1.069388076 25 0.055124191 -0.053773794 26 0.533698185 0.055124191 27 0.779445926 0.533698185 28 2.803277222 0.779445926 29 -2.066765114 2.803277222 30 -0.795723669 -2.066765114 31 1.532959657 -0.795723669 32 0.881526899 1.532959657 33 1.020931396 0.881526899 34 1.479408502 1.020931396 35 1.280480828 1.479408502 36 0.982664407 1.280480828 37 -2.680486797 0.982664407 38 -1.168865724 -2.680486797 39 -2.487771633 -1.168865724 40 -1.425006274 -2.487771633 41 -0.841282969 -1.425006274 42 1.165274871 -0.841282969 43 -0.880074853 1.165274871 44 0.071340004 -0.880074853 45 -1.582361390 0.071340004 46 -0.005314667 -1.582361390 47 1.751428822 -0.005314667 48 -0.866650048 1.751428822 49 0.628118565 -0.866650048 50 -0.209846084 0.628118565 51 0.911865476 -0.209846084 52 1.010299876 0.911865476 53 0.764982963 1.010299876 54 -0.842853856 0.764982963 55 -0.092280283 -0.842853856 56 0.732838756 -0.092280283 57 0.153420788 0.732838756 58 -1.677631505 0.153420788 59 0.778370669 -1.677631505 60 0.040049267 0.778370669 61 2.674974771 0.040049267 62 1.384872803 2.674974771 63 -0.055420129 1.384872803 64 -0.353609491 -0.055420129 65 0.720586730 -0.353609491 66 1.090827507 0.720586730 67 -1.050975933 1.090827507 68 -0.807760295 -1.050975933 69 -3.188566174 -0.807760295 70 0.501234970 -3.188566174 71 -2.956975032 0.501234970 72 0.915903044 -2.956975032 73 1.258789675 0.915903044 74 -1.816957268 1.258789675 75 0.790494510 -1.816957268 76 0.494969755 0.790494510 77 0.668349857 0.494969755 78 -1.443398580 0.668349857 79 0.235864108 -1.443398580 80 -0.077750665 0.235864108 81 -0.285131883 -0.077750665 82 -0.416239784 -0.285131883 83 0.580152412 -0.416239784 84 -2.475015566 0.580152412 85 -0.305974018 -2.475015566 86 0.298878394 -0.305974018 87 1.346393771 0.298878394 88 0.345750543 1.346393771 89 1.431071438 0.345750543 90 -1.412998214 1.431071438 91 -0.194489995 -1.412998214 92 -0.766788629 -0.194489995 93 -1.034268816 -0.766788629 94 -0.121383008 -1.034268816 95 -1.661964962 -0.121383008 96 -1.131227143 -1.661964962 97 -1.113538951 -1.131227143 98 1.144264408 -1.113538951 99 -2.564822457 1.144264408 100 0.009536128 -2.564822457 101 -0.147413645 0.009536128 102 1.013690785 -0.147413645 103 1.047713276 1.013690785 104 -1.271075578 1.047713276 105 -2.161302287 -1.271075578 106 0.167393287 -2.161302287 107 -1.224064046 0.167393287 108 0.249066878 -1.224064046 109 1.252655904 0.249066878 110 -2.737303214 1.252655904 111 1.348987194 -2.737303214 112 -0.114610095 1.348987194 113 1.395488295 -0.114610095 114 1.880884766 1.395488295 115 1.576781339 1.880884766 116 -1.585174093 1.576781339 117 1.607263632 -1.585174093 118 3.278072267 1.607263632 119 0.998267513 3.278072267 120 -0.062409676 0.998267513 121 1.263800424 -0.062409676 122 0.400720139 1.263800424 123 0.023984695 0.400720139 124 -0.648528250 0.023984695 125 0.686886552 -0.648528250 126 -0.724388870 0.686886552 127 1.190798719 -0.724388870 128 0.668268290 1.190798719 129 0.072702415 0.668268290 130 -0.774385296 0.072702415 131 -0.271502166 -0.774385296 132 1.463242514 -0.271502166 133 0.029409253 1.463242514 134 -0.136350551 0.029409253 135 -2.590432765 -0.136350551 136 -0.717399007 -2.590432765 137 -0.312344287 -0.717399007 138 -0.917559144 -0.312344287 139 -2.715506765 -0.917559144 140 1.756540838 -2.715506765 141 0.934764961 1.756540838 142 -0.336834960 0.934764961 143 1.514048217 -0.336834960 144 -0.522770314 1.514048217 145 -3.383933190 -0.522770314 146 0.847168859 -3.383933190 147 1.605040399 0.847168859 148 0.317867508 1.605040399 149 -0.450589675 0.317867508 150 0.195391219 -0.450589675 151 0.240695468 0.195391219 152 -0.408493103 0.240695468 153 3.106185971 -0.408493103 154 1.379340792 3.106185971 155 -0.797778916 1.379340792 156 0.557132971 -0.797778916 157 -1.174556306 0.557132971 158 0.568739384 -1.174556306 159 0.641283155 0.568739384 160 -2.494896754 0.641283155 161 0.728319446 -2.494896754 162 -0.622812412 0.728319446 163 0.441412556 -0.622812412 > 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() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7dtdx1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8dtdx1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/962ci1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/1062ci1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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="/var/www/html/rcomp/tmp/1192b51290533524.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
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="/var/www/html/rcomp/tmp/12ulat1290533524.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="/var/www/html/rcomp/tmp/13jm751290533524.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
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
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="/var/www/html/rcomp/tmp/14cdoq1290533524.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="/var/www/html/rcomp/tmp/15fe4w1290533524.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="/var/www/html/rcomp/tmp/16bnkn1290533524.tab") + } > try(system("convert tmp/1hjyo1290533524.ps tmp/1hjyo1290533524.png",intern=TRUE)) character(0) > try(system("convert tmp/2rsf91290533524.ps tmp/2rsf91290533524.png",intern=TRUE)) character(0) > try(system("convert tmp/3rsf91290533524.ps tmp/3rsf91290533524.png",intern=TRUE)) character(0) > try(system("convert tmp/4rsf91290533524.ps tmp/4rsf91290533524.png",intern=TRUE)) character(0) > try(system("convert tmp/5rsf91290533524.ps tmp/5rsf91290533524.png",intern=TRUE)) character(0) > try(system("convert tmp/621ec1290533524.ps tmp/621ec1290533524.png",intern=TRUE)) character(0) > try(system("convert tmp/7dtdx1290533524.ps tmp/7dtdx1290533524.png",intern=TRUE)) character(0) > try(system("convert tmp/8dtdx1290533524.ps tmp/8dtdx1290533524.png",intern=TRUE)) character(0) > try(system("convert tmp/962ci1290533524.ps tmp/962ci1290533524.png",intern=TRUE)) character(0) > try(system("convert tmp/1062ci1290533524.ps tmp/1062ci1290533524.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.230 1.737 10.086