Paper Multiple regression normaliteit na verwijderen gecorreleerde verklare...

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12.9 12 1 0 11 18 13 149 18 68
12.2 8 1 1 19 23 8 139 31 39
12.8 11 1 0 16 22 14 148 39 32
7.4 13 1 1 24 22 16 158 46 62
6.7 11 1 1 15 19 14 128 31 33
12.6 10 1 1 17 25 13 224 67 52
14.8 7 1 0 19 28 15 159 35 62
13.3 10 1 1 19 16 13 105 52 77
11.1 15 1 1 28 28 20 159 77 76
8.2 12 1 1 26 21 17 167 37 41
11.4 12 1 1 15 22 15 165 32 48
6.4 10 1 1 26 24 16 159 36 63
10.6 10 1 1 16 24 12 119 38 30
12 14 1 0 24 26 17 176 69 78
6.3 6 1 0 25 28 11 54 21 19
11.3 12 0 0 22 24 16 91 26 31
11.9 14 1 1 15 20 16 163 54 66
9.3 11 1 0 21 26 15 124 36 35
9.6 8 0 1 22 21 13 137 42 42
10 12 1 0 27 28 14 121 23 45
6.4 15 1 1 26 27 19 153 34 21
13.8 13 1 1 26 23 16 148 112 25
10.8 11 1 0 22 24 17 221 35 44
13.8 12 1 1 21 24 10 188 47 69
11.7 7 1 1 22 22 15 149 47 54
10.9 11 1 1 20 21 14 244 37 74
16.1 7 0 1 21 25 14 148 109 80
13.4 12 0 0 20 20 16 92 24 42
9.9 12 1 1 22 21 15 150 20 61
11.5 13 1 0 21 26 17 153 22 41
8.3 9 1 0 8 23 14 94 23 46
11.7 11 1 0 22 21 16 156 32 39
9 12 1 1 20 27 15 132 30 34
9.7 15 1 1 24 25 16 161 92 51
10.8 12 1 1 17 23 16 105 43 42
10.3 6 1 1 20 25 10 97 55 31
10.4 5 1 0 23 23 8 151 16 39
12.7 13 0 1 20 19 17 131 49 20
9.3 11 1 1 22 22 14 166 71 49
11.8 6 1 0 19 24 10 157 43 53
5.9 12 1 1 15 19 14 111 29 31
11.4 10 1 1 20 21 12 145 56 39
13 6 1 1 22 27 16 162 46 54
10.8 12 1 1 17 25 16 163 19 49
12.3 11 0 1 14 25 16 59 23 34
11.3 6 1 0 24 23 8 187 59 46
11.8 12 1 1 17 17 16 109 30 55
7.9 12 0 1 23 28 15 90 61 42
12.7 8 1 0 25 25 8 105 7 50
12.3 10 0 1 16 20 13 83 38 13
11.6 11 0 1 18 25 14 116 32 37
6.7 7 0 1 20 21 13 42 16 25
10.9 12 1 1 18 24 16 148 19 30
12.1 13 0 1 23 28 19 155 22 28
13.3 14 1 1 24 20 19 125 48 45
10.1 12 1 1 23 19 14 116 23 35
5.7 6 0 0 13 24 15 128 26 28
14.3 14 1 1 20 21 13 138 33 41
8 10 0 0 20 24 10 49 9 6
13.3 12 0 1 19 23 16 96 24 45
9.3 11 1 1 22 18 15 164 34 73
12.5 10 1 0 22 27 11 162 48 17
7.6 7 1 0 15 25 9 99 18 40
15.9 12 1 1 17 20 16 202 43 64
9.2 7 1 0 19 21 12 186 33 37
9.1 12 0 1 20 23 12 66 28 25
11.1 12 1 0 22 27 14 183 71 65
13 10 1 1 21 24 14 214 26 100
14.5 10 1 1 21 27 13 188 67 28
12.2 12 0 0 16 24 15 104 34 35
12.3 12 1 0 20 23 17 177 80 56
11.4 12 1 0 21 24 14 126 29 29
8.8 8 0 0 20 21 11 76 16 43
14.6 10 0 1 23 23 9 99 59 59
12.6 5 1 0 18 27 7 139 32 50
13 10 1 0 16 25 15 162 43 59
12.6 12 0 1 17 19 12 108 38 27
13.2 11 1 0 24 24 15 159 29 61
9.9 9 0 0 13 25 14 74 36 28
7.7 12 1 1 19 23 16 110 32 51
10.5 11 0 0 20 23 14 96 35 35
13.4 10 0 0 22 25 13 116 21 29
10.9 12 0 0 19 26 16 87 29 48
4.3 10 0 1 21 26 13 97 12 25
10.3 9 0 0 15 16 16 127 37 44
11.8 11 0 1 21 23 16 106 37 64
11.2 12 0 1 24 26 16 80 47 32
11.4 7 0 0 22 25 10 74 51 20
8.6 11 0 0 20 23 12 91 32 28
13.2 12 0 0 21 26 12 133 21 34
12.6 6 0 1 19 22 12 74 13 31
5.6 9 0 1 14 20 12 114 14 26
9.9 15 0 1 25 27 19 140 -2 58
8.8 10 0 0 11 20 14 95 20 23
7.7 11 0 1 17 22 13 98 24 21
9 12 0 0 22 24 16 121 11 21
7.3 12 0 1 20 21 15 126 23 33
11.4 12 0 1 22 24 12 98 24 16
13.6 11 0 1 15 26 8 95 14 20
7.9 9 0 1 23 24 10 110 52 37
10.7 11 0 1 20 24 16 70 15 35
10.3 12 0 0 22 27 16 102 23 33
8.3 12 0 1 16 25 10 86 19 27
9.6 14 0 1 25 27 18 130 35 41
14.2 8 0 1 18 19 12 96 24 40
8.5 10 0 0 19 22 16 102 39 35
13.5 9 0 0 25 22 10 100 29 28
4.9 10 0 0 21 25 14 94 13 32
6.4 9 0 0 22 23 12 52 8 22
9.6 10 0 0 21 24 11 98 18 44
11.6 12 0 0 22 24 15 118 24 27
11.1 11 0 1 23 23 7 99 19 17
4.35 9 1 1 20 22 16 48 23 12
12.7 11 1 1 6 24 16 50 16 45
18.1 12 1 1 15 19 16 150 33 37
17.85 12 1 1 18 25 16 154 32 37
16.6 7 0 0 24 26 12 109 37 108
12.6 12 0 1 22 18 15 68 14 10
17.1 12 1 1 21 24 14 194 52 68
19.1 12 1 0 23 28 15 158 75 72
16.1 10 1 1 20 23 16 159 72 143
13.35 15 1 0 20 19 13 67 15 9
18.4 10 1 0 18 19 10 147 29 55
14.7 15 1 1 25 27 17 39 13 17
10.6 10 1 1 16 24 15 100 40 37
12.6 15 1 1 20 26 18 111 19 27
16.2 9 1 1 14 21 16 138 24 37
13.6 15 1 1 22 25 20 101 121 58
18.9 12 0 1 26 28 16 131 93 66
14.1 13 1 1 20 19 17 101 36 21
14.5 12 1 1 17 20 16 114 23 19
16.15 12 1 0 22 26 15 165 85 78
14.75 8 1 1 22 27 13 114 41 35
14.8 9 1 1 20 23 16 111 46 48
12.45 15 1 1 17 18 16 75 18 27
12.65 12 1 1 22 23 16 82 35 43
17.35 12 1 1 17 21 17 121 17 30
8.6 15 1 1 22 23 20 32 4 25
18.4 11 1 0 21 22 14 150 28 69
16.1 12 1 1 25 21 17 117 44 72
11.6 6 0 1 11 14 6 71 10 23
17.75 14 1 1 19 24 16 165 38 13
15.25 12 1 1 24 26 15 154 57 61
17.65 12 1 1 17 24 16 126 23 43
16.35 12 1 0 22 22 16 149 36 51
17.65 11 1 0 17 20 14 145 22 67
13.6 12 1 1 26 20 16 120 40 36
14.35 12 1 0 20 18 16 109 31 44
14.75 12 1 0 19 18 16 132 11 45
18.25 12 1 1 21 25 14 172 38 34
9.9 8 1 0 24 28 14 169 24 36
16 8 1 1 21 23 16 114 37 72
18.25 12 1 1 19 20 16 156 37 39
16.85 12 1 0 13 22 15 172 22 43
14.6 11 0 1 24 27 16 68 15 25
13.85 10 0 1 28 24 16 89 2 56
18.95 11 1 1 27 23 18 167 43 80
15.6 12 1 0 22 20 15 113 31 40
14.85 13 0 0 23 22 16 115 29 73
11.75 12 0 0 19 21 16 78 45 34
18.45 12 0 0 18 24 16 118 25 72
15.9 10 0 1 23 26 17 87 4 42
17.1 10 1 0 21 24 14 173 31 61
16.1 11 1 1 22 18 18 2 -4 23
19.9 8 0 0 17 17 9 162 66 74
10.95 12 0 1 15 23 15 49 61 16
18.45 9 0 0 21 21 14 122 32 66
15.1 12 0 1 20 21 15 96 31 9
15 9 0 0 26 24 13 100 39 41
11.35 11 0 0 19 22 16 82 19 57
15.95 15 0 1 28 24 20 100 31 48
18.1 8 0 0 21 24 14 115 36 51
14.6 8 0 1 19 24 12 141 42 53
15.4 11 1 1 22 23 15 165 21 29
15.4 11 1 1 21 21 15 165 21 29
17.6 11 0 1 20 24 15 110 25 55
13.35 13 1 1 19 19 16 118 32 54
19.1 7 1 0 11 19 11 158 26 43
15.35 12 0 1 17 23 16 146 28 51
7.6 8 1 0 19 25 7 49 32 20
13.4 8 0 0 20 24 11 90 41 79
13.9 4 0 0 17 21 9 121 29 39
19.1 11 1 1 21 18 15 155 33 61
15.25 10 0 0 21 23 16 104 17 55
12.9 7 0 1 12 20 14 147 13 30
16.1 12 0 0 23 23 15 110 32 55
17.35 11 0 0 22 23 13 108 30 22
13.15 9 0 0 22 23 13 113 34 37
12.15 10 0 0 21 23 12 115 59 2
12.6 8 0 1 20 27 16 61 13 38
10.35 8 0 1 18 19 14 60 23 27
15.4 11 0 1 21 25 16 109 10 56
9.6 12 0 1 24 25 14 68 5 25
18.2 10 0 0 22 21 15 111 31 39
13.6 10 0 0 20 25 10 77 19 33
14.85 12 0 1 17 17 16 73 32 43
14.75 8 1 0 19 22 14 151 30 57
14.1 11 0 0 16 23 16 89 25 43
14.9 8 0 0 19 27 12 78 48 23
16.25 10 0 0 23 27 16 110 35 44
19.25 14 1 1 8 5 16 220 67 54
13.6 9 0 1 22 19 15 65 15 28
13.6 9 1 0 23 24 14 141 22 36
15.65 10 0 0 15 23 16 117 18 39
12.75 13 1 1 17 28 11 122 33 16
14.6 12 0 0 21 25 15 63 46 23
9.85 13 1 1 25 27 18 44 24 40
12.65 8 0 1 18 16 13 52 14 24
19.2 3 0 0 20 25 7 131 12 78
16.6 8 0 1 21 26 7 101 38 57
11.2 12 0 1 21 24 17 42 12 37
15.25 11 1 1 24 23 18 152 28 27
11.9 9 1 0 22 24 15 107 41 61
13.2 12 0 0 22 27 8 77 12 27
16.35 12 1 0 23 25 13 154 31 69
12.4 12 1 1 17 19 13 103 33 34
15.85 10 0 1 15 19 15 96 34 44
18.15 13 1 1 22 24 18 175 21 34
11.15 9 0 1 19 20 16 57 20 39
15.65 12 0 0 18 21 14 112 44 51
17.75 11 1 0 21 28 15 143 52 34
7.65 14 0 0 20 26 19 49 7 31
12.35 11 1 1 19 19 16 110 29 13
15.6 9 1 1 19 23 12 131 11 12
19.3 12 1 0 16 23 16 167 26 51
15.2 8 0 0 18 21 11 56 24 24
17.1 15 1 0 23 26 16 137 7 19
15.6 12 0 1 22 25 15 86 60 30
18.4 14 1 1 23 25 19 121 13 81
19.05 12 1 0 20 24 15 149 20 42
18.55 9 1 0 24 23 14 168 52 22
19.1 9 1 0 25 22 14 140 28 85
13.1 13 0 1 25 27 17 88 25 27
12.85 13 1 1 20 26 16 168 39 25
9.5 15 1 1 23 23 20 94 9 22
4.5 11 1 1 21 22 16 51 19 19
11.85 7 0 0 23 26 9 48 13 14
13.6 10 1 1 23 22 13 145 60 45
11.7 11 1 1 11 17 15 66 19 45
12.4 14 0 1 21 25 19 85 34 28
13.35 14 1 0 27 22 16 109 14 51
11.4 13 0 0 19 28 17 63 17 41
14.9 12 0 1 21 22 16 102 45 31
19.9 8 0 0 16 21 9 162 66 74
11.2 13 0 1 21 24 11 86 48 19
14.6 9 0 1 22 26 14 114 29 51
17.6 12 1 0 16 26 19 164 -2 73
14.05 13 1 1 18 24 13 119 51 24
16.1 11 1 0 23 27 14 126 2 61
13.35 11 1 1 24 22 15 132 24 23
11.85 13 1 1 20 23 15 142 40 14
11.95 12 1 0 20 22 14 83 20 54
14.75 12 0 1 18 23 16 94 19 51
15.15 10 0 0 4 15 17 81 16 62
13.2 9 1 1 14 20 12 166 20 36
16.85 10 0 0 22 22 15 110 40 59
7.85 13 0 1 17 25 17 64 27 24
7.7 13 1 0 23 27 15 93 25 26
12.6 9 0 0 20 24 10 104 49 54
7.85 11 0 1 18 21 16 105 39 39
10.95 12 0 1 19 17 15 49 61 16
12.35 8 0 0 20 26 11 88 19 36
9.95 12 0 1 15 20 16 95 67 31
14.9 12 0 1 24 22 16 102 45 31
16.65 12 0 0 21 24 16 99 30 42
13.4 9 0 1 19 23 14 63 8 39
13.95 12 0 0 19 22 14 76 19 25
15.7 12 0 0 27 28 16 109 52 31
16.85 11 0 1 23 21 16 117 22 38
10.95 12 0 1 23 24 18 57 17 31
15.35 6 0 0 20 28 14 120 33 17
12.2 7 0 1 17 25 20 73 34 22
15.1 10 0 0 21 24 15 91 22 55
17.75 12 0 0 23 24 16 108 30 62
15.2 10 0 1 22 21 16 105 25 51
14.6 12 1 0 16 20 16 117 38 30
16.65 9 0 0 20 26 12 119 26 49
8.1 3 0 1 16 16 8 31 13 16

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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'
}
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}