• QR Code (size=2)
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7	7	10	17	14	22
8	8	11	19	21	39
8	10	11	18	20	40
10	10	13	17	20	34
8	5	7	17	20	38
9	7	12	19	18	39
8	8	11	19	18	39
10	9	12	12	17	38
7	8	12	15	15	31
10	8	12	16	18	34
8	8	11	16	16	32
8	10	12	14	17	37
6	10	10	15	19	36
7	8	12	16	21	38
9	10	10	14	15	29
9	9	15	18	21	33
8	7	12	15	16	35
8	8	15	18	18	34
10	10	13	19	18	45
7	4	12	15	17	30
7	9	12	17	18	33
7	5	9	9	16	30
6	4	12	18	15	40
9	9	8	17	15	34
7	6	10	15	19	31
8	8	11	13	14	27
10	10	14	17	19	33
9	9	14	14	19	42
8	8	11	15	15	36
8	7	11	14	17	33
10	10	11	17	21	42
8	6	10	14	13	33
4	9	8	13	12	21
6	5	12	19	15	43
7	4	10	15	19	34
7	8	12	14	19	32
3	2	8	11	14	34
8	10	10	16	18	28
8	5	9	13	11	30
6	8	12	15	17	27
10	8	15	17	18	29
8	8	13	15	13	40
4	7	11	12	12	29
8	10	12	15	17	41
7	10	12	15	20	33
6	7	9	8	16	42
9	10	14	16	22	39
10	10	10	12	16	35
9	8	14	13	23	33
7	7	12	16	20	33
10	8	15	16	23	44
7	6	9	8	13	34
10	10	14	14	18	30
9	9	10	15	18	30
7	9	11	16	17	35
10	7	11	16	17	39
8	8	12	17	18	34
8	6	11	18	21	39
6	7	10	9	13	25
9	9	13	19	19	39
7	7	8	14	16	33
8	7	9	14	17	34
8	7	9	15	18	36
9	10	15	19	18	34
5	4	8	12	12	31
9	10	13	17	19	35
6	3	10	16	16	34
8	8	13	17	20	36
10	10	15	15	21	40
7	8	10	11	18	31
5	6	9	8	13	33
4	6	12	16	17	28
9	10	12	20	20	42
10	10	12	20	21	38
8	7	12	13	19	35
6	8	10	11	15	34
6	7	9	15	14	28
9	9	12	15	15	35
3	2	12	14	16	25
7	10	12	16	19	39
9	10	8	15	17	25
7	7	11	15	17	32
8	8	10	16	15	35
9	8	15	19	19	41
9	10	13	20	21	34
8	8	11	14	19	33
9	8	12	16	18	32
6	7	11	14	18	34
7	8	11	11	15	25
8	8	14	16	19	38
7	10	14	16	19	37
9	9	14	15	18	38
9	9	12	16	20	36
5	4	10	12	18	39
6	5	12	13	12	31
8	10	11	11	15	40
10	9	15	20	17	34
5	6	9	11	15	33
8	8	11	14	17	32
8	8	14	16	20	33
10	9	10	15	11	32
7	8	12	13	14	28
9	4	10	15	14	32
8	5	12	13	12	34
8	10	13	17	19	36
10	10	13	18	22	38
9	9	12	14	16	31
9	9	12	13	15	36
6	6	9	12	15	27
8	7	10	17	18	31
5	4	7	6	12	28
3	6	12	9	17	30
6	4	12	15	10	29
6	4	12	15	10	29
10	10	13	17	18	31
9	6	12	19	16	35
9	9	15	20	22	42
5	7	8	10	12	28
6	4	7	9	10	38
7	8	9	15	20	34
8	8	11	16	20	28
9	8	11	16	19	30
3	7	6	9	10	26
5	6	6	10	13	27
5	5	8	9	15	31
9	5	11	17	19	35
10	7	12	17	17	33
7	4	13	19	15	34
8	8	9	10	12	30
6	7	10	12	14	28
5	6	9	9	13	30
8	8	10	11	15	29
7	5	12	17	20	32
5	3	7	9	12	34
6	5	13	14	16	34
10	10	10	19	15	35
10	7	10	17	17	40
6	4	11	13	15	34
4	2	8	11	12	28
8	6	12	14	17	35
5	3	4	7	11	31
7	8	13	17	16	33
10	9	13	16	16	36
8	5	10	12	15	30
7	6	12	10	17	27
2	2	6	10	7	30
7	6	8	8	14	25
9	10	13	18	21	39
8	8	12	15	20	36
5	10	10	18	15	31
8	8	10	14	13	33
6	8	10	16	20	30
7	6	8	11	15	31
10	9	11	16	16	32
8	9	12	17	19	33
10	9	12	20	16	43
9	8	12	14	19	35
8	8	11	16	17	36
10	10	12	17	19	42
4	3	10	11	14	31
6	6	12	13	16	26
9	9	13	11	16	38
4	3	10	8	14	27
6	4	7	9	11	27
7	5	11	9	17	31
9	9	10	12	20	32
8	8	12	15	20	36
6	5	11	18	17	36
4	4	10	10	13	25
8	5	12	15	20	33
8	7	11	16	17	32
9	7	14	18	16	40
6	8	12	15	19	36
5	8	12	17	20	36
5	6	10	17	17	35
8	7	11	14	14	31
8	9	12	17	20	31
9	9	12	13	19	36
7	6	12	16	18	36
9	9	8	12	17	37
8	9	11	17	17	31
6	8	8	10	10	31
7	6	6	9	12	26
8	6	13	15	19	35
8	10	11	14	19	32
7	8	9	16	21	36
7	10	11	17	21	37
8	8	14	18	17	34
8	7	9	14	19	33
9	7	13	17	21	35
9	8	12	14	15	31
9	8	12	15	14	38
8	7	11	14	15	36
2	2	9	10	13	32
8	5	7	9	14	28
8	7	10	12	14	33
8	5	12	13	19	31
7	5	11	14	17	34
10	10	11	18	19	33
8	8	11	15	18	36
10	7	13	14	21	36
5	6	11	10	12	29
4	6	8	9	15	31
10	5	11	17	19	35
8	7	10	12	16	31
7	8	12	16	19	35
5	7	8	11	16	36
7	8	13	13	18	35
9	9	10	12	18	38
8	5	11	15	15	28
8	5	11	15	15	28
2	5	8	10	11	28
9	10	13	16	18	34
8	5	12	11	13	31
5	5	9	14	9	44
7	8	15	17	21	36
8	10	12	16	19	36
7	7	14	16	13	34
5	2	7	11	15	32
10	6	11	16	18	36
6	3	10	13	16	38
6	6	7	7	10	28
5	4	11	13	12	37
7	4	11	14	18	32
8	8	10	14	17	36
8	7	9	9	15	30
4	5	11	15	16	38
9	9	12	16	19	37
4	5	10	11	15	33
10	6	13	20	24	43
6	5	10	14	15	26
6	2	10	9	14	33
8	8	12	16	16	34
8	7	12	13	16	36
8	9	12	15	20	36
8	9	12	15	20	36
8	9	12	15	20	36
8	10	12	15	20	39
7	6	7	14	14	33
7	9	14	15	22	35
8	9	9	13	16	25
10	6	8	12	9	26
10	10	13	17	14	35
3	5	6	8	11	16
8	9	12	17	23	40
2	4	8	10	10	14
4	2	6	9	10	22
4	3	6	9	8	21
9	9	12	15	21	38
10	10	10	14	18	38
6	6	9	12	15	27
10	9	11	16	20	40
10	8	14	19	17	40
3	2	6	6	5	19
9	6	9	11	14	29
9	9	11	16	19	37
6	6	9	12	15	27
5	4	8	12	12	26
4	3	6	8	10	24
4	3	10	11	11	29
6	4	6	8	15	26
6	6	9	12	15	27
8	8	12	16	20	35
8	6	12	18	20	39
5	7	13	16	20	38
7	8	12	15	19	36
6	3	12	20	16	37
10	10	10	10	21	36
8	8	9	15	22	32
8	6	12	14	17	33
9	10	14	14	21	39
5	8	6	8	19	34
10	10	13	19	23	39
8	7	12	17	21	36
9	10	13	18	22	33
8	6	6	10	11	30
7	7	12	15	20	39
10	9	10	16	18	37
10	6	9	12	16	37
9	7	12	13	18	35
4	6	7	10	13	32
4	4	10	14	17	36
8	6	11	15	20	36
9	8	15	20	20	41
10	9	10	9	15	36
8	8	12	12	18	37
5	6	10	13	15	29
10	6	12	16	19	39
8	10	11	12	19	37
7	8	11	14	19	32
8	8	12	15	20	36
8	7	15	19	20	43
9	4	12	16	16	30
8	9	11	16	18	33
6	8	9	14	17	28
8	10	11	14	18	30
8	8	11	14	13	28
5	6	9	13	20	39
9	7	15	18	21	34
8	8	12	15	17	34
8	5	9	15	19	29
8	10	12	15	20	32
6	2	12	13	15	33
6	6	9	14	15	27
9	7	9	15	19	35
8	5	11	14	18	38
9	8	12	19	22	40
10	7	12	16	20	34
8	7	12	16	18	34
8	10	12	12	14	26
7	7	6	10	15	39
7	6	11	11	17	34
10	10	12	13	16	39
8	6	9	14	17	26
7	5	11	11	15	30
10	8	9	11	17	34
7	8	10	16	18	34
7	5	10	9	16	29
9	8	9	16	18	41
9	10	12	19	22	43
8	7	11	13	16	31
6	7	9	15	16	33
8	7	9	14	20	34
9	7	12	15	18	30
2	2	6	11	16	23
6	4	10	14	16	29
8	6	12	15	20	35
8	7	11	17	21	40
7	9	14	16	18	27
8	9	8	13	15	30
6	4	9	15	18	27
10	9	10	14	18	29
10	9	10	15	20	33
10	8	10	14	18	32
8	7	11	12	16	33
8	9	10	12	19	36
7	7	12	15	20	34
10	6	14	17	22	45
5	7	10	13	18	30
3	2	8	5	8	22
2	3	8	7	13	24
3	4	7	10	13	25
4	5	11	15	18	26
2	2	6	9	12	27
6	6	9	9	16	27
8	8	12	15	21	35
8	5	12	14	20	36
5	4	12	11	18	32
10	10	9	18	22	35
9	10	15	20	23	35
8	10	15	20	23	36
9	9	13	16	21	37
8	5	9	15	16	33
5	5	12	14	14	25
7	7	9	13	18	35
9	10	15	18	22	37
8	9	11	14	20	36
4	8	11	12	18	35
7	8	6	9	12	29
8	8	14	19	17	35
7	8	11	13	15	31
7	8	8	12	18	30
9	7	10	14	18	37
6	6	10	6	15	36
7	8	9	14	16	35
4	2	8	11	15	32
6	5	9	11	16	34
10	4	10	14	19	37
9	9	11	12	19	36
10	10	14	19	23	39
8	6	12	13	20	37
4	4	9	14	18	31
8	10	13	17	21	40
5	6	8	12	19	38
8	7	12	16	18	35
9	7	14	15	19	38
8	8	9	15	17	32
4	6	10	15	21	41
8	5	12	16	19	28
10	6	12	15	24	40
6	7	9	12	12	25
7	6	9	13	15	28
10	9	12	14	18	37
9	9	15	17	19	37
8	7	12	14	22	40
3	6	11	14	19	26
8	7	8	14	16	30
7	7	11	15	19	32
7	8	11	11	18	31
8	7	10	11	18	28
8	8	12	16	19	34
7	7	9	12	21	39
7	4	11	12	19	33
9	10	15	19	22	43
9	8	14	18	23	37
9	8	6	16	17	31
4	2	9	16	18	31
6	6	9	13	19	34
6	4	8	11	15	32
6	4	7	10	14	27
8	9	10	14	18	34
3	2	6	14	17	28
8	6	9	14	19	32
8	7	9	16	16	39
6	4	7	10	14	28
10	10	11	16	20	39
2	3	9	7	16	32
9	7	12	16	18	36
6	4	9	15	16	31
6	8	10	17	21	39
5	4	11	11	16	23
4	5	7	11	14	25
7	6	12	10	16	32
5	5	8	13	19	32
8	9	13	14	19	36
6	6	11	13	19	39
9	8	11	13	18	31
6	4	12	12	16	32
4	4	11	10	14	28
7	8	12	15	19	34
2	4	3	6	11	28
8	10	10	15	18	38
9	8	13	15	18	35
6	5	10	11	16	32
5	3	6	14	20	26
7	7	11	14	18	32
8	6	12	16	20	28
4	5	9	12	16	31
9	5	10	15	18	33
9	9	15	20	19	38
9	2	9	12	19	38
7	7	6	9	15	36
5	7	9	13	17	31
7	5	15	15	21	36
9	9	15	19	24	43
8	4	9	11	16	37
6	5	11	11	13	28
9	9	9	17	21	35
8	7	11	15	16	34
7	6	10	14	17	40
7	8	9	15	17	31
7	7	6	11	18	41
8	6	12	12	18	35
10	8	13	15	23	38
6	6	12	16	20	37
6	7	12	16	20	31

par6 <- '12'
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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
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$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}
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('
',RC.texteval('reset_test_fitted'),'
',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('
',RC.texteval('reset_test_regressors'),'
',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('
',RC.texteval('reset_test_principal_components'),'
',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('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')