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17	7	7	10	14	22	3	1
19	8	8	11	21	39	3	0
18	8	10	11	20	40	4	0
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17	8	5	7	20	38	4	0
19	9	7	12	18	39	5	0
19	8	8	11	18	39	4	0
12	10	9	12	17	38	4	1
15	7	8	12	15	31	3	1
16	10	8	12	18	34	3	0
16	8	8	11	16	32	4	0
14	8	10	12	17	37	4	1
15	6	10	10	19	36	4	0
16	7	8	12	21	38	4	1
14	9	10	10	15	29	3	1
18	9	9	15	21	33	3	0
15	8	7	12	16	35	4	1
18	8	8	15	18	34	4	0
19	10	10	13	18	45	5	0
15	7	4	12	17	30	4	1
17	7	9	12	18	33	4	0
9	7	5	9	16	30	4	1
18	6	4	12	15	40	4	1
17	9	9	8	15	34	5	0
15	7	6	10	19	31	4	1
13	8	8	11	14	27	3	0
17	10	10	14	19	33	4	0
14	9	9	14	19	42	5	1
15	8	8	11	15	36	4	1
14	8	7	11	17	33	4	1
17	10	10	11	21	42	5	0
14	8	6	10	13	33	4	1
13	4	9	8	12	21	1	0
19	6	5	12	15	43	5	0
15	7	4	10	19	34	4	0
14	7	8	12	19	32	3	0
11	3	2	8	14	34	3	1
16	8	10	10	18	28	3	0
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12	4	7	11	12	29	2	0
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15	7	10	12	20	33	4	1
8	6	7	9	16	42	4	0
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14	7	7	8	16	33	4	0
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17	8	8	13	20	36	4	0
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15	9	9	12	15	35	4	0
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14	6	7	11	18	34	4	1
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16	7	10	14	19	37	4	0
15	9	9	14	18	38	4	0
16	9	9	12	20	36	5	0
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9	6	4	7	10	38	4	0
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16	8	8	11	20	28	3	0
16	9	8	11	19	30	3	0
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10	5	6	6	13	27	2	1
9	5	5	8	15	31	3	1
17	9	5	11	19	35	4	1
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9	6	4	7	11	27	2	0
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10	4	4	10	13	25	3	0
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17	8	9	12	20	31	4	0
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10	6	8	8	10	31	3	1
9	7	6	6	12	26	3	0
15	8	6	13	19	35	4	0
14	8	10	11	19	32	4	1
16	7	8	9	21	36	4	1
17	7	10	11	21	37	4	1
18	8	8	14	17	34	5	1
14	8	7	9	19	33	3	0
17	9	7	13	21	35	4	1
14	9	8	12	15	31	4	0
15	9	8	12	14	38	3	0
14	8	7	11	15	36	4	1
10	2	2	9	13	32	3	0
9	8	5	7	14	28	3	0
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13	8	5	12	19	31	4	0
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18	10	10	11	19	33	4	0
15	8	8	11	18	36	4	0
14	10	7	13	21	36	4	0
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15	8	5	11	15	28	4	1
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16	9	10	13	18	34	4	0
11	8	5	12	13	31	4	0
14	5	5	9	9	44	5	0
17	7	8	15	21	36	3	1
16	8	10	12	19	36	4	0
16	7	7	14	13	34	4	0
11	5	2	7	15	32	3	1
16	10	6	11	18	36	4	0
13	6	3	10	16	38	4	1
7	6	6	7	10	28	4	0
13	5	4	11	12	37	4	1
14	7	4	11	18	32	4	0
14	8	8	10	17	36	4	0
9	8	7	9	15	30	3	1
15	4	5	11	16	38	3	0
16	9	9	12	19	37	4	1
11	4	5	10	15	33	3	1
20	10	6	13	24	43	5	0
14	6	5	10	15	26	3	0
9	6	2	10	14	33	3	1
16	8	8	12	16	34	4	1
13	8	7	12	16	36	4	1
15	8	9	12	20	36	4	1
15	8	9	12	20	36	4	1
15	8	9	12	20	36	4	1
15	8	10	12	20	39	4	1
14	7	6	7	14	33	3	0
15	7	9	14	22	35	4	0
13	8	9	9	16	25	3	0
12	10	6	8	9	26	2	0
17	10	10	13	14	35	5	1
8	3	5	6	11	16	2	0
17	8	9	12	23	40	4	1
10	2	4	8	10	14	2	0
9	4	2	6	10	22	2	1
9	4	3	6	8	21	2	1
15	9	9	12	21	38	4	1
14	10	10	10	18	38	4	0
12	6	6	9	15	27	3	0
16	10	9	11	20	40	4	0
19	10	8	14	17	40	5	0
6	3	2	6	5	19	1	1
11	9	6	9	14	29	4	0
16	9	9	11	19	37	5	1
12	6	6	9	15	27	3	0
12	5	4	8	12	26	2	0
8	4	3	6	10	24	2	1
11	4	3	10	11	29	3	0
8	6	4	6	15	26	3	0
12	6	6	9	15	27	3	0
16	8	8	12	20	35	4	0
18	8	6	12	20	39	4	0
16	5	7	13	20	38	3	0
15	7	8	12	19	36	4	0
20	6	3	12	16	37	3	0
10	10	10	10	21	36	4	1
15	8	8	9	22	32	4	0
14	8	6	12	17	33	4	0
14	9	10	14	21	39	5	0
8	5	8	6	19	34	3	1
19	10	10	13	23	39	4	0
17	8	7	12	21	36	4	0
18	9	10	13	22	33	4	0
10	8	6	6	11	30	3	1
15	7	7	12	20	39	3	1
16	10	9	10	18	37	4	1
12	10	6	9	16	37	5	1
13	9	7	12	18	35	4	0
10	4	6	7	13	32	4	1
14	4	4	10	17	36	3	0
15	8	6	11	20	36	4	0
20	9	8	15	20	41	5	0
9	10	9	10	15	36	3	0
12	8	8	12	18	37	3	1
13	5	6	10	15	29	3	1
16	10	6	12	19	39	4	0
12	8	10	11	19	37	4	1
14	7	8	11	19	32	3	0
15	8	8	12	20	36	4	0
19	8	7	15	20	43	5	0
16	9	4	12	16	30	5	1
16	8	9	11	18	33	4	1
14	6	8	9	17	28	4	0
14	8	10	11	18	30	4	0
14	8	8	11	13	28	2	1
13	5	6	9	20	39	4	0
18	9	7	15	21	34	5	0
15	8	8	12	17	34	4	1
15	8	5	9	19	29	4	1
15	8	10	12	20	32	3	1
13	6	2	12	15	33	2	1
14	6	6	9	15	27	3	1
15	9	7	9	19	35	4	0
14	8	5	11	18	38	4	0
19	9	8	12	22	40	4	0
16	10	7	12	20	34	4	0
16	8	7	12	18	34	4	1
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10	7	7	6	15	39	4	1
11	7	6	11	17	34	2	0
13	10	10	12	16	39	5	0
14	8	6	9	17	26	3	0
11	7	5	11	15	30	2	0
11	10	8	9	17	34	4	0
16	7	8	10	18	34	3	0
9	7	5	10	16	29	5	1
16	9	8	9	18	41	4	1
19	9	10	12	22	43	4	1
13	8	7	11	16	31	4	1
15	6	7	9	16	33	3	1
14	8	7	9	20	34	4	1
15	9	7	12	18	30	2	0
11	2	2	6	16	23	3	1
14	6	4	10	16	29	2	1
15	8	6	12	20	35	4	0
17	8	7	11	21	40	4	0
16	7	9	14	18	27	2	1
13	8	9	8	15	30	3	1
15	6	4	9	18	27	3	1
14	10	9	10	18	29	3	1
15	10	9	10	20	33	4	1
14	10	8	10	18	32	3	1
12	8	7	11	16	33	4	1
12	8	9	10	19	36	3	0
15	7	7	12	20	34	4	0
17	10	6	14	22	45	5	0
13	5	7	10	18	30	3	1
5	3	2	8	8	22	4	0
7	2	3	8	13	24	1	0
10	3	4	7	13	25	2	0
15	4	5	11	18	26	4	0
9	2	2	6	12	27	3	1
9	6	6	9	16	27	3	1
15	8	8	12	21	35	3	1
14	8	5	12	20	36	4	1
11	5	4	12	18	32	3	1
18	10	10	9	22	35	4	0
20	9	10	15	23	35	5	0
20	8	10	15	23	36	5	0
16	9	9	13	21	37	4	0
15	8	5	9	16	33	3	0
14	5	5	12	14	25	3	1
13	7	7	9	18	35	4	0
18	9	10	15	22	37	4	0
14	8	9	11	20	36	3	1
12	4	8	11	18	35	4	0
9	7	8	6	12	29	4	0
19	8	8	14	17	35	4	0
13	7	8	11	15	31	3	1
12	7	8	8	18	30	4	0
14	9	7	10	18	37	4	1
6	6	6	10	15	36	4	0
14	7	8	9	16	35	4	1
11	4	2	8	15	32	2	1
11	6	5	9	16	34	4	0
14	10	4	10	19	37	5	1
12	9	9	11	19	36	5	0
19	10	10	14	23	39	5	0
13	8	6	12	20	37	4	1
14	4	4	9	18	31	3	1
17	8	10	13	21	40	5	0
12	5	6	8	19	38	3	1
16	8	7	12	18	35	4	0
15	9	7	14	19	38	4	0
15	8	8	9	17	32	4	1
15	4	6	10	21	41	5	0
16	8	5	12	19	28	4	1
15	10	6	12	24	40	5	0
12	6	7	9	12	25	3	1
13	7	6	9	15	28	3	1
14	10	9	12	18	37	4	0
17	9	9	15	19	37	4	0
14	8	7	12	22	40	4	0
14	3	6	11	19	26	2	1
14	8	7	8	16	30	3	1
15	7	7	11	19	32	4	1
11	7	8	11	18	31	4	1
11	8	7	10	18	28	3	1
16	8	8	12	19	34	4	0
12	7	7	9	21	39	4	1
12	7	4	11	19	33	4	0
19	9	10	15	22	43	5	1
18	9	8	14	23	37	4	0
16	9	8	6	17	31	3	1
16	4	2	9	18	31	4	0
13	6	6	9	19	34	4	1
11	6	4	8	15	32	3	0
10	6	4	7	14	27	3	1
14	8	9	10	18	34	5	1
14	3	2	6	17	28	2	1
14	8	6	9	19	32	3	1
16	8	7	9	16	39	4	0
10	6	4	7	14	28	3	0
16	10	10	11	20	39	4	1
7	2	3	9	16	32	2	1
16	9	7	12	18	36	4	0
15	6	4	9	16	31	3	0
17	6	8	10	21	39	5	1
11	5	4	11	16	23	3	1
11	4	5	7	14	25	2	1
10	7	6	12	16	32	3	1
13	5	5	8	19	32	4	0
14	8	9	13	19	36	3	0
13	6	6	11	19	39	4	1
13	9	8	11	18	31	2	0
12	6	4	12	16	32	4	1
10	4	4	11	14	28	4	0
15	7	8	12	19	34	4	1
6	2	4	3	11	28	2	0
15	8	10	10	18	38	4	0
15	9	8	13	18	35	4	0
11	6	5	10	16	32	4	1
14	5	3	6	20	26	3	0
14	7	7	11	18	32	3	0
16	8	6	12	20	28	3	0
12	4	5	9	16	31	3	1
15	9	5	10	18	33	3	0
20	9	9	15	19	38	5	1
12	9	2	9	19	38	4	0
9	7	7	6	15	36	4	1
13	5	7	9	17	31	2	0
15	7	5	15	21	36	4	1
19	9	9	15	24	43	4	0
11	8	4	9	16	37	3	0
11	6	5	11	13	28	3	0
17	9	9	9	21	35	4	0
15	8	7	11	16	34	4	0
14	7	6	10	17	40	3	0
15	7	8	9	17	31	4	1
11	7	7	6	18	41	4	1
12	8	6	12	18	35	4	1
15	10	8	13	23	38	4	0
16	6	6	12	20	37	4	1
16	6	7	12	20	31	3	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')