Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
IA 57 49 47 41 68 45 28 58 89 66 70 81 43 75 52 51 56 55 75 50 41 53 66 66 69 80 65 34 96 55 67 62 73 38 69 91 56 51 80 65 53 42 78 52 80 58 60 68 50 59 43 69 67 72 70 70 43 46 57 52 78 58 76 73 72 72 67 64 77 53 54 60 50 58 70 71 46 35 54 78 73 69 37 28 75 44 71 42 69 58 104 59 77 54 54 85 77 59 55 57 74 53 51 81 69 61 62 62 83 57 77 28 64 64 49 52 54 62 53 82 42 61 37 45 67 47 65 52 56 61 73 37 64 80 78 58 53 70 36 59 35 40 60 48 53 44 93 64 46 59 80 41 83 56 81 68 66 45 80 71 32 87 69 68 65 40 37 81 48 55 57 61 74 56 62 36 61 64 51 43 40 56 57 46 53 45 42 84 66 61 76 68 79 47 69 43 87 69 74 74 82 41 56 88 66 60 79 37 57 50 62 76 71 56 92 50 77 63 48 55 49 67 47 64 40 79 49 69 84 83 88 60 85 78 41 61 59 67 77 65 55 65 62 98 53 59 56 57 46 85 63 80 70 60 93 65 83 47 41 58 66 53 57 63 36 60 78 99 69 47 55 45 55 46 62 61 72 50 60 73 85 55 66 36 56 66 73 100 83 56 73 70 53 51 75 37 83 55 67 63 72 73 73 69 70 64 76 56 62 55 37 54 56 83 83 59 53 57 61 65 51 90 55 73 80 58 53 51 79 81 52 60 73 49 86 41 39 33 67 33 48 74 69 65 72 36 50 67 36 88 91 39 87 37 93 84 32 36 70 33 82 35 90 41 78 45 89 43 80 38 77 41 79 48 75 41 82 37 43 84 37 93 42 90 86 48 88 48
Data Y:
Psiko 1.08 1.8 1.4 1.08 1.48 2.16 1.2 2.12 3.2 2.56 1.72 1.52 2.28 1.64 1.12 1.24 1.8 1.04 1.16 1.16 1.2 1.76 1.08 1.2 1 2.12 1.12 1.24 1.36 1.36 1.6 3.16 1.84 1.24 2.08 2.92 1.12 1.68 3.52 3.2 1.88 1.44 1.76 1.4 3.2 1.56 1.44 1.92 1.76 1.48 2.52 2.8 1.76 2.76 1.56 2.56 1.32 1.12 2 1.76 1.56 1.28 2.16 1.72 2.72 3.28 2.04 2.16 1.92 1.24 1.2 1.56 1.56 2.36 2.28 1.2 1.08 1.36 1.04 3.04 3.08 1.72 1.08 1.2 1.32 1.6 1.64 1.2 1.52 1.6 2.56 1.04 1.4 1.64 1.2 2.08 4 1.28 2.12 2.08 2.04 1.32 1.72 2.36 2.4 1.48 2.16 1.24 1.6 1.72 1.84 1.28 1.76 2.12 1.16 1.68 1.32 2.28 2.4 1.48 1.56 1.52 1.36 1.4 1.88 1.16 2.4 2.44 1.48 2.16 1.84 1.2 1.84 1.84 2.48 1.8 1.2 1.44 1.32 1.64 4 1.4 1.16 1 1.52 1.64 1.84 1.48 1.72 2.24 1.84 1.04 1.8 2.12 2.16 1.44 1.36 1.88 1.84 1.4 1.2 2.72 1.28 1.04 2.04 1 1.04 1.68 2.6 1.96 1 1.96 1.24 1.08 1.44 1.16 1.96 2.32 1.8 1.52 1 3.72 1.12 1.2 1.12 1.32 1.2 1.92 2.08 1.28 3.16 1.4 3.4 1.28 1.36 1.2 3.76 1.16 1.32 1.52 1.92 1.08 1.76 2.88 1.68 1.44 1.6 1.08 1.32 2.32 2.4 2.56 1.16 1.44 3.08 1.04 1.52 3.72 1.52 2.32 1.4 2.04 1.24 1.12 1.56 3 2.04 2.16 2.36 2.12 1.96 2.52 1.92 2.48 1.36 1.2 2.12 1.2 1.4 1.6 1.8 2.12 3.04 3.08 1.32 2.36 1.48 1.36 1.04 3.08 1.68 2.2 1.72 2 2.44 1.04 2.52 1.44 1.6 1.12 1.6 1.4 1.88 1.68 1.36 2.64 2.52 3.36 1.6 1.36 2.04 1 2.6 1.04 2.96 1.2 2 1 2.24 1.48 2.68 1.8 1.4 1.36 1.32 1.6 2.48 3.4 3.8 1.96 3 1.52 1.32 1.36 2.48 1.08 2.76 1.72 1.28 1.48 2.44 2.24 2.8 2.36 2.4 1.96 1.2 1.24 1.44 1.32 1.2 1.2 1.08 2 1.76 1.04 1.64 1.32 1 3.16 1.4 1.56 1.44 1.72 2.08 1.92 1 1.08 2.4 3.16 2 1.56 1.76 1.36 2.2 1.56 1.16 1.2 1.36 1.32 1.16 1.72 1.48 1.24 2.24 1.04 1.16 2.28 1.24 3.28 3.16 1.16 3.36 1.08 3.36 3.32 1.12 1.24 2.98 1.24 2.88 1.28 2.64 1.36 2.68 1.48 3 1.48 2.64 1.32 2.24 1.24 2.44 1.24 2.4 1.4 2.32 1.24 1.32 2.4 1.28 3.04 1.32 2.6 2.68 1.36 2.92 1.28
Box-Cox transformation parameter (X series)
1
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree (d) of non-seasonal differencing (X series)
0
0
1
2
Degree (D) of seasonal differencing (X series)
0
0
1
2
Seasonal Period
1
1
2
3
4
12
Box-Cox transformation parameter (Y series)
1
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree (d) of non-seasonal differencing (Y series)
0
0
1
2
Degree (D) of seasonal differencing (Y series)
0
0
1
2
Number of non-seasonal time lags in test
1
1
2
3
4
5
6
7
8
9
10
11
Chart options
Label y-axis:
Label x-axis:
R Code
library(lmtest) par1 <- as.numeric(par1) par2 <- as.numeric(par2) par3 <- as.numeric(par3) par4 <- as.numeric(par4) par5 <- as.numeric(par5) par6 <- as.numeric(par6) par7 <- as.numeric(par7) par8 <- as.numeric(par8) ox <- x oy <- y if (par1 == 0) { x <- log(x) } else { x <- (x ^ par1 - 1) / par1 } if (par5 == 0) { y <- log(y) } else { y <- (y ^ par5 - 1) / par5 } if (par2 > 0) x <- diff(x,lag=1,difference=par2) if (par6 > 0) y <- diff(y,lag=1,difference=par6) if (par3 > 0) x <- diff(x,lag=par4,difference=par3) if (par7 > 0) y <- diff(y,lag=par4,difference=par7) print(x) print(y) (gyx <- grangertest(y ~ x, order=par8)) (gxy <- grangertest(x ~ y, order=par8)) bitmap(file='test1.png') op <- par(mfrow=c(2,1)) (r <- ccf(ox,oy,main='Cross Correlation Function (raw data)',ylab='CCF',xlab='Lag (k)')) (r <- ccf(x,y,main='Cross Correlation Function (transformed and differenced)',ylab='CCF',xlab='Lag (k)')) par(op) dev.off() bitmap(file='test2.png') op <- par(mfrow=c(2,1)) acf(ox,lag.max=round(length(x)/2),main='ACF of x (raw)') acf(x,lag.max=round(length(x)/2),main='ACF of x (transformed and differenced)') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow=c(2,1)) acf(oy,lag.max=round(length(y)/2),main='ACF of y (raw)') acf(y,lag.max=round(length(y)/2),main='ACF of y (transformed and differenced)') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Granger Causality Test: Y = f(X)',5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Model',header=TRUE) a<-table.element(a,'Res.DF',header=TRUE) a<-table.element(a,'Diff. DF',header=TRUE) a<-table.element(a,'F',header=TRUE) a<-table.element(a,'p-value',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Complete model',header=TRUE) a<-table.element(a,gyx$Res.Df[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Reduced model',header=TRUE) a<-table.element(a,gyx$Res.Df[2]) a<-table.element(a,gyx$Df[2]) a<-table.element(a,gyx$F[2]) a<-table.element(a,gyx$Pr[2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Granger Causality Test: X = f(Y)',5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Model',header=TRUE) a<-table.element(a,'Res.DF',header=TRUE) a<-table.element(a,'Diff. DF',header=TRUE) a<-table.element(a,'F',header=TRUE) a<-table.element(a,'p-value',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Complete model',header=TRUE) a<-table.element(a,gxy$Res.Df[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Reduced model',header=TRUE) a<-table.element(a,gxy$Res.Df[2]) a<-table.element(a,gxy$Df[2]) a<-table.element(a,gxy$F[2]) a<-table.element(a,gxy$Pr[2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation