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Author's title

Author

*Unverified author*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 20 Dec 2011 11:48:29 -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/t1324399739i9kzhthnz0zdqt1.htm/, Retrieved Sun, 05 May 2024 22:36:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158050, Retrieved Sun, 05 May 2024 22:36:52 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

123

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

-     [Multiple Regression] [] [2011-12-18 13:02:22] [6a3e51c0c7ab195427042dfaef1df5a0]
- RMP   [Recursive Partitioning (Regression Trees)] [] [2011-12-18 13:11:57] [6a3e51c0c7ab195427042dfaef1df5a0]
- R P     [Recursive Partitioning (Regression Trees)] [] [2011-12-18 13:13:17] [6a3e51c0c7ab195427042dfaef1df5a0]
-    D      [Recursive Partitioning (Regression Trees)] [paper] [2011-12-19 15:43:16] [a9dc51245fb8ca00f931d89893d090c8]
-   P         [Recursive Partitioning (Regression Trees)] [paper] [2011-12-19 16:18:31] [a9dc51245fb8ca00f931d89893d090c8]
-   P             [Recursive Partitioning (Regression Trees)] [] [2011-12-20 16:48:29] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-                   [Recursive Partitioning (Regression Trees)] [paper Deel 3] [2011-12-21 16:01:43] [6a3e51c0c7ab195427042dfaef1df5a0] 
- R P                 [Recursive Partitioning (Regression Trees)] [Paper Deel 3] [2011-12-21 16:46:13] [6a3e51c0c7ab195427042dfaef1df5a0] 
- RM                    [Recursive Partitioning (Regression Trees)] [Paper Deel 3] [2011-12-21 16:46:46] [6a3e51c0c7ab195427042dfaef1df5a0] 

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Dataseries X:

Download CSV

Histogram

Boxplots

272545	1747	69	483	32033	3	116	144
179444	1209	64	429	20654	4	127	133
222373	1844	69	673	16346	16	106	162
218443	2683	104	1137	35926	2	133	148
167843	1228	51	374	10621	1	64	88
70849	631	28	179	10024	3	89	129
506574	4627	123	2251	43068	0	122	128
33186	381	19	111	1271	0	22	67
216660	2063	59	740	34416	7	117	132
213274	1758	44	595	20318	0	82	120
307153	2132	109	800	24409	0	147	169
237633	2128	114	660	20648	7	184	210
164292	1667	68	635	12347	8	113	122
364402	2965	79	1172	21857	4	171	191
244103	2098	84	674	11034	10	87	162
384448	4904	178	1692	33433	0	199	223
325587	2242	68	811	35902	6	139	140
323652	2977	157	1168	22355	4	92	144
176082	1438	55	507	31219	3	85	111
266736	2347	87	689	21983	8	193	199
278265	2522	70	837	40085	0	160	187
442703	2889	103	1270	18507	1	144	144
180393	1447	41	462	16278	5	84	89
189897	1717	54	601	24662	9	208	208
234247	3362	121	1242	31452	1	154	165
237452	2898	125	1025	32580	0	139	146
267268	2828	127	1062	22883	5	127	158
270787	1972	86	618	27652	0	148	154
155915	1495	51	559	9845	0	99	117
342564	2840	69	1062	20190	0	135	158
282172	2299	76	913	46201	3	171	183
216584	1909	76	643	10971	6	149	186
318563	2091	84	779	34811	1	178	185
98672	971	37	322	3029	4	137	141
386258	3293	95	1243	38941	4	151	156
273950	2764	56	1186	4958	0	127	159
425120	3682	120	1324	32344	0	151	161
227636	1918	83	640	19433	2	89	139
115658	947	33	284	12558	1	46	55
349863	3433	194	1210	36524	2	153	163
324178	3246	79	1490	26041	10	122	145
178083	1692	67	667	16637	9	111	148
195153	1735	73	635	28395	5	108	115
177694	1771	61	479	16747	6	142	174
153778	2496	82	1022	9105	1	45	73
455168	5501	151	2068	11941	2	131	147
78800	918	42	330	7935	2	66	82
208051	2228	76	648	19499	0	180	201
348077	4051	118	1367	22938	10	165	181
175523	2081	54	868	25314	3	146	164
224591	1875	74	588	28524	0	137	158
24188	496	24	218	2694	0	7	12
372238	2537	314	833	20867	8	157	163
65029	744	17	255	3597	5	61	67
101097	1161	64	454	5296	3	41	52
279012	3027	58	1108	32982	1	120	134
317644	2526	84	662	38975	5	228	230
340471	3705	185	1119	42721	5	137	145
358958	2667	141	1058	41455	0	150	153
252529	2175	83	822	23923	12	127	139
370628	3949	140	1302	26719	10	161	178
304468	3165	117	1145	53405	12	73	101
265870	2939	113	1185	12526	11	97	169
264889	2610	88	931	26584	8	142	163
228595	1426	66	557	37062	2	125	139
216027	1646	65	436	25696	0	87	116
198798	1971	132	596	24634	6	128	137
238146	2746	145	837	27269	9	148	167
234891	2308	81	848	25270	2	116	135
175816	1684	69	625	24634	5	89	102
239314	2537	68	865	17828	13	154	173
73566	893	32	385	3007	6	67	88
242622	2195	84	718	20065	7	171	175
187167	1695	53	705	24648	2	90	133
209049	2061	63	732	21588	2	133	148
360592	2329	86	988	25217	4	144	169
342846	2695	92	1077	30927	3	133	140
207650	1809	107	524	18487	6	125	154
206500	2290	62	697	18050	2	134	148
182357	1791	64	644	17696	0	110	134
153613	1678	46	622	17326	1	89	109
456979	4023	124	1227	39361	0	138	175
145943	1369	69	653	9648	5	99	99
280366	2308	104	656	26759	2	92	122
80953	870	25	437	7905	0	27	28
150216	1966	54	822	4527	0	77	101
167878	1459	59	423	41517	6	137	139
369718	3795	205	1489	21261	1	137	143
322454	2673	116	929	36099	0	122	206
179797	3085	104	1044	39039	1	159	171
262883	2367	91	792	13841	1	85	138
262793	2209	77	678	23841	3	138	148
189142	1829	63	597	8589	9	90	114
275997	3087	74	1099	15049	1	135	140
328875	2559	82	966	39038	4	147	156
189252	1624	36	555	30391	3	139	140
222504	1607	51	552	39932	5	127	127
287386	2109	79	778	43840	0	104	141
389104	4015	151	1322	43146	12	248	251
397681	3705	108	1415	50099	13	106	114
287748	2714	136	853	40312	8	176	198
294320	2325	65	848	32616	0	130	155
186856	1999	179	640	11338	0	59	138
43287	602	14	214	7409	4	64	71
185468	2146	80	716	18213	4	36	84
235352	2325	146	795	45873	0	98	167
268077	2617	48	1170	39844	0	125	155
305195	2688	90	1048	28317	0	124	150
143356	1207	72	399	24797	0	83	112
154165	3102	88	906	7471	0	127	161
307000	1869	68	609	27259	4	143	149
298039	2304	88	688	23201	0	115	164
23623	398	11	156	238	0	0	0
195817	2205	73	779	28830	0	103	155
61857	530	25	192	3913	4	30	32
163766	1596	48	457	9935	0	119	169
414506	3083	117	1195	27738	1	102	140
21054	387	16	146	338	0	0	0
252805	2137	52	866	13326	5	77	111
31961	492	22	200	3988	0	9	25
317367	3450	115	1230	24347	3	137	146
240153	2089	65	696	27111	7	163	183
175083	1658	88	491	3938	13	146	181
152043	1685	53	670	17416	3	84	107
38214	568	34	276	1888	0	21	27
216299	2059	42	716	18700	2	151	163
357602	2792	82	1021	36809	0	187	198
198104	1395	61	481	24959	0	171	205
410803	3590	80	1582	37343	4	167	187
316105	2387	97	820	21849	0	145	187
397297	3334	124	1153	49809	3	175	186
187992	1250	35	473	21654	0	137	151
102424	1121	42	401	8728	0	100	131
286327	2880	335	954	20920	4	150	155
407378	4104	170	1447	27195	4	163	172
143860	1759	54	546	1037	15	149	160
391854	4138	132	1728	42570	2	161	172
157429	1831	77	689	17672	4	112	143
258751	1787	48	590	34245	2	135	151
282399	2535	94	897	16786	1	124	158
217665	1816	113	613	20954	0	45	125
366774	3873	116	1548	16378	9	120	145
236660	2181	88	759	31852	1	126	145
173260	2035	63	716	2805	3	78	79
323545	2960	99	955	38086	11	136	174
168994	1915	57	720	21166	5	179	192
253330	2648	86	1023	34672	2	118	132
301703	2633	105	818	36171	1	147	159
1	2	0	0	0	9	0	0
14688	207	10	85	2065	0	0	0
98	5	1	0	0	0	0	0
455	8	2	0	0	0	0	0
0	0	0	0	0	1	0	0
0	0	0	0	0	0	0	0
246435	2116	84	737	19354	2	88	133
382374	3286	154	1080	22124	3	129	204
0	0	0	0	0	0	0	0
203	4	4	0	0	0	0	0
7199	151	5	74	556	0	0	0
46660	474	20	259	2089	0	13	15
17547	141	5	69	2658	0	4	4
116678	1047	42	285	1813	0	76	152
969	29	2	0	0	0	0	0
206501	1822	68	591	17372	2	71	125

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=158050&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=158050&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158050&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

10-Fold Cross Validation
	Prediction (training)			Prediction (testing)
Actual	C1	C2	CV	C1	C2	CV
C1	661	67	0.908	78	14	0.8478
C2	55	681	0.9253	5	79	0.9405
Overall	-	-	0.9167	-	-	0.892

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 661 & 67 & 0.908 & 78 & 14 & 0.8478 \tabularnewline
C2 & 55 & 681 & 0.9253 & 5 & 79 & 0.9405 \tabularnewline
Overall & - & - & 0.9167 & - & - & 0.892 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158050&T=1

[TABLE]
[ROW][C]10-Fold Cross Validation[/C][/ROW]
[ROW][C][/C][C]Prediction (training)[/C][C]Prediction (testing)[/C][/ROW]
[ROW][C]Actual[/C][C]C1[/C][C]C2[/C][C]CV[/C][C]C1[/C][C]C2[/C][C]CV[/C][/ROW]
[ROW][C]C1[/C][C]661[/C][C]67[/C][C]0.908[/C][C]78[/C][C]14[/C][C]0.8478[/C][/ROW]
[ROW][C]C2[/C][C]55[/C][C]681[/C][C]0.9253[/C][C]5[/C][C]79[/C][C]0.9405[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.9167[/C][C]-[/C][C]-[/C][C]0.892[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158050&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158050&T=1

As an alternative you can also use a QR Code:

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

10-Fold Cross Validation
	Prediction (training)			Prediction (testing)
Actual	C1	C2	CV	C1	C2	CV
C1	661	67	0.908	78	14	0.8478
C2	55	681	0.9253	5	79	0.9405
Overall	-	-	0.9167	-	-	0.892

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2
C1	74	8
C2	6	76

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 74 & 8 \tabularnewline
C2 & 6 & 76 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158050&T=2

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][/ROW]
[ROW][C]C1[/C][C]74[/C][C]8[/C][/ROW]
[ROW][C]C2[/C][C]6[/C][C]76[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158050&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158050&T=2

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
C1	74	8
C2	6	76

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 = 1 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;

Parameters (R input):

par1 = 1 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;

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