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Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 20 Dec 2011 13:49:03 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/20/t132440700615k1zcci6p2lrra.htm/, Retrieved Mon, 06 May 2024 06:42:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158148, Retrieved Mon, 06 May 2024 06:42:35 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

182

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Kendall tau Correlation Matrix] [Paper, Pearson Co...] [2011-12-18 12:42:54] [75512e061a94450f738c2449abbaac12]
-   P   [Kendall tau Correlation Matrix] [Paper, Kendall's ...] [2011-12-19 10:46:53] [75512e061a94450f738c2449abbaac12]
- RMP     [Multiple Regression] [Paper, 3.3 Meervo...] [2011-12-19 15:22:52] [75512e061a94450f738c2449abbaac12]
- RMPD      [Skewness and Kurtosis Test] [paper, skewness a...] [2011-12-20 18:39:20] [75512e061a94450f738c2449abbaac12]
- RMPD          [Recursive Partitioning (Regression Trees)] [paper, regression...] [2011-12-20 18:49:03] [242bbde8f74d68805b56d9ecebfdbe63] [Current]

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Dataseries X:

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Histogram

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1683	150596	84	37	18	63	20465	23975	0
1323	154801	50	42	20	56	33629	85634	1
192	7215	18	0	0	0	1423	1929	0
2172	122139	91	49	26	60	25629	36294	0
3335	221399	129	76	30	112	54002	72255	0
6310	441870	237	118	34	130	151036	189748	1
1478	134379	52	42	23	71	33287	61834	1
1324	140428	53	57	30	107	31172	68167	0
1488	103255	40	45	30	50	28113	38462	0
2756	271630	91	67	26	79	57803	101219	1
1931	121593	71	50	24	58	49830	43270	2
1966	172071	63	71	30	91	52143	76183	0
1575	83707	94	41	19	36	21055	31476	0
2855	197412	98	66	25	91	47007	62157	4
1263	134398	48	42	17	58	28735	46261	4
1479	139224	73	54	19	65	59147	50063	3
1636	134153	52	75	33	131	78950	64483	0
1076	64149	52	0	15	45	13497	2341	5
2376	122294	82	54	34	110	46154	48149	0
678	24889	22	13	15	33	53249	12743	0
902	52197	52	16	15	37	10726	18743	0
2308	188915	89	77	27	78	83700	97057	0
1590	163147	66	34	25	67	40400	17675	0
1863	98575	48	38	34	69	33797	33106	1
1799	143546	80	50	21	58	36205	53311	1
1385	139780	25	39	21	60	30165	42754	0
1870	163784	146	54	25	88	58534	59056	0
1161	152479	75	67	28	71	44663	101621	0
2417	304108	109	55	26	85	92556	118120	0
1952	184024	40	52	20	67	40078	79572	0
1514	151621	41	50	28	84	34711	42744	0
1487	164516	41	54	20	58	31076	65931	2
2051	120179	94	53	17	35	74608	38575	4
2843	214701	116	76	25	74	58092	28795	0
2216	196865	48	52	24	89	42009	94440	1
1	0	1	0	0	0	0	0	0
1830	181527	57	46	27	75	36022	38229	0
1563	93107	49	44	14	39	23333	31972	3
2046	129352	45	35	32	93	53349	40071	9
2005	229143	58	82	31	123	92596	132480	0
1934	177063	67	70	21	73	49598	62797	2
1572	126602	53	31	34	118	44093	40429	0
950	93742	29	25	23	76	84205	45545	2
1877	152153	72	48	24	65	63369	57568	1
1036	95704	42	44	22	82	60132	39019	2
1097	139793	84	40	22	67	37403	53866	2
730	76348	30	23	35	63	24460	38345	1
1918	188980	86	63	21	84	46456	50210	0
1826	172100	79	43	31	112	66616	80947	1
2444	146552	54	62	26	75	41554	43461	7
658	48188	28	12	22	39	22346	14812	0
1425	109185	60	63	21	63	30874	37819	0
2246	263652	68	60	27	93	68701	102738	0
1899	215609	75	53	26	69	35728	54509	0
1630	174876	54	53	33	117	29010	62956	1
1496	115124	49	35	11	30	23110	55411	6
1681	179712	60	49	26	65	38844	50611	0
816	70369	20	25	26	78	27084	26692	0
902	109215	58	47	21	80	35139	60056	0
2606	166096	85	30	38	85	57476	25155	10
1557	130414	51	50	29	107	33277	42840	6
1780	102057	71	36	19	60	31141	39358	0
1265	115310	56	43	19	53	61281	47241	11
1117	101181	32	44	24	62	25820	49611	3
1069	135228	31	14	26	90	23284	41833	0
1229	94982	37	38	29	89	35378	48930	0
2155	166919	67	58	34	127	74990	110600	8
2500	118169	64	68	25	71	29653	52235	2
1003	102361	36	48	24	75	64622	53986	0
340	31970	15	5	21	42	4157	4105	0
2586	200413	107	53	19	42	29245	59331	3
1119	103381	58	36	12	8	50008	47796	1
1251	94940	61	62	28	82	52338	38302	2
1516	101560	65	46	21	41	13310	14063	1
2473	144176	60	67	34	118	92901	54414	0
1288	71921	37	2	32	91	10956	9903	2
1911	126905	54	64	27	96	34241	53987	1
2279	131184	87	59	26	81	75043	88937	0
816	60138	23	16	21	46	21152	21928	0
1234	84971	71	34	31	60	42249	29487	0
907	80420	64	54	26	69	42005	35334	0
1827	233569	57	39	26	85	41152	57596	0
841	56252	25	26	23	17	14399	29750	0
1309	97181	32	37	25	61	28263	41029	0
764	50800	41	17	22	55	17215	12416	0
1439	125941	45	32	26	55	48140	51158	0
2500	211032	210	55	33	124	62897	79935	0
974	71960	92	39	22	65	22883	26552	0
1152	90379	53	39	24	73	41622	25807	6
1261	125650	47	28	21	67	40715	50620	0
1508	115572	36	45	28	66	65897	61467	5
2005	136266	67	66	22	61	76542	65292	1
1191	146715	55	39	22	74	37477	55516	0
1265	124626	57	27	15	55	53216	42006	0
761	49176	33	22	13	27	40911	26273	0
2156	212926	102	43	36	115	57021	90248	0
1689	173884	55	88	24	76	73116	61476	0
223	19349	12	13	1	0	3895	9604	0
2074	181141	95	23	24	83	46609	45108	3
1879	145502	70	40	31	90	29351	47232	0
566	45448	26	8	4	4	2325	3439	0
802	58280	20	41	20	56	31747	30553	0
1131	115944	44	51	23	63	32665	24751	0
981	94341	52	24	23	52	19249	34458	1
591	59090	37	23	12	24	15292	24649	0
596	27676	22	2	16	17	5842	2342	0
1261	120586	41	78	28	101	33994	52739	0
861	88011	31	12	10	20	13018	6245	0
0	0	0	0	0	0	0	0	0
1030	85610	31	46	25	51	98177	35381	0
991	84193	58	22	21	76	37941	19595	0
1178	117769	39	49	21	55	31032	50848	0
1200	107653	56	52	21	70	32683	39443	0
849	71894	57	36	21	38	34545	27023	0
78	3616	5	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0
924	154806	38	35	23	81	27525	61022	0
1480	136061	73	68	29	64	66856	63528	0
1870	141822	89	26	27	66	28549	34835	1
861	106515	37	32	23	89	38610	37172	0
778	43410	19	7	1	3	2781	13	0
1533	146920	64	67	25	76	41211	62548	1
889	88874	38	30	17	48	22698	31334	0
1705	111924	49	55	29	62	41194	20839	8
700	60373	39	3	12	32	32689	5084	3
285	19764	12	10	2	4	5752	9927	1
1490	121665	46	46	18	61	26757	53229	2
981	108685	26	23	25	90	22527	29877	0
1368	124493	37	43	29	91	44810	37310	0
256	11796	9	1	2	1	0	0	0
98	10674	9	0	0	0	0	0	0
1317	131263	52	33	18	39	100674	50067	0
41	6836	3	0	1	0	0	0	0
1768	153278	55	48	21	45	57786	47708	5
42	5118	3	5	0	0	0	0	0
528	40248	16	8	4	7	5444	6012	1
0	0	0	0	0	0	0	0	0
938	100728	42	25	25	75	28470	27749	0
1245	84267	36	21	26	52	61849	47555	0
81	7131	4	0	0	0	0	0	1
257	8812	13	0	4	1	2179	1336	0
891	63952	22	15	17	49	8019	11017	1
1114	120111	47	47	21	69	39644	55184	0
1079	94127	18	17	22	56	23494	43485	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158148&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158148&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158148&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2	C3
C1	98	9	0
C2	11	24	0
C3	0	2	0

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 & C3 \tabularnewline
C1 & 98 & 9 & 0 \tabularnewline
C2 & 11 & 24 & 0 \tabularnewline
C3 & 0 & 2 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158148&T=1

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][C]C3[/C][/ROW]
[ROW][C]C1[/C][C]98[/C][C]9[/C][C]0[/C][/ROW]
[ROW][C]C2[/C][C]11[/C][C]24[/C][C]0[/C][/ROW]
[ROW][C]C3[/C][C]0[/C][C]2[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158148&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158148&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2	C3
C1	98	9	0
C2	11	24	0
C3	0	2	0

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 2 ; par2 = equal ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = equal ; par3 = 3 ; par4 = no ;

R code (references can be found in the software module):

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code