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

par6 <- '12'
par5 <- '0'
par4 <- '0'
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')