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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationMon, 19 Dec 2011 14:16:47 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/19/t1324324491au9c9cluw7d90n3.htm/, Retrieved Wed, 15 May 2024 07:51:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157652, Retrieved Wed, 15 May 2024 07:51:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [WS 10 - recursive...] [2010-12-11 16:07:41] [033eb2749a430605d9b2be7c4aac4a0c]
-         [Recursive Partitioning (Regression Trees)] [] [2010-12-13 18:24:09] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-           [Recursive Partitioning (Regression Trees)] [] [2010-12-25 21:51:47] [2e1e44f0ae3cb9513dc28781dfdb387b]
-   PD        [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-15 19:16:06] [9bc4436833541bde47df2e8d3b08804c]
-   PD            [Recursive Partitioning (Regression Trees)] [with categorizati...] [2011-12-19 19:16:47] [f914a0f804421ae312123c83c378984e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
30	112285	24188	146283	144	145
28	84786	18273	98364	103	101
38	83123	14130	86146	98	98
30	101193	32287	96933	135	132
22	38361	8654	79234	61	60
26	68504	9245	42551	39	38
25	119182	33251	195663	150	144
18	22807	1271	6853	5	5
11	17140	5279	21529	28	28
26	116174	27101	95757	84	84
25	57635	16373	85584	80	79
38	66198	19716	143983	130	127
44	71701	17753	75851	82	78
30	57793	9028	59238	60	60
40	80444	18653	93163	131	131
34	53855	8828	96037	84	84
47	97668	29498	151511	140	133
30	133824	27563	136368	151	150
31	101481	18293	112642	91	91
23	99645	22530	94728	138	132
36	114789	15977	105499	150	136
36	99052	35082	121527	124	124
30	67654	16116	127766	119	118
25	65553	15849	98958	73	70
39	97500	16026	77900	110	107
34	69112	26569	85646	123	119
31	82753	24785	98579	90	89
31	85323	17569	130767	116	112
33	72654	23825	131741	113	108
25	30727	7869	53907	56	52
33	77873	14975	178812	115	112
35	117478	37791	146761	119	116
42	74007	9605	82036	129	123
43	90183	27295	163253	127	125
30	61542	2746	27032	27	27
33	101494	34461	171975	175	162
13	27570	8098	65990	35	32
32	55813	4787	86572	64	64
36	79215	24919	159676	96	92
0	1423	603	1929	0	0
28	55461	16329	85371	84	83
14	31081	12558	58391	41	41
17	22996	7784	31580	47	47
32	83122	28522	136815	126	120
30	70106	22265	120642	105	105
35	60578	14459	69107	80	79
20	39992	14526	50495	70	65
28	79892	22240	108016	73	70
28	49810	11802	46341	57	55
39	71570	7623	78348	40	39
34	100708	11912	79336	68	67
26	33032	7935	56968	21	21
39	82875	18220	93176	127	127
39	139077	19199	161632	154	152
33	71595	19918	87850	116	113
28	72260	21884	127969	102	99
4	5950	2694	15049	7	7
39	115762	15808	155135	148	141
18	32551	3597	25109	21	21
14	31701	5296	45824	35	35
29	80670	25239	102996	112	109
44	143558	29801	160604	137	133
21	117105	18450	158051	135	123
16	23789	7132	44547	26	26
28	120733	34861	162647	230	230
35	105195	35940	174141	181	166
28	73107	16688	60622	71	68
38	132068	24683	179566	147	147
23	149193	46230	184301	190	179
36	46821	10387	75661	64	61
32	87011	21436	96144	105	101
29	95260	30546	129847	107	108
25	55183	19746	117286	94	90
27	106671	15977	71180	116	114
36	73511	22583	109377	106	103
28	92945	17274	85298	143	142
23	78664	16469	73631	81	79
40	70054	14251	86767	89	88
23	22618	3007	23824	26	25
40	74011	16851	93487	84	83
28	83737	21113	82981	113	113
34	69094	17401	73815	120	118
33	93133	23958	94552	110	110
28	95536	23567	132190	134	129
34	225920	13065	128754	54	51
30	62133	15358	66363	96	93
33	61370	14587	67808	78	76
22	43836	12770	61724	51	49
38	106117	24021	131722	121	118
26	38692	9648	68580	38	38
35	84651	20537	106175	145	141
8	56622	7905	55792	59	58
24	15986	4527	25157	27	27
29	95364	30495	76669	91	91
20	26706	7117	57283	48	48
29	89691	17719	105805	68	63
45	67267	27056	129484	58	56
37	126846	33473	72413	150	144
33	41140	9758	87831	74	73
33	102860	21115	96971	181	168
25	51715	7236	71299	65	64
32	55801	13790	77494	97	97
29	111813	32902	120336	121	117
28	120293	25131	93913	99	100
28	138599	30910	136048	152	149
31	161647	35947	181248	188	187
52	115929	29848	146123	138	127
21	24266	6943	32036	40	37
24	162901	42705	186646	254	245
41	109825	31808	102255	87	87
33	129838	26675	168237	178	177
32	37510	8435	64219	51	49
19	43750	7409	19630	49	49
20	40652	14993	76825	73	73
31	87771	36867	115338	176	177
31	85872	33835	109427	94	94
32	89275	24164	118168	120	117
18	44418	12607	84845	66	60
23	192565	22609	153197	56	55
17	35232	5892	29877	39	39
20	40909	17014	63506	66	64
12	13294	5394	22445	27	26
17	32387	9178	47695	65	64
30	140867	6440	68370	58	58
31	120662	21916	146304	98	95
10	21233	4011	38233	25	25
13	44332	5818	42071	26	26
22	61056	18647	50517	77	76
42	101338	20556	103950	130	129
1	1168	238	5841	11	11
9	13497	70	2341	2	2
32	65567	22392	84396	101	101
11	25162	3913	24610	31	28
25	32334	12237	35753	36	36
36	40735	8388	55515	120	89
31	91413	22120	209056	195	193
0	855	338	6622	4	4
24	97068	11727	115814	89	84
13	44339	3704	11609	24	23
8	14116	3988	13155	39	39
13	10288	3030	18274	14	14
19	65622	13520	72875	78	78
18	16563	1421	10112	15	14
33	76643	20923	142775	106	101
40	110681	20237	68847	83	82
22	29011	3219	17659	24	24
38	92696	3769	20112	37	36
24	94785	12252	61023	77	75
8	8773	1888	13983	16	16
35	83209	14497	65176	56	55
43	93815	28864	132432	132	131
43	86687	21721	112494	144	131
14	34553	4821	45109	40	39
41	105547	33644	170875	153	144
38	103487	15923	180759	143	139
45	213688	42935	214921	220	211
31	71220	18864	100226	79	78
13	23517	4977	32043	50	50
28	56926	7785	54454	39	39
31	91721	17939	78876	95	90
40	115168	23436	170745	169	166
30	111194	325	6940	12	12
16	51009	13539	49025	63	57
37	135777	34538	122037	134	133
30	51513	12198	53782	69	69
35	74163	26924	127748	119	119
32	51633	12716	86839	119	119
27	75345	8172	44830	75	65
20	33416	10855	77395	63	61
18	83305	11932	89324	55	49
31	98952	14300	103300	103	101
31	102372	25515	112283	197	196
21	37238	2805	10901	16	15
39	103772	29402	120691	140	136
41	123969	16440	58106	89	89
13	27142	11221	57140	40	40
32	135400	28732	122422	125	123
18	21399	5250	25899	21	21
39	130115	28608	139296	167	163
14	24874	8092	52678	32	29
7	34988	4473	23853	36	35
17	45549	1572	17306	13	13
0	6023	2065	7953	5	5
30	64466	14817	89455	96	96
37	54990	16714	147866	151	151
0	1644	556	4245	6	6
5	6179	2089	21509	13	13
1	3926	2658	7670	3	3
16	32755	10695	66675	57	56
32	34777	1669	14336	23	23
24	73224	16267	53608	61	57
17	27114	7768	30059	21	14
11	20760	7252	29668	43	43
24	37636	6387	22097	20	20
22	65461	18715	96841	82	72
12	30080	7936	41907	90	87
19	24094	8643	27080	25	21
13	69008	7294	35885	60	56
17	54968	4570	41247	61	59
15	46090	7185	28313	85	82
16	27507	10058	36845	43	43
24	10672	2342	16548	25	25
15	34029	8509	36134	41	38
17	46300	13275	55764	26	25
18	24760	6816	28910	38	38
20	18779	1930	13339	12	12
16	21280	8086	25319	29	29
16	40662	10737	66956	49	47
18	28987	8033	47487	46	45
22	22827	7058	52785	41	40
8	18513	6782	44683	31	30
17	30594	5401	35619	41	41
18	24006	6521	21920	26	25
16	27913	10856	45608	23	23
23	42744	2154	7721	14	14
22	12934	6117	20634	16	16
13	22574	5238	29788	25	26
13	41385	4820	31931	21	21
16	18653	5615	37754	32	27
16	18472	4272	32505	9	9
20	30976	8702	40557	35	33
22	63339	15340	94238	42	42
17	25568	8030	44197	68	68
18	33747	9526	43228	32	32
17	4154	1278	4103	6	6
12	19474	4236	44144	68	67
7	35130	3023	32868	33	33
17	39067	7196	27640	84	77
14	13310	3394	14063	46	46
23	65892	6371	28990	30	30
17	4143	1574	4694	0	0
14	28579	9620	42648	36	36
15	51776	6978	64329	47	46
17	21152	4911	21928	20	18
21	38084	8645	25836	50	48
18	27717	8987	22779	30	29
18	32928	5544	40820	30	28
17	11342	3083	27530	34	34
17	19499	6909	32378	33	33
16	16380	3189	10824	34	34
15	36874	6745	39613	37	33
21	48259	16724	60865	83	80
16	16734	4850	19787	32	32
14	28207	7025	20107	30	30
15	30143	6047	36605	43	41
17	41369	7377	40961	41	41
15	45833	9078	48231	51	51
15	29156	4605	39725	19	18
10	35944	3238	21455	37	34
6	36278	8100	23430	33	31
22	45588	9653	62991	41	39
21	45097	8914	49363	54	54
1	3895	786	9604	14	14
18	28394	6700	24552	25	24
17	18632	5788	31493	25	24
4	2325	593	3439	8	8
10	25139	4506	19555	26	26
16	27975	6382	21228	20	19
16	14483	5621	23177	11	11
9	13127	3997	22094	14	14
16	5839	520	2342	3	1
17	24069	8891	38798	40	39
7	3738	999	3255	5	5
15	18625	7067	24261	38	37
14	36341	4639	18511	32	32
14	24548	5654	40798	41	38
18	21792	6928	28893	46	47
12	26263	1514	21425	47	47
16	23686	9238	50276	37	37
21	49303	8204	37643	51	51
19	25659	5926	30377	49	45
16	28904	5785	27126	21	21
1	2781	4	13	1	1
16	29236	5930	42097	44	42
10	19546	3710	24451	26	26
19	22818	705	14335	21	21
12	32689	443	5084	4	4
2	5752	2416	9927	10	10
14	22197	7747	43527	43	43
17	20055	5432	27184	34	34
19	25272	4913	21610	32	31
14	82206	2650	20484	20	19
11	32073	2370	20156	34	34
4	5444	775	6012	6	6
16	20154	5576	18475	12	11
20	36944	1352	12645	24	24
12	8019	3080	11017	16	16
15	30884	10205	37623	72	72
16	19540	6095	35873	27	21

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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=157652&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=157652&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157652&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C113711
C215126

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 137 & 11 \tabularnewline
C2 & 15 & 126 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157652&T=1

[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]137[/C][C]11[/C][/ROW]
[ROW][C]C2[/C][C]15[/C][C]126[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157652&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157652&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C113711
C215126


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = quantiles ; par3 = 2 ; 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')
}