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Author's title

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

Thu, 22 Dec 2011 14:04:08 -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/22/t1324580699ohbp03g5kohzyn7.htm/, Retrieved Fri, 03 May 2024 08:23:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159866, Retrieved Fri, 03 May 2024 08:23:52 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [WS10: pearson] [2011-12-11 13:35:32] [17977ad44e8eb3a4dcd5a9173c81cab3]
- RMP     [Multiple Regression] [WS10: MR] [2011-12-11 14:26:04] [17977ad44e8eb3a4dcd5a9173c81cab3]
- RM        [Recursive Partitioning (Regression Trees)] [WS10: RP2] [2011-12-11 15:20:26] [17977ad44e8eb3a4dcd5a9173c81cab3]
- R PD        [Recursive Partitioning (Regression Trees)] [regression trees] [2011-12-22 17:19:01] [141ef847e2c5f8e947fe4eabcb0cf143]
-                 [Recursive Partitioning (Regression Trees)] [cross validation] [2011-12-22 19:04:08] [1a4698f17d8e7f554418314cf0e4bd67] [Current]

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Dataseries X:

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Histogram

Boxplots

1418	210907	30	112285	24188	146283
869	120982	28	84786	18273	98364
1530	176508	38	83123	14130	86146
2172	179321	30	101193	32287	96933
901	123185	22	38361	8654	79234
463	52746	26	68504	9245	42551
3201	385534	25	119182	33251	195663
371	33170	18	22807	1271	6853
1192	101645	11	17140	5279	21529
1583	149061	26	116174	27101	95757
1439	165446	25	57635	16373	85584
1764	237213	38	66198	19716	143983
1495	173326	44	71701	17753	75851
1373	133131	30	57793	9028	59238
2187	258873	40	80444	18653	93163
1491	180083	34	53855	8828	96037
4041	324799	47	97668	29498	151511
1706	230964	30	133824	27563	136368
2152	236785	31	101481	18293	112642
1036	135473	23	99645	22530	94728
1882	202925	36	114789	15977	105499
1929	215147	36	99052	35082	121527
2242	344297	30	67654	16116	127766
1220	153935	25	65553	15849	98958
1289	132943	39	97500	16026	77900
2515	174724	34	69112	26569	85646
2147	174415	31	82753	24785	98579
2352	225548	31	85323	17569	130767
1638	223632	33	72654	23825	131741
1222	124817	25	30727	7869	53907
1812	221698	33	77873	14975	178812
1677	210767	35	117478	37791	146761
1579	170266	42	74007	9605	82036
1731	260561	43	90183	27295	163253
807	84853	30	61542	2746	27032
2452	294424	33	101494	34461	171975
829	101011	13	27570	8098	65990
1940	215641	32	55813	4787	86572
2662	325107	36	79215	24919	159676
186	7176	0	1423	603	1929
1499	167542	28	55461	16329	85371
865	106408	14	31081	12558	58391
1793	96560	17	22996	7784	31580
2527	265769	32	83122	28522	136815
2747	269651	30	70106	22265	120642
1324	149112	35	60578	14459	69107
2702	175824	20	39992	14526	50495
1383	152871	28	79892	22240	108016
1179	111665	28	49810	11802	46341
2099	116408	39	71570	7623	78348
4308	362301	34	100708	11912	79336
918	78800	26	33032	7935	56968
1831	183167	39	82875	18220	93176
3373	277965	39	139077	19199	161632
1713	150629	33	71595	19918	87850
1438	168809	28	72260	21884	127969
496	24188	4	5950	2694	15049
2253	329267	39	115762	15808	155135
744	65029	18	32551	3597	25109
1161	101097	14	31701	5296	45824
2352	218946	29	80670	25239	102996
2144	244052	44	143558	29801	160604
4691	341570	21	117105	18450	158051
1112	103597	16	23789	7132	44547
2694	233328	28	120733	34861	162647
1973	256462	35	105195	35940	174141
1769	206161	28	73107	16688	60622
3148	311473	38	132068	24683	179566
2474	235800	23	149193	46230	184301
2084	177939	36	46821	10387	75661
1954	207176	32	87011	21436	96144
1226	196553	29	95260	30546	129847
1389	174184	25	55183	19746	117286
1496	143246	27	106671	15977	71180
2269	187559	36	73511	22583	109377
1833	187681	28	92945	17274	85298
1268	119016	23	78664	16469	73631
1943	182192	40	70054	14251	86767
893	73566	23	22618	3007	23824
1762	194979	40	74011	16851	93487
1403	167488	28	83737	21113	82981
1425	143756	34	69094	17401	73815
1857	275541	33	93133	23958	94552
1840	243199	28	95536	23567	132190
1502	182999	34	225920	13065	128754
1441	135649	30	62133	15358	66363
1420	152299	33	61370	14587	67808
1416	120221	22	43836	12770	61724
2970	346485	38	106117	24021	131722
1317	145790	26	38692	9648	68580
1644	193339	35	84651	20537	106175
870	80953	8	56622	7905	55792
1654	122774	24	15986	4527	25157
1054	130585	29	95364	30495	76669
937	112611	20	26706	7117	57283
3004	286468	29	89691	17719	105805
2008	241066	45	67267	27056	129484
2547	148446	37	126846	33473	72413
1885	204713	33	41140	9758	87831
1626	182079	33	102860	21115	96971
1468	140344	25	51715	7236	71299
2445	220516	32	55801	13790	77494
1964	243060	29	111813	32902	120336
1381	162765	28	120293	25131	93913
1369	182613	28	138599	30910	136048
1659	232138	31	161647	35947	181248
2888	265318	52	115929	29848	146123
1290	85574	21	24266	6943	32036
2845	310839	24	162901	42705	186646
1982	225060	41	109825	31808	102255
1904	232317	33	129838	26675	168237
1391	144966	32	37510	8435	64219
602	43287	19	43750	7409	19630
1743	155754	20	40652	14993	76825
1559	164709	31	87771	36867	115338
2014	201940	31	85872	33835	109427
2143	235454	32	89275	24164	118168
2146	220801	18	44418	12607	84845
874	99466	23	192565	22609	153197
1590	92661	17	35232	5892	29877
1590	133328	20	40909	17014	63506
1210	61361	12	13294	5394	22445
2072	125930	17	32387	9178	47695
1281	100750	30	140867	6440	68370
1401	224549	31	120662	21916	146304
834	82316	10	21233	4011	38233
1105	102010	13	44332	5818	42071
1272	101523	22	61056	18647	50517
1944	243511	42	101338	20556	103950
391	22938	1	1168	238	5841
761	41566	9	13497	70	2341
1605	152474	32	65567	22392	84396
530	61857	11	25162	3913	24610
1988	99923	25	32334	12237	35753
1386	132487	36	40735	8388	55515
2395	317394	31	91413	22120	209056
387	21054	0	855	338	6622
1742	209641	24	97068	11727	115814
620	22648	13	44339	3704	11609
449	31414	8	14116	3988	13155
800	46698	13	10288	3030	18274
1684	131698	19	65622	13520	72875
1050	91735	18	16563	1421	10112
2699	244749	33	76643	20923	142775
1606	184510	40	110681	20237	68847
1502	79863	22	29011	3219	17659
1204	128423	38	92696	3769	20112
1138	97839	24	94785	12252	61023
568	38214	8	8773	1888	13983
1459	151101	35	83209	14497	65176
2158	272458	43	93815	28864	132432
1111	172494	43	86687	21721	112494
1421	108043	14	34553	4821	45109
2833	328107	41	105547	33644	170875
1955	250579	38	103487	15923	180759
2922	351067	45	213688	42935	214921
1002	158015	31	71220	18864	100226
1060	98866	13	23517	4977	32043
956	85439	28	56926	7785	54454
2186	229242	31	91721	17939	78876
3604	351619	40	115168	23436	170745
1035	84207	30	111194	325	6940
1417	120445	16	51009	13539	49025
3261	324598	37	135777	34538	122037
1587	131069	30	51513	12198	53782
1424	204271	35	74163	26924	127748
1701	165543	32	51633	12716	86839
1249	141722	27	75345	8172	44830
946	116048	20	33416	10855	77395
1926	250047	18	83305	11932	89324
3352	299775	31	98952	14300	103300
1641	195838	31	102372	25515	112283
2035	173260	21	37238	2805	10901
2312	254488	39	103772	29402	120691
1369	104389	41	123969	16440	58106
1577	136084	13	27142	11221	57140
2201	199476	32	135400	28732	122422
961	92499	18	21399	5250	25899
1900	224330	39	130115	28608	139296
1254	135781	14	24874	8092	52678
1335	74408	7	34988	4473	23853
1597	81240	17	45549	1572	17306
207	14688	0	6023	2065	7953
1645	181633	30	64466	14817	89455
2429	271856	37	54990	16714	147866
151	7199	0	1644	556	4245
474	46660	5	6179	2089	21509
141	17547	1	3926	2658	7670
1639	133368	16	32755	10695	66675
872	95227	32	34777	1669	14336
1318	152601	24	73224	16267	53608
1018	98146	17	27114	7768	30059
1383	79619	11	20760	7252	29668
1314	59194	24	37636	6387	22097
1335	139942	22	65461	18715	96841
1403	118612	12	30080	7936	41907
910	72880	19	24094	8643	27080
616	65475	13	69008	7294	35885
1407	99643	17	54968	4570	41247
771	71965	15	46090	7185	28313
766	77272	16	27507	10058	36845
473	49289	24	10672	2342	16548
1376	135131	15	34029	8509	36134
1232	108446	17	46300	13275	55764
1521	89746	18	24760	6816	28910
572	44296	20	18779	1930	13339
1059	77648	16	21280	8086	25319
1544	181528	16	40662	10737	66956
1230	134019	18	28987	8033	47487
1206	124064	22	22827	7058	52785
1205	92630	8	18513	6782	44683
1255	121848	17	30594	5401	35619
613	52915	18	24006	6521	21920
721	81872	16	27913	10856	45608
1109	58981	23	42744	2154	7721
740	53515	22	12934	6117	20634
1126	60812	13	22574	5238	29788
728	56375	13	41385	4820	31931
689	65490	16	18653	5615	37754
592	80949	16	18472	4272	32505
995	76302	20	30976	8702	40557
1613	104011	22	63339	15340	94238
2048	98104	17	25568	8030	44197
705	67989	18	33747	9526	43228
301	30989	17	4154	1278	4103
1803	135458	12	19474	4236	44144
799	73504	7	35130	3023	32868
861	63123	17	39067	7196	27640
1186	61254	14	13310	3394	14063
1451	74914	23	65892	6371	28990
628	31774	17	4143	1574	4694
1161	81437	14	28579	9620	42648
1463	87186	15	51776	6978	64329
742	50090	17	21152	4911	21928
979	65745	21	38084	8645	25836
675	56653	18	27717	8987	22779
1241	158399	18	32928	5544	40820
676	46455	17	11342	3083	27530
1049	73624	17	19499	6909	32378
620	38395	16	16380	3189	10824
1081	91899	15	36874	6745	39613
1688	139526	21	48259	16724	60865
736	52164	16	16734	4850	19787
617	51567	14	28207	7025	20107
812	70551	15	30143	6047	36605
1051	84856	17	41369	7377	40961
1656	102538	15	45833	9078	48231
705	86678	15	29156	4605	39725
945	85709	10	35944	3238	21455
554	34662	6	36278	8100	23430
1597	150580	22	45588	9653	62991
982	99611	21	45097	8914	49363
222	19349	1	3895	786	9604
1212	99373	18	28394	6700	24552
1143	86230	17	18632	5788	31493
435	30837	4	2325	593	3439
532	31706	10	25139	4506	19555
882	89806	16	27975	6382	21228
608	62088	16	14483	5621	23177
459	40151	9	13127	3997	22094
578	27634	16	5839	520	2342
826	76990	17	24069	8891	38798
509	37460	7	3738	999	3255
717	54157	15	18625	7067	24261
637	49862	14	36341	4639	18511
857	84337	14	24548	5654	40798
830	64175	18	21792	6928	28893
652	59382	12	26263	1514	21425
707	119308	16	23686	9238	50276
954	76702	21	49303	8204	37643
1461	103425	19	25659	5926	30377
672	70344	16	28904	5785	27126
778	43410	1	2781	4	13
1141	104838	16	29236	5930	42097
680	62215	10	19546	3710	24451
1090	69304	19	22818	705	14335
616	53117	12	32689	443	5084
285	19764	2	5752	2416	9927
1145	86680	14	22197	7747	43527
733	84105	17	20055	5432	27184
888	77945	19	25272	4913	21610
849	89113	14	82206	2650	20484
1182	91005	11	32073	2370	20156
528	40248	4	5444	775	6012
642	64187	16	20154	5576	18475
947	50857	20	36944	1352	12645
819	56613	12	8019	3080	11017
757	62792	15	30884	10205	37623
894	72535	16	19540	6095	35873

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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159866&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159866&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159866&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	'Gertrude Mary Cox' @ cox.wessa.net

10-Fold Cross Validation
	Prediction (training)			Prediction (testing)
Actual	C1	C2	CV	C1	C2	CV
C1	1201	95	0.9267	140	14	0.9091
C2	114	1187	0.9124	19	120	0.8633
Overall	-	-	0.9195	-	-	0.8874

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 1201 & 95 & 0.9267 & 140 & 14 & 0.9091 \tabularnewline
C2 & 114 & 1187 & 0.9124 & 19 & 120 & 0.8633 \tabularnewline
Overall & - & - & 0.9195 & - & - & 0.8874 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159866&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]1201[/C][C]95[/C][C]0.9267[/C][C]140[/C][C]14[/C][C]0.9091[/C][/ROW]
[ROW][C]C2[/C][C]114[/C][C]1187[/C][C]0.9124[/C][C]19[/C][C]120[/C][C]0.8633[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.9195[/C][C]-[/C][C]-[/C][C]0.8874[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159866&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159866&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	1201	95	0.9267	140	14	0.9091
C2	114	1187	0.9124	19	120	0.8633
Overall	-	-	0.9195	-	-	0.8874

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2
C1	135	10
C2	11	133

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 135 & 10 \tabularnewline
C2 & 11 & 133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159866&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]135[/C][C]10[/C][/ROW]
[ROW][C]C2[/C][C]11[/C][C]133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159866&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159866&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	135	10
C2	11	133

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):

Parameters (R input):

par1 = 2 ; 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