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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 computationFri, 24 Dec 2010 13:47:12 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/24/t1293198311w74ea11ainageui.htm/, Retrieved Tue, 30 Apr 2024 02:09:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114958, Retrieved Tue, 30 Apr 2024 02:09:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146

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]
-   P     [Recursive Partitioning (Regression Trees)] [WS 10 - recursive...] [2010-12-11 16:27:23] [033eb2749a430605d9b2be7c4aac4a0c]
-   P       [Recursive Partitioning (Regression Trees)] [] [2010-12-13 18:26:49] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-             [Recursive Partitioning (Regression Trees)] [] [2010-12-21 21:11:02] [07fa8844ca5618cd0482008937d9acea]
-   PD            [Recursive Partitioning (Regression Trees)] [] [2010-12-24 13:47:12] [a4848c79f7a98c5639a543e143e21e11] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1595	17	60720	319369
0	5565	47	94355	493408
0	601	6	60720	319210
1	188	11	77655	381180
1	7146	105	134028	297978
0	1135	17	62285	290476
1	450	8	59325	292136
1	34	8	60630	314353
1	133	14	65990	339445
1	119	6	59118	303677
0	2053	53	100423	397144
0	4036	63	100269	424898
1	0	0	60720	315380
1	4655	393	60720	341570
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1	1766	73	60438	305959
1	312	14	58598	318690
1	448	40	59781	323361
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1	60	10	61124	314049
1	0	0	60720	315380
1	364	35	47705	298466
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0	1442	118	121173	438493
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1	149	5	62041	313332
1	2212	14	60720	319864
0	7489	322	136996	430866
1	0	0	60720	315380
1	402	26	84990	332743
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1	0	0	60720	315380
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0	7561	87	82425	364304
1	5131	71	86111	292154
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1	40949	481	129352	1398893
0	4980	91	113521	409642
1	272	8	112995	429112
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1	25830	115	245546	1073089
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1	138	6	69817	331420
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1	1866	29	56225	322331
0	42634	156	157214	1629616
1	249	62	54323	292754
1	422	28	57935	318056
1	2675	79	59017	355178
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1	365	8	54820	309560
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0	5336	81	112477	697458
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1	2489	40	67804	238125
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1	0	0	60720	315380
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1	84738	588	-26007	223193
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1	972	34	71642	359335
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1	2099	64	106611	308636
0	3437	47	48022	158492
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1	0	0	60720	315380
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1	483	27	60830	306268
1	0	0	60720	315380
1	2239	21	88590	442882
0	2949	41	82903	378509
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1	365	83	87192	346611
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0	21950	519	114337	856956
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1	141	6	61630	315366
1	572	16	58580	307930
1	13326	102	165548	702380
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0	10	2	61656	316155
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1	18562	155	102313	377934
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1	2220	22	301670	644190
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0	11819	85	106113	574339
1	0	0	60720	315380
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1	1503	39	56364	343466
1	0	0	60720	315380
1	0	0	60720	315380
1	30	4	62045	318098
0	3310	33	75230	448243
1	554	11	79285	325738
0	0	0	60720	315380
1	468	21	60720	312161
1	4917	42	52811	243650
1	3256	66	35250	407159
1	0	0	60720	315380
1	125	13	59734	317698
1	0	0	60720	315380
1	22	2	60722	312502
0	0	0	60720	315380
1	514	52	78780	322378
0	0	0	60720	315380
1	0	0	60720	315380
0	0	0	60720	315380
1	0	0	60720	315380
1	0	0	60720	315380
1	0	0	60720	315380
1	10327	91	88577	640273
1	13718	61	96448	345783
1	3748	9	50350	652925
1	14416	361	49857	439798
0	1526	25	69351	278990
0	666	171	117869	339836
1	2844	67	72683	240897
1	0	0	60720	315380
1	368	17	61167	297141
1	0	0	60720	315380
1	333	13	70811	331323
1	26	1	60896	313880
1	0	0	60720	315380
1	0	0	60720	315380
1	1303	61	69863	309422
1	20	3	60938	315245
1	2384	97	61348	405972
0	203	27	50804	300962
0	71	1	60745	316176
1	53	14	59506	302409
1	562	13	58456	283587
1	622	7	60950	263276
1	645	5	60720	312075
0	1763	24	61600	308336
1	0	0	60720	315380
1	317	9	63915	298700
1	1	6	60719	315372
1	275	4	59500	318745
1	0	0	60720	315380
1	936	30	67939	408881
1	8568	37	32168	786690
0	11528	70	-14545	-83265
0	0	0	60720	315380
1	738	10	60720	321376
1	0	0	60720	315380
1	592	7	64270	276898
1	126	10	60951	315547
1	2	1	60743	315487
1	0	0	60720	315380
0	18014	239	-1710	1405225
0	0	0	60720	315380
0	0	0	60720	315380
0	37704	452	60448	983660
0	63	4	65688	318574
1	1431	34	106885	310768
1	94	3	61360	312887
1	192	4	65276	312339
1	32	1	59988	314964
1	6869	105	117520	379983
1	0	0	60720	315380
1	0	0	60720	315380
1	1	4	60722	315398
0	0	0	60720	315380
1	2328	94	82732	253588
0	209	13	64016	316647
1	28	2	60890	315688
1	176	28	68136	310670
1	1920	15	79420	165404
0	87550	458	153198	4111912
1	520	10	58650	291650
0	0	0	60720	315380
1	1013	2	69770	253468
1	15	8	60831	315688
1	587	12	59595	325699
1	5371	66	87720	446211
1	0	0	60720	315380
1	1012	47	114768	368078
1	0	0	60720	315380
1	876	59	138971	352850
0	162556	1081	213118	6282154
1	43556	247	32648	227132
0	3425	12	83620	283910
1	810	43	74015	236761
1	0	0	60720	315380
0	0	0	60720	315380
0	2365	79	191778	550608
0	1261	33	76114	307528
1	0	0	60720	315380
1	1585	107	93099	355864
1	16189	295	116384	358589
1	0	0	60720	315380
0	0	0	60720	315380
1	10579	92	110309	375195
1	474	20	61977	288985
1	0	0	60720	315380
1	3642	86	90262	458343
1	0	0	60720	315380
0	472	20	80045	269587
1	98	2	61490	315236
1	3999	30	51252	42754
1	0	0	60720	315380
1	621	48	60720	308256
1	0	0	60720	315380
1	0	0	60720	315380
0	30	4	60798	313164
1	0	0	60720	315380
1	746	23	71561	269661
0	0	0	60720	315380
1	0	0	60720	315380
0	0	0	60720	315380
1	0	0	60720	315380
0	4150	150	134759	518365
0	4658	158	156608	233773
1	814	18	39625	301881
1	1002	15	56750	298568
0	496	17	87390	325479
1	389	14	58990	325506
1	12679	110	48020	984885
1	400	152	60720	313267
1	53	6	60349	315793
1	0	0	60720	315380
0	5109	97	67038	215362
1	576	18	113761	314073
1	438	11	58320	298096
1	165	24	62841	325176
1	4069	145	79804	207393
0	23	5	62555	314806
1	1285	107	99489	341340
0	4677	75	90131	426280
0	24811	505	95350	929118
1	167	2	64245	307322
1	0	0	60720	315380
1	4628	62	69159	387475
1	226	5	65745	291787
0	1765	20	77623	247060
1	460	15	63346	329784
1	36	3	60894	315834
1	989	11	58930	304555
0	3055	43	60247	376641
0	0	0	60720	315380
1	0	0	60720	315380
0	0	0	60720	315380
1	0	0	60720	315380
1	0	0	60720	315380
1	13253	947	90829	357760
1	24	8	59818	315637
1	0	0	60720	315380
0	0	0	60720	315380
1	0	0	60720	315380
1	0	0	60720	315380
1	131	6	59661	327071
1	514	15	102725	377516
1	3366	80	108479	299243
0	9327	101	87419	387699
1	384	22	54683	309836
1	17821	373	122844	444477
1	397	10	62710	322327
1	897	5	60720	314913
1	218	21	60720	315553
0	3369	28	87161	688779
1	5702	61	101481	321896
1	8636	855	128294	301607
1	534	7	62620	304485
1	0	0	60720	315380
0	0	0	60720	315380
1	726	3	69980	231861
1	1380	26	60982	347385
1	180	11	59635	316386
0	7285	115	188873	491303
1	880	74	80791	261216
1	1	2	60727	315388
1	0	0	60720	315380
1	96	5	60379	313729
1	1889	45	37527	358649
0	45187	353	234817	1926517
1	288	4	60510	296656
1	1270	26	69206	275311
1	6526	26	55830	-42143
0	0	0	60720	315380
1	226	12	72835	343929
1	694	8	68060	367655
1	0	0	60720	315380
1	249	72	56726	313491

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114958&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114958&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114958&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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C1117102
C20212

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 117 & 102 \tabularnewline
C2 & 0 & 212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114958&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]117[/C][C]102[/C][/ROW]
[ROW][C]C2[/C][C]0[/C][C]212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114958&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114958&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
C1117102
C20212


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 4 ; 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')
}