| Workshop 7 | *The author of this computation has been verified* | R Software Module: /rwasp_multipleregression.wasp (opens new window with default values) | Title produced by software: Multiple Regression | Date of computation: Fri, 24 Dec 2010 10:50:01 +0000 | | Cite this page as follows: | Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio.htm/, Retrieved Fri, 24 Dec 2010 11:51:51 +0100 | | BibTeX entries for LaTeX users: | @Manual{KEY,
author = {{YOUR NAME}},
publisher = {Office for Research Development and Education},
title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio.htm/},
year = {2010},
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2010},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org},
}
| | Original text written by user: | | | IsPrivate? | No (this computation is public) | | User-defined keywords: | | | Dataseries X: | » Textbox « » Textfile « » CSV « | 6282929 213118 1081 162556
4324047 81767 309 29790
4108272 153198 458 87550
-1212617 -26007 588 84738
1485329 126942 299 54660
1779876 157214 156 42634
1367203 129352 481 40949
2519076 234817 323 42312
912684 60448 452 37704
1443586 47818 109 16275
1220017 245546 115 25830
984885 48020 110 12679
1457425 -1710 239 18014
-572920 32648 247 43556
929144 95350 497 24524
1151176 151352 103 6532
790090 288170 109 7123
774497 114337 502 20813
990576 37884 248 37597
454195 122844 373 17821
876607 82340 119 12988
711969 79801 84 22330
702380 165548 102 13326
264449 116384 295 16189
450033 134028 105 7146
541063 63838 64 15824
588864 74996 267 26088
-37216 31080 129 11326
783310 32168 37 8568
467359 49857 361 14416
688779 87161 28 3369
608419 106113 85 11819
696348 80570 44 6620
597793 102129 49 4519
821730 301670 22 2220
377934 102313 155 18562
651939 88577 91 10327
697458 112477 81 5336
700368 191778 79 2365
225986 79804 145 4069
348695 128294 816 7710
373683 96448 61 13718
50 etc... | | Output produced by software: | Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!
Multiple Linear Regression - Estimated Regression Equation | Wealth[t] = -216241.33931436 + 5.27817321004937Dividends[t] -230.700757875951Trades[t] + 28.3041837527339Costs[t] + e[t] |
Multiple Linear Regression - Ordinary Least Squares | Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value | (Intercept) | -216241.33931436 | 260735.548091 | -0.8294 | 0.411189 | 0.205594 | Dividends | 5.27817321004937 | 1.794062 | 2.942 | 0.005092 | 0.002546 | Trades | -230.700757875951 | 834.369671 | -0.2765 | 0.783405 | 0.391702 | Costs | 28.3041837527339 | 6.437106 | 4.397 | 6.4e-05 | 3.2e-05 |
Multiple Linear Regression - Regression Statistics | Multiple R | 0.721039475305495 | R-squared | 0.519897924948823 | Adjusted R-squared | 0.488586920054181 | F-TEST (value) | 16.6043193662491 | F-TEST (DF numerator) | 3 | F-TEST (DF denominator) | 46 | p-value | 1.89261485483705e-07 | Multiple Linear Regression - Residual Statistics | Residual Standard Deviation | 843234.15063263 | Sum Squared Residuals | 32708016308484.1 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error | 1 | 6282929 | 5260259.75371044 | 1022669.24628956 | 2 | 4324047 | 987234.149362023 | 3336812.85063798 | 3 | 4108272 | 2964734.58056345 | 1143537.41943655 | 4 | -1212617 | 1909277.08721999 | -3121894.08721999 | 5 | 1485329 | 1931907.68163525 | -446578.681635252 | 6 | 1779876 | 1784292.63561575 | -4416.63561574885 | 7 | 1367203 | 1514561.87770431 | -147358.877704313 | 8 | 2519076 | 2146253.73750155 | 372822.262498454 | 9 | 912684 | 1065717.87653985 | -153033.876539853 | 10 | 1443586 | 471654.555211046 | 971931.444788954 | 11 | 1220017 | 1784359.45889780 | -564342.458897803 | 12 | 984885 | 370708.200666769 | 614176.799333231 | 13 | 1457425 | 229467.069485852 | 1227957.93051415 | 14 | -572920 | 1131914.39998605 | -1704834.39998605 | 15 | 929144 | 866506.001951546 | 62637.9980484542 | 16 | 1151176 | 743741.482584666 | 407434.517415334 | 17 | 790090 | 1481234.15288781 | -691144.15288781 | 18 | 774497 | 860532.346994978 | -86035.3469949775 | 19 | 990576 | 990655.58317345 | -79.583173450461 | 20 | 454195 | 850508.046470685 | -396313.046470685 | 21 | 876607 | 558524.791194374 | 318082.208805626 | 22 | 711969 | 817615.720557757 | -105646.720557757 | 23 | 702380 | 1011199.75464848 | -308819.754648477 | 24 | 264449 | 788213.278763628 | -523764.278763628 | 25 | 450033 | 669219.777202197 | -219186.777202197 | 26 | 541063 | 553827.237267971 | -12764.2372679711 | 27 | 588864 | 856402.982134945 | -267538.982134945 | 28 | -37216 | 238617.071471440 | -275833.071471440 | 29 | 783310 | 187521.254858521 | 595788.745141479 | 30 | 467359 | 371662.681805265 | 95696.3181947352 | 31 | 688779 | 332706.689689186 | 356072.310310814 | 32 | 608419 | 658759.037877714 | -50340.0378777138 | 33 | 696348 | 386243.939315873 | 310104.060684127 | 34 | 597793 | 439415.481697454 | 158377.518302546 | 35 | 821730 | 1433785.04421903 | -612055.044219029 | 36 | 377934 | 813408.037672894 | -435474.037672894 | 37 | 651939 | 522586.945759953 | 129352.054240047 | 38 | 697458 | 509776.111948998 | 187681.888051002 | 39 | 700368 | 844710.197265502 | -144342.197265502 | 40 | 225986 | 286696.109338281 | -60710.1093382805 | 41 | 348695 | 490890.052802516 | -142195.052802516 | 42 | 373683 | 667031.956938051 | -293348.956938051 | 43 | 501709 | 354847.427894736 | 146861.572105264 | 44 | 413743 | 574247.434951195 | -160504.434951195 | 45 | 379825 | 265480.157138787 | 114344.842861213 | 46 | 336260 | 418892.833142698 | -82632.8331426981 | 47 | 636765 | 924979.609921747 | -288214.609921747 | 48 | 481231 | 644531.66585773 | -163300.665857730 | 49 | 469107 | 546613.479365427 | -77506.4793654269 | 50 | 211928 | 244060.559134379 | -32132.559134379 |
Goldfeld-Quandt test for Heteroskedasticity | p-values | Alternative Hypothesis | breakpoint index | greater | 2-sided | less | 7 | 0.9999999959842 | 8.031599993387e-09 | 4.0157999966935e-09 | 8 | 0.999999999972723 | 5.4554394701337e-11 | 2.72771973506685e-11 | 9 | 0.999999999874883 | 2.50235015477770e-10 | 1.25117507738885e-10 | 10 | 0.99999999997513 | 4.97396308670504e-11 | 2.48698154335252e-11 | 11 | 0.999999999994918 | 1.01644405554525e-11 | 5.08222027772623e-12 | 12 | 0.999999999989315 | 2.13694855513371e-11 | 1.06847427756685e-11 | 13 | 0.999999999999702 | 5.95564203285812e-13 | 2.97782101642906e-13 | 14 | 1 | 8.54398056652114e-17 | 4.27199028326057e-17 | 15 | 1 | 1.61287117539092e-16 | 8.06435587695458e-17 | 16 | 1 | 3.35283701050966e-17 | 1.67641850525483e-17 | 17 | 1 | 7.28514433037711e-17 | 3.64257216518855e-17 | 18 | 1 | 2.65667961995887e-16 | 1.32833980997944e-16 | 19 | 1 | 5.45953629383485e-16 | 2.72976814691742e-16 | 20 | 0.999999999999998 | 3.31301813495944e-15 | 1.65650906747972e-15 | 21 | 0.999999999999998 | 3.73430084398991e-15 | 1.86715042199495e-15 | 22 | 0.99999999999999 | 1.89370901551996e-14 | 9.4685450775998e-15 | 23 | 0.999999999999944 | 1.12319236372707e-13 | 5.61596181863533e-14 | 24 | 0.999999999999838 | 3.23248861417049e-13 | 1.61624430708524e-13 | 25 | 0.999999999999054 | 1.89219820308593e-12 | 9.46099101542965e-13 | 26 | 0.999999999993876 | 1.22481942924936e-11 | 6.12409714624678e-12 | 27 | 0.999999999968538 | 6.29236179496501e-11 | 3.14618089748251e-11 | 28 | 0.999999999989076 | 2.18477857889081e-11 | 1.09238928944541e-11 | 29 | 0.999999999993036 | 1.39278135215315e-11 | 6.96390676076577e-12 | 30 | 0.99999999995875 | 8.24994804329799e-11 | 4.12497402164899e-11 | 31 | 0.999999999897176 | 2.05648437832462e-10 | 1.02824218916231e-10 | 32 | 0.99999999940928 | 1.18144140057883e-09 | 5.90720700289415e-10 | 33 | 0.999999999249994 | 1.50001231948556e-09 | 7.5000615974278e-10 | 34 | 0.99999999689505 | 6.20990099601257e-09 | 3.10495049800629e-09 | 35 | 0.999999980739143 | 3.85217139968214e-08 | 1.92608569984107e-08 | 36 | 0.999999909920896 | 1.80158207267686e-07 | 9.00791036338429e-08 | 37 | 0.99999983333779 | 3.33324420138176e-07 | 1.66662210069088e-07 | 38 | 0.999999934560217 | 1.30879567015064e-07 | 6.54397835075318e-08 | 39 | 0.999999501278378 | 9.97443244377578e-07 | 4.98721622188789e-07 | 40 | 0.99999673524599 | 6.52950802144258e-06 | 3.26475401072129e-06 | 41 | 0.99998725520848 | 2.54895830394685e-05 | 1.27447915197342e-05 | 42 | 0.999839458393447 | 0.000321083213105298 | 0.000160541606552649 | 43 | 0.999299853996546 | 0.00140029200690877 | 0.000700146003454385 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | Description | # significant tests | % significant tests | OK/NOK | 1% type I error level | 37 | 1 | NOK | 5% type I error level | 37 | 1 | NOK | 10% type I error level | 37 | 1 | NOK |
| | Charts produced by software: | | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/10rqac1293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/10rqac1293187793.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/137vi1293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/137vi1293187793.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/2vgul1293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/2vgul1293187793.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/3vgul1293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/3vgul1293187793.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/4vgul1293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/4vgul1293187793.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/56pco1293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/56pco1293187793.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/66pco1293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/66pco1293187793.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/7hyb91293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/7hyb91293187793.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/8hyb91293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/8hyb91293187793.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/9rqac1293187793.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/24/t1293187901u9thvsokagkjwio/9rqac1293187793.ps (open in new window) |
| | Parameters (Session): | par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; | | Parameters (R input): | par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; | | R code (references can be found in the software module): | library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}
| |
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