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• QR Code (size=6)

0.68	3.82	2.06	2.65 
1.03	3.78	1.40	2.66 
1.05	3.80	1.51	2.66 
1.92	3.89	1.11	2.65 
1.58	3.85	1.96	2.64 
1.21	3.83	2.13	2.63 
0.25	3.65	2.29	2.60 
-0.36	3.63	2.52	2.59 
-0.53	3.49	2.47	2.57 
-1.95	3.32	2.32	2.55 
-3.81	2.93	2.41	2.52 
-3.88	2.95	2.19	2.50 
-3.12	3.09	2.61	2.47 
-2.35	3.14	2.45	2.44 
-1.69	3.16	2.25	2.41 
-1.49	3.16	2.02	2.38 
-0.92	3.10	1.86	2.36 
-1.62	3.04	2.05	2.34 
-1.32	3.09	2.22	2.34 
-1.79	3.04	2.49	2.34 
-1.05	3.18	2.44	2.35 
-0.06	3.27	2.09	2.35 
0.35	3.28	1.58	2.36 
1.42	3.52	1.69	2.36 
1.9	3.53	2.25	2.37 
1.26	3.48	2.20	2.37 
0.15	3.11	1.89	2.37 
-0.41	2.94	1.53	2.36 
0.83	3.15	1.55	2.35 
2.07	3.35	1.26	2.34 
2.32	3.39	2.12	2.33 
2.81	0.00	2.51	2.30 
3.78	3.65	2.35	2.28 
2.86	3.61	1.97	2.26 
1.29	3.48	3.19	2.24 
1.21	3.39	3.37	2.23 
1.31	3.33	3.55	2.23 
0.98	3.26	3.44	2.23 
0.67	3.09	3.27	2.24 
-0.06	2.92	3.02	2.27 
-0.31	2.75	2.82	2.29 
-0.72	2.71	2.68	2.32 
0.46	2.80	2.58	2.36 
1.34	2.89	2.16	2.40 
1.38	3.04	1.88	2.45 
2.27	3.07	1.91	2.49 
2.99	3.34	1.35	2.53 
4.15	3.53	1.61	2.56 
4.49	3.56	1.29	2.55 
4.87	3.67	0.91	2.54 
4.84	3.62	1.03	2.51 
3.91	3.52	1.38	2.49 
2.62	3.54	2.51	2.47 
1.86	3.57	2.85	2.45 
2.03	3.58	2.67	2.45 
1.19	3.37	3.38	2.45 
0.78	3.28	3.19	2.45 
0.89	3.32	2.82	2.43 
0.46	3.25	2.84	2.42 
1.19	3.37	3.27	2.41 
0.87	3.34	3.59	2.40 
0.57	3.38	3.61	2.38 
1.57	3.53	3.65	2.38 
1.09	3.43	3.52	2.39 
0.77	3.37	3.24	2.40 
0.15	3.22	3.03	2.41 
0.32	3.32	2.48	2.42 
0.15	3.26	3.06	2.43 
-0.44	3.21	3.12	2.43 
-0.78	3.14	3.12	2.43 
-1.53	3.05	3.24	2.41 
-1.7	3.03	3.07	2.38 
0.67	2.99	2.07	2.36 
-0.73	2.83	1.14	2.33 
0.13	3.03	0.84	2.30 
1.09	3.14	1.87	2.28 
-0.18	3.02	1.64	2.27 
-0.3	2.92	1.79	2.26 
-1.17	2.81	1.73	2.26 
-1.23	2.76	1.85	2.25 
-1.38	2.73	1.90	2.24 
-1.23	2.87	1.89	2.24 
-0.46	2.82	1.51	2.24 
-0.37	2.82	1.38	2.24 
0.01	3.03	1.00	2.24 
2.68	3.27	1.09	2.25 
2.69	3.19	0.64	2.25 
0.97	2.98	1.07	2.25 
0.67	2.87	1.29	2.24 
0.53	2.94	0.93	2.23 
0.63	2.96	1.50	2.22 
0.37	2.90	1.52	2.21 
0.34	3.01	1.27	2.20 
1.22	3.12	0.86	2.20 
2.42	3.27	0.31	2.21 
3.45	3.51	0.63	2.23 
4.97	3.75	0.61	2.24 
5.71	4.03	0.98	2.25 
6.57	4.15	1.13	2.26 
6.3	4.23	2.08	2.26 
7.37	4.31	1.37	2.25 
6.77	4.08	1.74	2.25 
2.71	3.79	3.03	2.25 
1.59	3.81	4.18	2.25 
1.86	3.83	3.78	2.25 
3.66	3.71	2.64	2.26 
1.74	3.72	4.15	2.26 
1.48	3.67	3.46	2.27 
0.89	3.70	3.64	2.28 
1.29	3.76	4.06	2.28 
0.97	3.59	4.05	2.29 
1.46	3.59	3.55	2.29 
1.18	3.50	3.09	2.30 
0.55	3.42	2.81	2.30 
0.02	3.42	3.54	2.30 
-0.17	3.48	3.95	2.30 
0.09	3.49	3.82	2.31 
-0.05	3.46	3.90	2.31 
0.79	3.67	3.92	2.32 
1.33	3.76	3.74	2.32 
1.79	3.76	3.41	2.32 
1.76	3.76	3.51	2.33 
2.24	3.81	3.48	2.34 
0.98	3.59	2.73	2.34 
1.5	3.39	0.68	2.35 
1.82	3.37	1.37	2.37 
1.19	3.45	1.31	2.39 
1.14	3.62	0.81	2.41 
1.26	3.56	2.67	2.43 
0.72	3.54	2.78	2.45 
0.69	3.58	3.04	2.47 
0.68	3.64	2.80	2.46 
1.43	3.71	3.13	2.46 
1.72	3.75	2.82	2.45 
1.99	3.83	2.94	2.44 
2.27	3.86	2.90	2.41 
2.43	3.82	2.89	2.40 
2.28	3.85	3.35	2.38 
2	3.82	3.60	2.37 
1.07	3.76	4.15	2.36 
0.94	3.78	4.41	2.36 
2.07	3.79	2.80	2.37 
2.29	3.86	3.32	2.37 
2.36	3.92	3.56	2.38 
2.6	3.92	3.17	2.39 
2.28	3.98	3.54	2.40 
3.65	4.14	3.83	2.41 
3.94	4.12	3.01	2.42 
3.46	4.12	2.97	2.42 
3.74	4.15	3.28	2.43 
3.73	4.17	3.72	2.44 
2.49	4.13	4.40	2.45 
2.57	4.21	4.56	2.46 
2.14	4.01	4.60	2.48 
0.2	0.00	4.43	2.50 
1.14	4.38	5.83	2.52 
1.68	4.47	6.93	2.53 
2.56	4.43	6.80	2.54 
1.36	4.26	6.01	2.55 
1.67	4.41	6.53	2.54 
2.37	4.49	6.51	2.53 
2.02	4.50	7.01	2.52 
1.8	4.46	7.09	2.50 
0.86	4.22	6.91	2.49 
0.66	4.17	6.13	2.47 
1.24	4.00	5.33	2.45 
-0.77	3.86	6.73	2.44 
-1.13	3.84	6.83	2.42 
-0.66	3.76	6.83	2.41 
2.9	3.56	5.33	2.39 
1.75	3.51	3.83	2.37 
1.58	3.62	4.10	2.35 
1.66	3.87	2.19	2.33 
2.92	4.13	2.66	2.32 
3.19	4.18	3.57	2.30 
2.95	4.19	5.15	2.29 
2.95	4.14	5.12	2.28 
2.87	4.20	5.46	2.28 
3.67	4.40	6.06	2.27 
3.53	4.41	6.47	2.27 
3.6	4.37	6.28	2.28 
3.62	4.44	5.85	2.29 
3.38	4.33	6.25	2.32 
2.39	4.18	6.39	2.35 
0.92	3.97	6.61	2.39 
0.32	3.96	6.65	2.42 
0.68	3.99	6.48	2.44 
0.89	3.87	5.63	2.45 
2.53	3.92	3.44	2.45 
2	3.97	4.97	2.45 
2.9	4.00	3.59	2.44 
4.87	4.18	2.31	2.43 
6.22	4.27	2.12	2.43 
6.02	4.21	1.84	2.43 
5.89	4.25	2.15	2.43 
4.51	4.28	3.23	2.44 
4.23	4.63	4.67	2.45 
5.06	4.71	4.49	2.46 
0	4.77	5.71	2.46 
4.75	4.76	5.86	2.47 
1.77	4.25	5.96	2.47 
1.96	4.52	6.49	2.46 
3.89	4.84	6.83	2.46 
6.02	5.11	7.00	2.46 
5.61	5.11	6.95	2.45 
5.49	5.10	6.97	2.45 
4.89	5.00	6.91	2.44 
3.7	4.85	7.24	2.44 
0.75	4.47	8.05	2.43 
-0.22	4.45	8.83	2.42 
-0.66	4.37	8.69	2.41 
-3.83	3.63	8.56	2.40 
-4.61	3.05	6.35	2.39 
0	2.80	6.67	2.38 
-5.71	2.74	3.07	2.38 
-3.13	2.81	1.25	2.37 
-5	2.33	0.52	2.37 
-3.88	2.79	0.51	2.38 
-0.49	3.10	0.46	2.38 
1.08	3.28	0.98	2.38 
3.4	3.66	0.98	2.39 
6.48	4.14	1.19	2.40 
5.45	3.90	0.51	2.41 
0.34	3.14	1.67	2.43 
-1.2	3.14	2.27	2.45 
-1.03	2.97	1.81	2.47 
-1.43	2.93	1.86	2.49 
-2.01	2.89	1.85	2.51 
-1.36	2.93	1.38	2.52 
-0.91	2.93	0.43	2.52 
-0.81	3.01	0.67	2.52 
0.47	3.19	0.44	2.51 
1.19	3.36	0.26	2.50 
2.27	3.58	0.41	2.49 
1.8	3.44	0.50	2.48 
3.66	3.95	1.35	2.48 
5.01	4.16	1.07	2.49 
4.07	4.11	1.72	2.50 
4.14	4.19	2.22	2.52 
4.96	4.33	2.74	2.55 
5.35	4.35	1.86	2.59 
4.88	4.42	2.46	2.62 
3.75	4.21	2.94	2.64 
1.34	3.88	3.37	2.66 
0.7	3.80	2.61	2.67 
1.6	3.76	2.73	2.67 
2.28	3.60	0.99	2.67 
0.25	3.36	0.77	2.68 
0.02	3.21	0.55	2.69 
-0.52	3.21	1.20	2.71 
0.18	3.50	0.76	2.74 
0.14	3.44	1.47	2.78 
-0.02	3.53	2.08	2.82 
1.72	3.78	1.81	2.85 
2.31	3.88	1.44	2.87 
2.18	3.94	1.47	2.88 
2.31	3.92	1.79	2.89 
1.77	3.79	1.31	2.89 
1.52	3.75	1.78	2.89 
0.84	3.77	2.30	2.90 
-0.2	3.74	2.33	2.92 
1.36	3.88	1.86	2.95 
1.91	3.99	1.87	3.00 
3.21	4.34	2.31	3.08 
4.12	4.38	1.56	3.15 
5.26	4.32	0.64	3.22 
4.86	4.40	0.45	3.30 
2.42	3.90	1.07	3.36 
0.52	4.10	0.35	3.40 
-0.63	4.12	0.55	3.39 
0.95	4.42	2.67	3.37 
0.02	4.37	3.81	3.35 
-0.44	4.28	4.81	3.31 
-0.8	4.22	4.37	3.27 
-0.1	4.15	4.02	3.24 

par6 <- '12'
par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '4'
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')