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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, 16 Dec 2011 09:15:08 -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/16/t1324044935t2a5wv4a56f2zl4.htm/, Retrieved Sun, 05 May 2024 11:50:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155956, Retrieved Sun, 05 May 2024 11:50:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62

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)] [Regression Trees ...] [2011-12-16 14:15:08] [10a6f28c51bb1cb94db47cee32729d66] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1418	396	79	30	94	146283	0.69
869	297	58	28	103	98364	0.81
1530	559	60	38	93	86146	0.49
2172	967	108	30	103	96933	0.54
901	270	49	22	51	79234	0.64
463	143	0	26	70	42551	0.81
3201	1562	121	25	91	195663	0.51
371	109	1	18	22	6853	0.21
1192	371	20	11	38	21529	0.21
1583	656	43	26	93	95757	0.64
1439	511	69	25	60	85584	0.52
1764	655	78	38	123	143983	0.61
1495	465	86	44	148	75851	0.44
1373	525	44	30	90	59238	0.44
2187	885	104	40	124	93163	0.36
1491	497	63	34	70	96037	0.53
4041	1436	158	47	168	151511	0.47
1706	612	102	30	115	136368	0.59
2152	865	77	31	71	112642	0.48
1036	385	82	23	66	94728	0.70
1882	567	115	36	134	105499	0.52
1929	639	101	36	117	121527	0.56
2242	963	80	30	108	127766	0.37
1220	398	50	25	84	98958	0.64
1289	410	83	39	156	77900	0.59
2515	966	123	34	120	85646	0.49
2147	801	73	31	114	98579	0.57
2352	892	81	31	94	130767	0.58
1638	513	105	33	120	131741	0.59
1222	469	47	25	81	53907	0.43
1812	683	105	33	110	178812	0.81
1677	643	94	35	133	146761	0.70
1579	535	44	42	122	82036	0.48
1731	625	114	43	158	163253	0.63
807	264	38	30	109	27032	0.32
2452	992	107	33	124	171975	0.58
829	238	30	13	39	65990	0.65
1940	818	71	32	92	86572	0.40
2662	937	84	36	126	159676	0.49
186	70	0	0	0	1929	0.27
1499	507	59	28	70	85371	0.51
865	260	33	14	37	58391	0.55
1793	503	42	17	38	31580	0.33
2527	927	96	32	120	136815	0.51
2747	1269	106	30	93	120642	0.45
1324	537	56	35	95	69107	0.46
2702	910	57	20	77	50495	0.29
1383	532	59	28	90	108016	0.71
1179	345	39	28	80	46341	0.42
2099	918	34	39	31	78348	0.67
4308	1635	76	34	110	79336	0.22
918	330	20	26	66	56968	0.72
1831	557	91	39	138	93176	0.51
3373	1178	115	39	133	161632	0.58
1713	740	85	33	113	87850	0.58
1438	452	76	28	100	127969	0.76
496	218	8	4	7	15049	0.62
2253	764	79	39	140	155135	0.47
744	255	21	18	61	25109	0.39
1161	454	30	14	41	45824	0.45
2352	866	76	29	96	102996	0.47
2144	574	101	44	164	160604	0.66
4691	1276	94	21	78	158051	0.46
1112	379	27	16	49	44547	0.43
2694	825	92	28	102	162647	0.70
1973	798	123	35	124	174141	0.68
1769	663	75	28	99	60622	0.29
3148	1069	128	38	129	179566	0.58
2474	921	105	23	62	184301	0.78
2084	858	55	36	73	75661	0.43
1954	711	56	32	114	96144	0.46
1226	503	41	29	99	129847	0.66
1389	382	72	25	70	117286	0.67
1496	464	67	27	104	71180	0.50
2269	717	75	36	116	109377	0.58
1833	690	114	28	91	85298	0.45
1268	462	118	23	74	73631	0.62
1943	657	77	40	138	86767	0.48
893	385	22	23	67	23824	0.32
1762	577	66	40	151	93487	0.48
1403	619	69	28	72	82981	0.50
1425	479	105	34	120	73815	0.51
1857	817	116	33	115	94552	0.34
1840	752	88	28	105	132190	0.54
1502	430	73	34	104	128754	0.70
1441	451	99	30	108	66363	0.49
1420	537	62	33	98	67808	0.45
1416	519	53	22	69	61724	0.51
2970	1000	118	38	111	131722	0.38
1317	637	30	26	99	68580	0.47
1644	465	100	35	71	106175	0.55
870	437	49	8	27	55792	0.69
1654	711	24	24	69	25157	0.20
1054	299	67	29	107	76669	0.59
937	248	46	20	73	57283	0.51
3004	1162	57	29	107	105805	0.37
2008	714	75	45	93	129484	0.54
2547	905	135	37	129	72413	0.49
1885	649	68	33	69	87831	0.43
1626	512	124	33	118	96971	0.53
1468	472	33	25	73	71299	0.51
2445	905	98	32	119	77494	0.35
1964	786	58	29	104	120336	0.50
1381	489	68	28	107	93913	0.58
1369	479	81	28	99	136048	0.75
1659	617	131	31	90	181248	0.78
2888	925	110	52	197	146123	0.55
1290	351	37	21	36	32036	0.37
2845	1144	130	24	85	186646	0.60
1982	669	93	41	139	102255	0.45
1904	707	118	33	106	168237	0.72
1391	458	39	32	50	64219	0.44
602	214	13	19	64	19630	0.45
1743	599	74	20	31	76825	0.49
1559	572	81	31	63	115338	0.70
2014	897	109	31	92	109427	0.54
2143	819	151	32	106	118168	0.50
2146	720	51	18	63	84845	0.38
874	273	28	23	69	153197	1.54
1590	508	40	17	41	29877	0.32
1590	506	56	20	56	63506	0.48
1210	451	27	12	25	22445	0.37
2072	699	37	17	65	47695	0.38
1281	407	83	30	93	68370	0.68
1401	465	54	31	114	146304	0.65
834	245	27	10	38	38233	0.46
1105	370	28	13	44	42071	0.41
1272	316	59	22	87	50517	0.50
1944	603	133	42	110	103950	0.43
391	154	12	1	0	5841	0.25
761	229	0	9	27	2341	0.06
1605	577	106	32	83	84396	0.55
530	192	23	11	30	24610	0.40
1988	617	44	25	80	35753	0.36
1386	411	71	36	98	55515	0.42
2395	975	116	31	82	209056	0.66
387	146	4	0	0	6622	0.31
1742	705	62	24	60	115814	0.55
620	184	12	13	28	11609	0.51
449	200	18	8	9	13155	0.42
800	274	14	13	33	18274	0.39
1684	502	60	19	59	72875	0.55
1050	382	7	18	49	10112	0.11
2699	964	98	33	115	142775	0.58
1606	537	64	40	140	68847	0.37
1502	438	29	22	49	17659	0.22
1204	369	32	38	120	20112	0.16
1138	417	25	24	66	61023	0.62
568	276	16	8	21	13983	0.37
1459	514	48	35	124	65176	0.43
2158	822	100	43	152	132432	0.49
1111	389	46	43	139	112494	0.65
1421	466	45	14	38	45109	0.42
2833	1255	129	41	144	170875	0.52
1955	694	130	38	120	180759	0.72
2922	1024	136	45	160	214921	0.61
1002	400	59	31	114	100226	0.63
1060	397	25	13	39	32043	0.32
956	350	32	28	78	54454	0.64
2186	719	63	31	119	78876	0.34
3604	1277	95	40	141	170745	0.49
1035	356	14	30	101	6940	0.08
1417	457	36	16	56	49025	0.41
3261	1402	113	37	133	122037	0.38
1587	600	47	30	83	53782	0.41
1424	480	92	35	116	127748	0.63
1701	595	70	32	90	86839	0.52
1249	436	19	27	36	44830	0.32
946	230	50	20	50	77395	0.67
1926	651	41	18	61	89324	0.36
3352	1367	91	31	97	103300	0.34
1641	564	111	31	98	112283	0.57
2035	716	41	21	78	10901	0.06
2312	747	120	39	117	120691	0.47
1369	467	135	41	148	58106	0.56
1577	671	27	13	41	57140	0.42
2201	861	87	32	105	122422	0.61
961	319	25	18	55	25899	0.28
1900	612	131	39	132	139296	0.62
1254	433	45	14	44	52678	0.39
1335	434	29	7	21	23853	0.32
1597	503	58	17	50	17306	0.21
207	85	4	0	0	7953	0.54
1645	564	47	30	73	89455	0.49
2429	824	109	37	86	147866	0.54
151	74	7	0	0	4245	0.59
474	259	12	5	13	21509	0.46
141	69	0	1	4	7670	0.44
1639	535	37	16	57	66675	0.50
872	239	37	32	48	14336	0.15
1318	438	46	24	46	53608	0.35
1018	459	15	17	48	30059	0.31
1383	426	42	11	32	29668	0.37
1314	288	7	24	68	22097	0.37
1335	498	54	22	87	96841	0.69
1403	454	54	12	43	41907	0.35
910	376	14	19	67	27080	0.37
616	225	16	13	46	35885	0.55
1407	555	33	17	46	41247	0.41
771	252	32	15	56	28313	0.39
766	208	21	16	48	36845	0.48
473	130	15	24	44	16548	0.34
1376	481	38	15	60	36134	0.27
1232	389	22	17	65	55764	0.51
1521	565	28	18	55	28910	0.32
572	173	10	20	38	13339	0.30
1059	278	31	16	52	25319	0.33
1544	609	32	16	60	66956	0.37
1230	422	32	18	54	47487	0.35
1206	445	43	22	86	52785	0.43
1205	387	27	8	24	44683	0.48
1255	339	37	17	52	35619	0.29
613	181	20	18	49	21920	0.41
721	245	32	16	61	45608	0.56
1109	384	0	23	61	7721	0.13
740	212	5	22	81	20634	0.39
1126	399	26	13	43	29788	0.49
728	229	10	13	40	31931	0.57
689	224	27	16	40	37754	0.58
592	203	11	16	56	32505	0.40
995	333	29	20	68	40557	0.53
1613	384	25	22	79	94238	0.91
2048	636	55	17	47	44197	0.45
705	185	23	18	57	43228	0.64
301	93	5	17	41	4103	0.13
1803	581	43	12	29	44144	0.33
799	248	23	7	3	32868	0.45
861	304	34	17	60	27640	0.44
1186	344	36	14	30	14063	0.23
1451	407	35	23	79	28990	0.39
628	170	0	17	47	4694	0.15
1161	312	37	14	40	42648	0.52
1463	507	28	15	48	64329	0.74
742	224	16	17	36	21928	0.44
979	340	26	21	42	25836	0.39
675	168	38	18	49	22779	0.40
1241	443	23	18	57	40820	0.26
676	204	22	17	12	27530	0.59
1049	367	30	17	40	32378	0.44
620	210	16	16	43	10824	0.28
1081	335	18	15	33	39613	0.43
1688	364	28	21	77	60865	0.44
736	178	32	16	43	19787	0.38
617	206	21	14	45	20107	0.39
812	279	23	15	47	36605	0.52
1051	387	29	17	43	40961	0.48
1656	490	50	15	45	48231	0.47
705	238	12	15	50	39725	0.46
945	343	21	10	35	21455	0.25
554	232	18	6	7	23430	0.68
1597	530	27	22	71	62991	0.42
982	291	41	21	67	49363	0.50
222	67	13	1	0	9604	0.50
1212	397	12	18	62	24552	0.25
1143	467	21	17	54	31493	0.37
435	178	8	4	4	3439	0.11
532	175	26	10	25	19555	0.62
882	299	27	16	40	21228	0.24
608	154	13	16	38	23177	0.37
459	106	16	9	19	22094	0.55
578	189	2	16	17	2342	0.08
826	194	42	17	67	38798	0.50
509	135	5	7	14	3255	0.09
717	201	37	15	30	24261	0.45
637	207	17	14	54	18511	0.37
857	280	38	14	35	40798	0.48
830	260	37	18	59	28893	0.45
652	227	29	12	24	21425	0.36
707	239	32	16	58	50276	0.42
954	333	35	21	42	37643	0.49
1461	428	17	19	46	30377	0.29
672	230	20	16	61	27126	0.39
778	292	7	1	3	13	0.00
1141	350	46	16	52	42097	0.40
680	186	24	10	25	24451	0.39
1090	326	40	19	40	14335	0.21
616	155	3	12	32	5084	0.10
285	75	10	2	4	9927	0.50
1145	361	37	14	49	43527	0.50
733	261	17	17	63	27184	0.32
888	299	28	19	67	21610	0.28
849	300	19	14	32	20484	0.23
1182	450	29	11	23	20156	0.22
528	183	8	4	7	6012	0.15
642	238	10	16	54	18475	0.29
947	165	15	20	37	12645	0.25
819	234	15	12	35	11017	0.19
757	176	28	15	51	37623	0.60
894	329	17	16	39	35873	0.49

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'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155956&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155956&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155956&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'AstonUniversity' @ aston.wessa.net






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C112619
C26138

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 126 & 19 \tabularnewline
C2 & 6 & 138 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155956&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]126[/C][C]19[/C][/ROW]
[ROW][C]C2[/C][C]6[/C][C]138[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155956&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155956&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
C112619
C26138


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}