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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

Fri, 23 Dec 2011 14:56:37 -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/23/t132467024710udrmytq7sx9pc.htm/, Retrieved Mon, 29 Apr 2024 19:19:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160686, Retrieved Mon, 29 Apr 2024 19:19:21 +0000

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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] [WS10 PCM] [2011-12-13 18:50:04] [43a0606d8103c0ba382f0586f4417c48]
- R P   [Kendall tau Correlation Matrix] [WS10 KCM] [2011-12-13 18:54:40] [43a0606d8103c0ba382f0586f4417c48]
-   PD    [Kendall tau Correlation Matrix] [WS10 PCM] [2011-12-13 19:11:43] [43a0606d8103c0ba382f0586f4417c48]
-           [Kendall tau Correlation Matrix] [WS10 KCM] [2011-12-13 19:12:31] [43a0606d8103c0ba382f0586f4417c48]
-   PD        [Kendall tau Correlation Matrix] [Paper Kendall] [2011-12-23 19:01:13] [43a0606d8103c0ba382f0586f4417c48]
- RMP           [Multiple Regression] [Paper MR] [2011-12-23 19:29:04] [43a0606d8103c0ba382f0586f4417c48]
- R P             [Multiple Regression] [Paper MR] [2011-12-23 19:41:05] [43a0606d8103c0ba382f0586f4417c48]
- RMP                 [Recursive Partitioning (Regression Trees)] [Paper RT] [2011-12-23 19:56:37] [635499bc27d9f41bf7bccae25a54e146] [Current]

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Dataseries X:

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Histogram

Boxplots

97	197426	39	178377	490
146	187326	173	250931	2563
116	184923	165	226168	1538
113	183500	181	211381	898
75	176225	139	214738	1212
228	169707	166	210012	790
138	169265	116	163073	738
153	167949	114	164263	845
248	165986	155	189944	1369
161	165933	127	147581	1830
155	165904	107	127667	711
142	160902	126	106330	992
145	160141	161	175721	1272
159	156349	185	169216	852
153	154771	63	18284	575
130	154451	121	134969	1101
177	151911	150	191889	1410
181	151715	160	197765	1352
140	150491	132	194679	1208
196	150047	147	75767	739
140	149959	176	195894	926
175	149695	88	191179	865
155	147172	82	178303	677
147	146975	75	135599	971
177	146760	128	195791	1574
159	144551	165	81716	1051
132	144408	88	115466	763
94	144244	62	88229	724
140	143592	93	113963	652
130	140824	96	186099	504
176	140358	121	117495	893
153	140015	146	145758	1034
179	139165	143	184531	1111
197	136588	135	134163	692
163	135356	76	91502	740
170	134238	152	191469	1716
145	134047	163	231257	884
129	131072	154	114268	925
57	128692	48	100187	723
144	127654	168	105590	732
92	126817	143	94333	637
144	126372	156	165278	1266
95	125818	103	111669	527
126	125386	128	134218	811
97	125081	121	135213	1390
144	124089	96	130332	1613
137	123534	76	100922	459
155	120192	161	197680	1118
163	119442	141	189723	1293
227	118906	103	102509	636
187	117440	137	157384	1031
148	116066	55	24469	524
208	114948	176	165354	1775
136	114799	66	153242	669
134	114360	101	79367	2089
149	113344	155	230054	1230
160	112431	123	140303	847
187	112302	145	198299	906
146	112098	134	106194	1154
102	110529	164	232241	1251
143	110459	75	113854	510
115	109432	148	116938	698
151	108535	85	118845	1586
144	108146	140	100125	1001
118	105079	70	99776	710
98	104978	117	139292	906
142	104767	103	124527	1030
151	104581	116	116136	1092
171	104128	50	122975	511
156	103925	152	164808	1319
171	103297	139	101345	1186
142	103129	114	158376	1201
148	103037	110	150773	1443
96	102812	120	136323	703
151	102153	98	80716	862
173	102070	94	86480	1031
151	101629	112	188355	1348
77	101382	81	127097	866
135	101047	169	135848	1079
71	100350	62	75882	695
133	100087	102	129711	1229
139	100046	133	128602	1288
201	96125	107	106314	764
141	95893	90	81180	919
158	95676	99	160792	691
126	93879	152	170492	1099
119	93487	84	133252	766
65	93473	57	121850	1150
128	92622	126	134097	1566
147	92280	118	147341	668
90	92059	101	91313	910
169	89626	85	134904	894
150	89506	118	160501	1351
156	89256	129	104864	1187
179	88977	85	111563	784
149	86652	50	114198	758
94	84601	85	105406	816
154	83515	158	96785	1370
103	83248	146	106020	785
148	83243	150	153990	763
84	82317	77	111848	569
144	81897	131	89770	781
203	81625	132	94853	743
160	81351	107	102204	900
152	79756	80	122531	575
147	79089	114	169351	981
111	79011	97	80238	784
89	76173	8	47552	179
87	72128	163	145707	542
121	71571	102	75881	746
146	71154	137	80906	767
100	70168	79	104470	695
127	69867	83	100826	1186
153	69652	56	33750	456
87	69446	87	113713	724
129	68946	164	174586	1145
113	68788	57	72591	785
124	67150	110	114651	905
92	66485	104	110896	661
112	66089	65	61394	507
102	65594	48	92795	632
115	64593	60	72558	790
148	64520	68	54518	488
135	59938	149	82390	1128
97	59900	104	96252	1257
59	57224	86	80684	800
101	56750	89	115750	846
27	56622	49	55792	437
112	55918	74	83963	795
89	52789	37	15673	309
40	48029	120	88634	833
130	45724	87	74151	641
73	43929	83	100792	415
64	43750	13	19630	214
99	38692	30	68580	657
78	37238	41	10901	716
110	37110	67	64057	665

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=160686&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=160686&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160686&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

Goodness of Fit
Correlation	0.6784
R-squared	0.4603
RMSE	26715.1341

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6784 \tabularnewline
R-squared & 0.4603 \tabularnewline
RMSE & 26715.1341 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160686&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6784[/C][/ROW]
[ROW][C]R-squared[/C][C]0.4603[/C][/ROW]
[ROW][C]RMSE[/C][C]26715.1341[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160686&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160686&T=1

As an alternative you can also use a QR Code:

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

Goodness of Fit
Correlation	0.6784
R-squared	0.4603
RMSE	26715.1341

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	197426	147945.96	49480.04
2	187326	147945.96	39380.04
3	184923	147945.96	36977.04
4	183500	147945.96	35554.04
5	176225	147945.96	28279.04
6	169707	147945.96	21761.04
7	169265	110517.535211268	58747.4647887324
8	167949	110517.535211268	57431.4647887324
9	165986	147945.96	18040.04
10	165933	110517.535211268	55415.4647887324
11	165904	110517.535211268	55386.4647887324
12	160902	110517.535211268	50384.4647887324
13	160141	147945.96	12195.04
14	156349	110517.535211268	45831.4647887324
15	154771	110517.535211268	44253.4647887324
16	154451	110517.535211268	43933.4647887324
17	151911	147945.96	3965.04000000001
18	151715	147945.96	3769.04000000001
19	150491	147945.96	2545.04000000001
20	150047	110517.535211268	39529.4647887324
21	149959	147945.96	2013.04000000001
22	149695	147945.96	1749.04000000001
23	147172	147945.96	-773.959999999992
24	146975	110517.535211268	36457.4647887324
25	146760	147945.96	-1185.95999999999
26	144551	110517.535211268	34033.4647887324
27	144408	110517.535211268	33890.4647887324
28	144244	86683	57561
29	143592	110517.535211268	33074.4647887324
30	140824	147945.96	-7121.95999999999
31	140358	110517.535211268	29840.4647887324
32	140015	110517.535211268	29497.4647887324
33	139165	147945.96	-8780.95999999999
34	136588	110517.535211268	26070.4647887324
35	135356	110517.535211268	24838.4647887324
36	134238	147945.96	-13707.96
37	134047	147945.96	-13898.96
38	131072	110517.535211268	20554.4647887324
39	128692	86683	42009
40	127654	110517.535211268	17136.4647887324
41	126817	86683	40134
42	126372	110517.535211268	15854.4647887324
43	125818	86683	39135
44	125386	110517.535211268	14868.4647887324
45	125081	86683	38398
46	124089	110517.535211268	13571.4647887324
47	123534	110517.535211268	13016.4647887324
48	120192	147945.96	-27753.96
49	119442	147945.96	-28503.96
50	118906	110517.535211268	8388.4647887324
51	117440	110517.535211268	6922.4647887324
52	116066	110517.535211268	5548.4647887324
53	114948	110517.535211268	4430.4647887324
54	114799	110517.535211268	4281.4647887324
55	114360	110517.535211268	3842.4647887324
56	113344	147945.96	-34601.96
57	112431	110517.535211268	1913.4647887324
58	112302	147945.96	-35643.96
59	112098	110517.535211268	1580.4647887324
60	110529	147945.96	-37416.96
61	110459	110517.535211268	-58.5352112676046
62	109432	86683	22749
63	108535	110517.535211268	-1982.5352112676
64	108146	110517.535211268	-2371.5352112676
65	105079	86683	18396
66	104978	86683	18295
67	104767	110517.535211268	-5750.5352112676
68	104581	110517.535211268	-5936.5352112676
69	104128	110517.535211268	-6389.5352112676
70	103925	110517.535211268	-6592.5352112676
71	103297	110517.535211268	-7220.5352112676
72	103129	110517.535211268	-7388.5352112676
73	103037	110517.535211268	-7480.5352112676
74	102812	86683	16129
75	102153	110517.535211268	-8364.5352112676
76	102070	110517.535211268	-8447.5352112676
77	101629	147945.96	-46316.96
78	101382	86683	14699
79	101047	110517.535211268	-9470.5352112676
80	100350	86683	13667
81	100087	110517.535211268	-10430.5352112676
82	100046	110517.535211268	-10471.5352112676
83	96125	110517.535211268	-14392.5352112676
84	95893	110517.535211268	-14624.5352112676
85	95676	110517.535211268	-14841.5352112676
86	93879	110517.535211268	-16638.5352112676
87	93487	86683	6804
88	93473	86683	6790
89	92622	110517.535211268	-17895.5352112676
90	92280	110517.535211268	-18237.5352112676
91	92059	86683	5376
92	89626	110517.535211268	-20891.5352112676
93	89506	110517.535211268	-21011.5352112676
94	89256	110517.535211268	-21261.5352112676
95	88977	110517.535211268	-21540.5352112676
96	86652	110517.535211268	-23865.5352112676
97	84601	86683	-2082
98	83515	110517.535211268	-27002.5352112676
99	83248	86683	-3435
100	83243	110517.535211268	-27274.5352112676
101	82317	86683	-4366
102	81897	110517.535211268	-28620.5352112676
103	81625	110517.535211268	-28892.5352112676
104	81351	110517.535211268	-29166.5352112676
105	79756	110517.535211268	-30761.5352112676
106	79089	110517.535211268	-31428.5352112676
107	79011	86683	-7672
108	76173	54184.4	21988.6
109	72128	86683	-14555
110	71571	86683	-15112
111	71154	110517.535211268	-39363.5352112676
112	70168	86683	-16515
113	69867	110517.535211268	-40650.5352112676
114	69652	110517.535211268	-40865.5352112676
115	69446	86683	-17237
116	68946	110517.535211268	-41571.5352112676
117	68788	54184.4	14603.6
118	67150	86683	-19533
119	66485	86683	-20198
120	66089	54184.4	11904.6
121	65594	86683	-21089
122	64593	54184.4	10408.6
123	64520	110517.535211268	-45997.5352112676
124	59938	110517.535211268	-50579.5352112676
125	59900	86683	-26783
126	57224	86683	-29459
127	56750	86683	-29933
128	56622	54184.4	2437.6
129	55918	86683	-30765
130	52789	54184.4	-1395.4
131	48029	86683	-38654
132	45724	110517.535211268	-64793.5352112676
133	43929	86683	-42754
134	43750	54184.4	-10434.4
135	38692	54184.4	-15492.4
136	37238	54184.4	-16946.4
137	37110	54184.4	-17074.4

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 197426 & 147945.96 & 49480.04 \tabularnewline
2 & 187326 & 147945.96 & 39380.04 \tabularnewline
3 & 184923 & 147945.96 & 36977.04 \tabularnewline
4 & 183500 & 147945.96 & 35554.04 \tabularnewline
5 & 176225 & 147945.96 & 28279.04 \tabularnewline
6 & 169707 & 147945.96 & 21761.04 \tabularnewline
7 & 169265 & 110517.535211268 & 58747.4647887324 \tabularnewline
8 & 167949 & 110517.535211268 & 57431.4647887324 \tabularnewline
9 & 165986 & 147945.96 & 18040.04 \tabularnewline
10 & 165933 & 110517.535211268 & 55415.4647887324 \tabularnewline
11 & 165904 & 110517.535211268 & 55386.4647887324 \tabularnewline
12 & 160902 & 110517.535211268 & 50384.4647887324 \tabularnewline
13 & 160141 & 147945.96 & 12195.04 \tabularnewline
14 & 156349 & 110517.535211268 & 45831.4647887324 \tabularnewline
15 & 154771 & 110517.535211268 & 44253.4647887324 \tabularnewline
16 & 154451 & 110517.535211268 & 43933.4647887324 \tabularnewline
17 & 151911 & 147945.96 & 3965.04000000001 \tabularnewline
18 & 151715 & 147945.96 & 3769.04000000001 \tabularnewline
19 & 150491 & 147945.96 & 2545.04000000001 \tabularnewline
20 & 150047 & 110517.535211268 & 39529.4647887324 \tabularnewline
21 & 149959 & 147945.96 & 2013.04000000001 \tabularnewline
22 & 149695 & 147945.96 & 1749.04000000001 \tabularnewline
23 & 147172 & 147945.96 & -773.959999999992 \tabularnewline
24 & 146975 & 110517.535211268 & 36457.4647887324 \tabularnewline
25 & 146760 & 147945.96 & -1185.95999999999 \tabularnewline
26 & 144551 & 110517.535211268 & 34033.4647887324 \tabularnewline
27 & 144408 & 110517.535211268 & 33890.4647887324 \tabularnewline
28 & 144244 & 86683 & 57561 \tabularnewline
29 & 143592 & 110517.535211268 & 33074.4647887324 \tabularnewline
30 & 140824 & 147945.96 & -7121.95999999999 \tabularnewline
31 & 140358 & 110517.535211268 & 29840.4647887324 \tabularnewline
32 & 140015 & 110517.535211268 & 29497.4647887324 \tabularnewline
33 & 139165 & 147945.96 & -8780.95999999999 \tabularnewline
34 & 136588 & 110517.535211268 & 26070.4647887324 \tabularnewline
35 & 135356 & 110517.535211268 & 24838.4647887324 \tabularnewline
36 & 134238 & 147945.96 & -13707.96 \tabularnewline
37 & 134047 & 147945.96 & -13898.96 \tabularnewline
38 & 131072 & 110517.535211268 & 20554.4647887324 \tabularnewline
39 & 128692 & 86683 & 42009 \tabularnewline
40 & 127654 & 110517.535211268 & 17136.4647887324 \tabularnewline
41 & 126817 & 86683 & 40134 \tabularnewline
42 & 126372 & 110517.535211268 & 15854.4647887324 \tabularnewline
43 & 125818 & 86683 & 39135 \tabularnewline
44 & 125386 & 110517.535211268 & 14868.4647887324 \tabularnewline
45 & 125081 & 86683 & 38398 \tabularnewline
46 & 124089 & 110517.535211268 & 13571.4647887324 \tabularnewline
47 & 123534 & 110517.535211268 & 13016.4647887324 \tabularnewline
48 & 120192 & 147945.96 & -27753.96 \tabularnewline
49 & 119442 & 147945.96 & -28503.96 \tabularnewline
50 & 118906 & 110517.535211268 & 8388.4647887324 \tabularnewline
51 & 117440 & 110517.535211268 & 6922.4647887324 \tabularnewline
52 & 116066 & 110517.535211268 & 5548.4647887324 \tabularnewline
53 & 114948 & 110517.535211268 & 4430.4647887324 \tabularnewline
54 & 114799 & 110517.535211268 & 4281.4647887324 \tabularnewline
55 & 114360 & 110517.535211268 & 3842.4647887324 \tabularnewline
56 & 113344 & 147945.96 & -34601.96 \tabularnewline
57 & 112431 & 110517.535211268 & 1913.4647887324 \tabularnewline
58 & 112302 & 147945.96 & -35643.96 \tabularnewline
59 & 112098 & 110517.535211268 & 1580.4647887324 \tabularnewline
60 & 110529 & 147945.96 & -37416.96 \tabularnewline
61 & 110459 & 110517.535211268 & -58.5352112676046 \tabularnewline
62 & 109432 & 86683 & 22749 \tabularnewline
63 & 108535 & 110517.535211268 & -1982.5352112676 \tabularnewline
64 & 108146 & 110517.535211268 & -2371.5352112676 \tabularnewline
65 & 105079 & 86683 & 18396 \tabularnewline
66 & 104978 & 86683 & 18295 \tabularnewline
67 & 104767 & 110517.535211268 & -5750.5352112676 \tabularnewline
68 & 104581 & 110517.535211268 & -5936.5352112676 \tabularnewline
69 & 104128 & 110517.535211268 & -6389.5352112676 \tabularnewline
70 & 103925 & 110517.535211268 & -6592.5352112676 \tabularnewline
71 & 103297 & 110517.535211268 & -7220.5352112676 \tabularnewline
72 & 103129 & 110517.535211268 & -7388.5352112676 \tabularnewline
73 & 103037 & 110517.535211268 & -7480.5352112676 \tabularnewline
74 & 102812 & 86683 & 16129 \tabularnewline
75 & 102153 & 110517.535211268 & -8364.5352112676 \tabularnewline
76 & 102070 & 110517.535211268 & -8447.5352112676 \tabularnewline
77 & 101629 & 147945.96 & -46316.96 \tabularnewline
78 & 101382 & 86683 & 14699 \tabularnewline
79 & 101047 & 110517.535211268 & -9470.5352112676 \tabularnewline
80 & 100350 & 86683 & 13667 \tabularnewline
81 & 100087 & 110517.535211268 & -10430.5352112676 \tabularnewline
82 & 100046 & 110517.535211268 & -10471.5352112676 \tabularnewline
83 & 96125 & 110517.535211268 & -14392.5352112676 \tabularnewline
84 & 95893 & 110517.535211268 & -14624.5352112676 \tabularnewline
85 & 95676 & 110517.535211268 & -14841.5352112676 \tabularnewline
86 & 93879 & 110517.535211268 & -16638.5352112676 \tabularnewline
87 & 93487 & 86683 & 6804 \tabularnewline
88 & 93473 & 86683 & 6790 \tabularnewline
89 & 92622 & 110517.535211268 & -17895.5352112676 \tabularnewline
90 & 92280 & 110517.535211268 & -18237.5352112676 \tabularnewline
91 & 92059 & 86683 & 5376 \tabularnewline
92 & 89626 & 110517.535211268 & -20891.5352112676 \tabularnewline
93 & 89506 & 110517.535211268 & -21011.5352112676 \tabularnewline
94 & 89256 & 110517.535211268 & -21261.5352112676 \tabularnewline
95 & 88977 & 110517.535211268 & -21540.5352112676 \tabularnewline
96 & 86652 & 110517.535211268 & -23865.5352112676 \tabularnewline
97 & 84601 & 86683 & -2082 \tabularnewline
98 & 83515 & 110517.535211268 & -27002.5352112676 \tabularnewline
99 & 83248 & 86683 & -3435 \tabularnewline
100 & 83243 & 110517.535211268 & -27274.5352112676 \tabularnewline
101 & 82317 & 86683 & -4366 \tabularnewline
102 & 81897 & 110517.535211268 & -28620.5352112676 \tabularnewline
103 & 81625 & 110517.535211268 & -28892.5352112676 \tabularnewline
104 & 81351 & 110517.535211268 & -29166.5352112676 \tabularnewline
105 & 79756 & 110517.535211268 & -30761.5352112676 \tabularnewline
106 & 79089 & 110517.535211268 & -31428.5352112676 \tabularnewline
107 & 79011 & 86683 & -7672 \tabularnewline
108 & 76173 & 54184.4 & 21988.6 \tabularnewline
109 & 72128 & 86683 & -14555 \tabularnewline
110 & 71571 & 86683 & -15112 \tabularnewline
111 & 71154 & 110517.535211268 & -39363.5352112676 \tabularnewline
112 & 70168 & 86683 & -16515 \tabularnewline
113 & 69867 & 110517.535211268 & -40650.5352112676 \tabularnewline
114 & 69652 & 110517.535211268 & -40865.5352112676 \tabularnewline
115 & 69446 & 86683 & -17237 \tabularnewline
116 & 68946 & 110517.535211268 & -41571.5352112676 \tabularnewline
117 & 68788 & 54184.4 & 14603.6 \tabularnewline
118 & 67150 & 86683 & -19533 \tabularnewline
119 & 66485 & 86683 & -20198 \tabularnewline
120 & 66089 & 54184.4 & 11904.6 \tabularnewline
121 & 65594 & 86683 & -21089 \tabularnewline
122 & 64593 & 54184.4 & 10408.6 \tabularnewline
123 & 64520 & 110517.535211268 & -45997.5352112676 \tabularnewline
124 & 59938 & 110517.535211268 & -50579.5352112676 \tabularnewline
125 & 59900 & 86683 & -26783 \tabularnewline
126 & 57224 & 86683 & -29459 \tabularnewline
127 & 56750 & 86683 & -29933 \tabularnewline
128 & 56622 & 54184.4 & 2437.6 \tabularnewline
129 & 55918 & 86683 & -30765 \tabularnewline
130 & 52789 & 54184.4 & -1395.4 \tabularnewline
131 & 48029 & 86683 & -38654 \tabularnewline
132 & 45724 & 110517.535211268 & -64793.5352112676 \tabularnewline
133 & 43929 & 86683 & -42754 \tabularnewline
134 & 43750 & 54184.4 & -10434.4 \tabularnewline
135 & 38692 & 54184.4 & -15492.4 \tabularnewline
136 & 37238 & 54184.4 & -16946.4 \tabularnewline
137 & 37110 & 54184.4 & -17074.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160686&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]197426[/C][C]147945.96[/C][C]49480.04[/C][/ROW]
[ROW][C]2[/C][C]187326[/C][C]147945.96[/C][C]39380.04[/C][/ROW]
[ROW][C]3[/C][C]184923[/C][C]147945.96[/C][C]36977.04[/C][/ROW]
[ROW][C]4[/C][C]183500[/C][C]147945.96[/C][C]35554.04[/C][/ROW]
[ROW][C]5[/C][C]176225[/C][C]147945.96[/C][C]28279.04[/C][/ROW]
[ROW][C]6[/C][C]169707[/C][C]147945.96[/C][C]21761.04[/C][/ROW]
[ROW][C]7[/C][C]169265[/C][C]110517.535211268[/C][C]58747.4647887324[/C][/ROW]
[ROW][C]8[/C][C]167949[/C][C]110517.535211268[/C][C]57431.4647887324[/C][/ROW]
[ROW][C]9[/C][C]165986[/C][C]147945.96[/C][C]18040.04[/C][/ROW]
[ROW][C]10[/C][C]165933[/C][C]110517.535211268[/C][C]55415.4647887324[/C][/ROW]
[ROW][C]11[/C][C]165904[/C][C]110517.535211268[/C][C]55386.4647887324[/C][/ROW]
[ROW][C]12[/C][C]160902[/C][C]110517.535211268[/C][C]50384.4647887324[/C][/ROW]
[ROW][C]13[/C][C]160141[/C][C]147945.96[/C][C]12195.04[/C][/ROW]
[ROW][C]14[/C][C]156349[/C][C]110517.535211268[/C][C]45831.4647887324[/C][/ROW]
[ROW][C]15[/C][C]154771[/C][C]110517.535211268[/C][C]44253.4647887324[/C][/ROW]
[ROW][C]16[/C][C]154451[/C][C]110517.535211268[/C][C]43933.4647887324[/C][/ROW]
[ROW][C]17[/C][C]151911[/C][C]147945.96[/C][C]3965.04000000001[/C][/ROW]
[ROW][C]18[/C][C]151715[/C][C]147945.96[/C][C]3769.04000000001[/C][/ROW]
[ROW][C]19[/C][C]150491[/C][C]147945.96[/C][C]2545.04000000001[/C][/ROW]
[ROW][C]20[/C][C]150047[/C][C]110517.535211268[/C][C]39529.4647887324[/C][/ROW]
[ROW][C]21[/C][C]149959[/C][C]147945.96[/C][C]2013.04000000001[/C][/ROW]
[ROW][C]22[/C][C]149695[/C][C]147945.96[/C][C]1749.04000000001[/C][/ROW]
[ROW][C]23[/C][C]147172[/C][C]147945.96[/C][C]-773.959999999992[/C][/ROW]
[ROW][C]24[/C][C]146975[/C][C]110517.535211268[/C][C]36457.4647887324[/C][/ROW]
[ROW][C]25[/C][C]146760[/C][C]147945.96[/C][C]-1185.95999999999[/C][/ROW]
[ROW][C]26[/C][C]144551[/C][C]110517.535211268[/C][C]34033.4647887324[/C][/ROW]
[ROW][C]27[/C][C]144408[/C][C]110517.535211268[/C][C]33890.4647887324[/C][/ROW]
[ROW][C]28[/C][C]144244[/C][C]86683[/C][C]57561[/C][/ROW]
[ROW][C]29[/C][C]143592[/C][C]110517.535211268[/C][C]33074.4647887324[/C][/ROW]
[ROW][C]30[/C][C]140824[/C][C]147945.96[/C][C]-7121.95999999999[/C][/ROW]
[ROW][C]31[/C][C]140358[/C][C]110517.535211268[/C][C]29840.4647887324[/C][/ROW]
[ROW][C]32[/C][C]140015[/C][C]110517.535211268[/C][C]29497.4647887324[/C][/ROW]
[ROW][C]33[/C][C]139165[/C][C]147945.96[/C][C]-8780.95999999999[/C][/ROW]
[ROW][C]34[/C][C]136588[/C][C]110517.535211268[/C][C]26070.4647887324[/C][/ROW]
[ROW][C]35[/C][C]135356[/C][C]110517.535211268[/C][C]24838.4647887324[/C][/ROW]
[ROW][C]36[/C][C]134238[/C][C]147945.96[/C][C]-13707.96[/C][/ROW]
[ROW][C]37[/C][C]134047[/C][C]147945.96[/C][C]-13898.96[/C][/ROW]
[ROW][C]38[/C][C]131072[/C][C]110517.535211268[/C][C]20554.4647887324[/C][/ROW]
[ROW][C]39[/C][C]128692[/C][C]86683[/C][C]42009[/C][/ROW]
[ROW][C]40[/C][C]127654[/C][C]110517.535211268[/C][C]17136.4647887324[/C][/ROW]
[ROW][C]41[/C][C]126817[/C][C]86683[/C][C]40134[/C][/ROW]
[ROW][C]42[/C][C]126372[/C][C]110517.535211268[/C][C]15854.4647887324[/C][/ROW]
[ROW][C]43[/C][C]125818[/C][C]86683[/C][C]39135[/C][/ROW]
[ROW][C]44[/C][C]125386[/C][C]110517.535211268[/C][C]14868.4647887324[/C][/ROW]
[ROW][C]45[/C][C]125081[/C][C]86683[/C][C]38398[/C][/ROW]
[ROW][C]46[/C][C]124089[/C][C]110517.535211268[/C][C]13571.4647887324[/C][/ROW]
[ROW][C]47[/C][C]123534[/C][C]110517.535211268[/C][C]13016.4647887324[/C][/ROW]
[ROW][C]48[/C][C]120192[/C][C]147945.96[/C][C]-27753.96[/C][/ROW]
[ROW][C]49[/C][C]119442[/C][C]147945.96[/C][C]-28503.96[/C][/ROW]
[ROW][C]50[/C][C]118906[/C][C]110517.535211268[/C][C]8388.4647887324[/C][/ROW]
[ROW][C]51[/C][C]117440[/C][C]110517.535211268[/C][C]6922.4647887324[/C][/ROW]
[ROW][C]52[/C][C]116066[/C][C]110517.535211268[/C][C]5548.4647887324[/C][/ROW]
[ROW][C]53[/C][C]114948[/C][C]110517.535211268[/C][C]4430.4647887324[/C][/ROW]
[ROW][C]54[/C][C]114799[/C][C]110517.535211268[/C][C]4281.4647887324[/C][/ROW]
[ROW][C]55[/C][C]114360[/C][C]110517.535211268[/C][C]3842.4647887324[/C][/ROW]
[ROW][C]56[/C][C]113344[/C][C]147945.96[/C][C]-34601.96[/C][/ROW]
[ROW][C]57[/C][C]112431[/C][C]110517.535211268[/C][C]1913.4647887324[/C][/ROW]
[ROW][C]58[/C][C]112302[/C][C]147945.96[/C][C]-35643.96[/C][/ROW]
[ROW][C]59[/C][C]112098[/C][C]110517.535211268[/C][C]1580.4647887324[/C][/ROW]
[ROW][C]60[/C][C]110529[/C][C]147945.96[/C][C]-37416.96[/C][/ROW]
[ROW][C]61[/C][C]110459[/C][C]110517.535211268[/C][C]-58.5352112676046[/C][/ROW]
[ROW][C]62[/C][C]109432[/C][C]86683[/C][C]22749[/C][/ROW]
[ROW][C]63[/C][C]108535[/C][C]110517.535211268[/C][C]-1982.5352112676[/C][/ROW]
[ROW][C]64[/C][C]108146[/C][C]110517.535211268[/C][C]-2371.5352112676[/C][/ROW]
[ROW][C]65[/C][C]105079[/C][C]86683[/C][C]18396[/C][/ROW]
[ROW][C]66[/C][C]104978[/C][C]86683[/C][C]18295[/C][/ROW]
[ROW][C]67[/C][C]104767[/C][C]110517.535211268[/C][C]-5750.5352112676[/C][/ROW]
[ROW][C]68[/C][C]104581[/C][C]110517.535211268[/C][C]-5936.5352112676[/C][/ROW]
[ROW][C]69[/C][C]104128[/C][C]110517.535211268[/C][C]-6389.5352112676[/C][/ROW]
[ROW][C]70[/C][C]103925[/C][C]110517.535211268[/C][C]-6592.5352112676[/C][/ROW]
[ROW][C]71[/C][C]103297[/C][C]110517.535211268[/C][C]-7220.5352112676[/C][/ROW]
[ROW][C]72[/C][C]103129[/C][C]110517.535211268[/C][C]-7388.5352112676[/C][/ROW]
[ROW][C]73[/C][C]103037[/C][C]110517.535211268[/C][C]-7480.5352112676[/C][/ROW]
[ROW][C]74[/C][C]102812[/C][C]86683[/C][C]16129[/C][/ROW]
[ROW][C]75[/C][C]102153[/C][C]110517.535211268[/C][C]-8364.5352112676[/C][/ROW]
[ROW][C]76[/C][C]102070[/C][C]110517.535211268[/C][C]-8447.5352112676[/C][/ROW]
[ROW][C]77[/C][C]101629[/C][C]147945.96[/C][C]-46316.96[/C][/ROW]
[ROW][C]78[/C][C]101382[/C][C]86683[/C][C]14699[/C][/ROW]
[ROW][C]79[/C][C]101047[/C][C]110517.535211268[/C][C]-9470.5352112676[/C][/ROW]
[ROW][C]80[/C][C]100350[/C][C]86683[/C][C]13667[/C][/ROW]
[ROW][C]81[/C][C]100087[/C][C]110517.535211268[/C][C]-10430.5352112676[/C][/ROW]
[ROW][C]82[/C][C]100046[/C][C]110517.535211268[/C][C]-10471.5352112676[/C][/ROW]
[ROW][C]83[/C][C]96125[/C][C]110517.535211268[/C][C]-14392.5352112676[/C][/ROW]
[ROW][C]84[/C][C]95893[/C][C]110517.535211268[/C][C]-14624.5352112676[/C][/ROW]
[ROW][C]85[/C][C]95676[/C][C]110517.535211268[/C][C]-14841.5352112676[/C][/ROW]
[ROW][C]86[/C][C]93879[/C][C]110517.535211268[/C][C]-16638.5352112676[/C][/ROW]
[ROW][C]87[/C][C]93487[/C][C]86683[/C][C]6804[/C][/ROW]
[ROW][C]88[/C][C]93473[/C][C]86683[/C][C]6790[/C][/ROW]
[ROW][C]89[/C][C]92622[/C][C]110517.535211268[/C][C]-17895.5352112676[/C][/ROW]
[ROW][C]90[/C][C]92280[/C][C]110517.535211268[/C][C]-18237.5352112676[/C][/ROW]
[ROW][C]91[/C][C]92059[/C][C]86683[/C][C]5376[/C][/ROW]
[ROW][C]92[/C][C]89626[/C][C]110517.535211268[/C][C]-20891.5352112676[/C][/ROW]
[ROW][C]93[/C][C]89506[/C][C]110517.535211268[/C][C]-21011.5352112676[/C][/ROW]
[ROW][C]94[/C][C]89256[/C][C]110517.535211268[/C][C]-21261.5352112676[/C][/ROW]
[ROW][C]95[/C][C]88977[/C][C]110517.535211268[/C][C]-21540.5352112676[/C][/ROW]
[ROW][C]96[/C][C]86652[/C][C]110517.535211268[/C][C]-23865.5352112676[/C][/ROW]
[ROW][C]97[/C][C]84601[/C][C]86683[/C][C]-2082[/C][/ROW]
[ROW][C]98[/C][C]83515[/C][C]110517.535211268[/C][C]-27002.5352112676[/C][/ROW]
[ROW][C]99[/C][C]83248[/C][C]86683[/C][C]-3435[/C][/ROW]
[ROW][C]100[/C][C]83243[/C][C]110517.535211268[/C][C]-27274.5352112676[/C][/ROW]
[ROW][C]101[/C][C]82317[/C][C]86683[/C][C]-4366[/C][/ROW]
[ROW][C]102[/C][C]81897[/C][C]110517.535211268[/C][C]-28620.5352112676[/C][/ROW]
[ROW][C]103[/C][C]81625[/C][C]110517.535211268[/C][C]-28892.5352112676[/C][/ROW]
[ROW][C]104[/C][C]81351[/C][C]110517.535211268[/C][C]-29166.5352112676[/C][/ROW]
[ROW][C]105[/C][C]79756[/C][C]110517.535211268[/C][C]-30761.5352112676[/C][/ROW]
[ROW][C]106[/C][C]79089[/C][C]110517.535211268[/C][C]-31428.5352112676[/C][/ROW]
[ROW][C]107[/C][C]79011[/C][C]86683[/C][C]-7672[/C][/ROW]
[ROW][C]108[/C][C]76173[/C][C]54184.4[/C][C]21988.6[/C][/ROW]
[ROW][C]109[/C][C]72128[/C][C]86683[/C][C]-14555[/C][/ROW]
[ROW][C]110[/C][C]71571[/C][C]86683[/C][C]-15112[/C][/ROW]
[ROW][C]111[/C][C]71154[/C][C]110517.535211268[/C][C]-39363.5352112676[/C][/ROW]
[ROW][C]112[/C][C]70168[/C][C]86683[/C][C]-16515[/C][/ROW]
[ROW][C]113[/C][C]69867[/C][C]110517.535211268[/C][C]-40650.5352112676[/C][/ROW]
[ROW][C]114[/C][C]69652[/C][C]110517.535211268[/C][C]-40865.5352112676[/C][/ROW]
[ROW][C]115[/C][C]69446[/C][C]86683[/C][C]-17237[/C][/ROW]
[ROW][C]116[/C][C]68946[/C][C]110517.535211268[/C][C]-41571.5352112676[/C][/ROW]
[ROW][C]117[/C][C]68788[/C][C]54184.4[/C][C]14603.6[/C][/ROW]
[ROW][C]118[/C][C]67150[/C][C]86683[/C][C]-19533[/C][/ROW]
[ROW][C]119[/C][C]66485[/C][C]86683[/C][C]-20198[/C][/ROW]
[ROW][C]120[/C][C]66089[/C][C]54184.4[/C][C]11904.6[/C][/ROW]
[ROW][C]121[/C][C]65594[/C][C]86683[/C][C]-21089[/C][/ROW]
[ROW][C]122[/C][C]64593[/C][C]54184.4[/C][C]10408.6[/C][/ROW]
[ROW][C]123[/C][C]64520[/C][C]110517.535211268[/C][C]-45997.5352112676[/C][/ROW]
[ROW][C]124[/C][C]59938[/C][C]110517.535211268[/C][C]-50579.5352112676[/C][/ROW]
[ROW][C]125[/C][C]59900[/C][C]86683[/C][C]-26783[/C][/ROW]
[ROW][C]126[/C][C]57224[/C][C]86683[/C][C]-29459[/C][/ROW]
[ROW][C]127[/C][C]56750[/C][C]86683[/C][C]-29933[/C][/ROW]
[ROW][C]128[/C][C]56622[/C][C]54184.4[/C][C]2437.6[/C][/ROW]
[ROW][C]129[/C][C]55918[/C][C]86683[/C][C]-30765[/C][/ROW]
[ROW][C]130[/C][C]52789[/C][C]54184.4[/C][C]-1395.4[/C][/ROW]
[ROW][C]131[/C][C]48029[/C][C]86683[/C][C]-38654[/C][/ROW]
[ROW][C]132[/C][C]45724[/C][C]110517.535211268[/C][C]-64793.5352112676[/C][/ROW]
[ROW][C]133[/C][C]43929[/C][C]86683[/C][C]-42754[/C][/ROW]
[ROW][C]134[/C][C]43750[/C][C]54184.4[/C][C]-10434.4[/C][/ROW]
[ROW][C]135[/C][C]38692[/C][C]54184.4[/C][C]-15492.4[/C][/ROW]
[ROW][C]136[/C][C]37238[/C][C]54184.4[/C][C]-16946.4[/C][/ROW]
[ROW][C]137[/C][C]37110[/C][C]54184.4[/C][C]-17074.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160686&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160686&T=2

As an alternative you can also use a QR Code:

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

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	197426	147945.96	49480.04
2	187326	147945.96	39380.04
3	184923	147945.96	36977.04
4	183500	147945.96	35554.04
5	176225	147945.96	28279.04
6	169707	147945.96	21761.04
7	169265	110517.535211268	58747.4647887324
8	167949	110517.535211268	57431.4647887324
9	165986	147945.96	18040.04
10	165933	110517.535211268	55415.4647887324
11	165904	110517.535211268	55386.4647887324
12	160902	110517.535211268	50384.4647887324
13	160141	147945.96	12195.04
14	156349	110517.535211268	45831.4647887324
15	154771	110517.535211268	44253.4647887324
16	154451	110517.535211268	43933.4647887324
17	151911	147945.96	3965.04000000001
18	151715	147945.96	3769.04000000001
19	150491	147945.96	2545.04000000001
20	150047	110517.535211268	39529.4647887324
21	149959	147945.96	2013.04000000001
22	149695	147945.96	1749.04000000001
23	147172	147945.96	-773.959999999992
24	146975	110517.535211268	36457.4647887324
25	146760	147945.96	-1185.95999999999
26	144551	110517.535211268	34033.4647887324
27	144408	110517.535211268	33890.4647887324
28	144244	86683	57561
29	143592	110517.535211268	33074.4647887324
30	140824	147945.96	-7121.95999999999
31	140358	110517.535211268	29840.4647887324
32	140015	110517.535211268	29497.4647887324
33	139165	147945.96	-8780.95999999999
34	136588	110517.535211268	26070.4647887324
35	135356	110517.535211268	24838.4647887324
36	134238	147945.96	-13707.96
37	134047	147945.96	-13898.96
38	131072	110517.535211268	20554.4647887324
39	128692	86683	42009
40	127654	110517.535211268	17136.4647887324
41	126817	86683	40134
42	126372	110517.535211268	15854.4647887324
43	125818	86683	39135
44	125386	110517.535211268	14868.4647887324
45	125081	86683	38398
46	124089	110517.535211268	13571.4647887324
47	123534	110517.535211268	13016.4647887324
48	120192	147945.96	-27753.96
49	119442	147945.96	-28503.96
50	118906	110517.535211268	8388.4647887324
51	117440	110517.535211268	6922.4647887324
52	116066	110517.535211268	5548.4647887324
53	114948	110517.535211268	4430.4647887324
54	114799	110517.535211268	4281.4647887324
55	114360	110517.535211268	3842.4647887324
56	113344	147945.96	-34601.96
57	112431	110517.535211268	1913.4647887324
58	112302	147945.96	-35643.96
59	112098	110517.535211268	1580.4647887324
60	110529	147945.96	-37416.96
61	110459	110517.535211268	-58.5352112676046
62	109432	86683	22749
63	108535	110517.535211268	-1982.5352112676
64	108146	110517.535211268	-2371.5352112676
65	105079	86683	18396
66	104978	86683	18295
67	104767	110517.535211268	-5750.5352112676
68	104581	110517.535211268	-5936.5352112676
69	104128	110517.535211268	-6389.5352112676
70	103925	110517.535211268	-6592.5352112676
71	103297	110517.535211268	-7220.5352112676
72	103129	110517.535211268	-7388.5352112676
73	103037	110517.535211268	-7480.5352112676
74	102812	86683	16129
75	102153	110517.535211268	-8364.5352112676
76	102070	110517.535211268	-8447.5352112676
77	101629	147945.96	-46316.96
78	101382	86683	14699
79	101047	110517.535211268	-9470.5352112676
80	100350	86683	13667
81	100087	110517.535211268	-10430.5352112676
82	100046	110517.535211268	-10471.5352112676
83	96125	110517.535211268	-14392.5352112676
84	95893	110517.535211268	-14624.5352112676
85	95676	110517.535211268	-14841.5352112676
86	93879	110517.535211268	-16638.5352112676
87	93487	86683	6804
88	93473	86683	6790
89	92622	110517.535211268	-17895.5352112676
90	92280	110517.535211268	-18237.5352112676
91	92059	86683	5376
92	89626	110517.535211268	-20891.5352112676
93	89506	110517.535211268	-21011.5352112676
94	89256	110517.535211268	-21261.5352112676
95	88977	110517.535211268	-21540.5352112676
96	86652	110517.535211268	-23865.5352112676
97	84601	86683	-2082
98	83515	110517.535211268	-27002.5352112676
99	83248	86683	-3435
100	83243	110517.535211268	-27274.5352112676
101	82317	86683	-4366
102	81897	110517.535211268	-28620.5352112676
103	81625	110517.535211268	-28892.5352112676
104	81351	110517.535211268	-29166.5352112676
105	79756	110517.535211268	-30761.5352112676
106	79089	110517.535211268	-31428.5352112676
107	79011	86683	-7672
108	76173	54184.4	21988.6
109	72128	86683	-14555
110	71571	86683	-15112
111	71154	110517.535211268	-39363.5352112676
112	70168	86683	-16515
113	69867	110517.535211268	-40650.5352112676
114	69652	110517.535211268	-40865.5352112676
115	69446	86683	-17237
116	68946	110517.535211268	-41571.5352112676
117	68788	54184.4	14603.6
118	67150	86683	-19533
119	66485	86683	-20198
120	66089	54184.4	11904.6
121	65594	86683	-21089
122	64593	54184.4	10408.6
123	64520	110517.535211268	-45997.5352112676
124	59938	110517.535211268	-50579.5352112676
125	59900	86683	-26783
126	57224	86683	-29459
127	56750	86683	-29933
128	56622	54184.4	2437.6
129	55918	86683	-30765
130	52789	54184.4	-1395.4
131	48029	86683	-38654
132	45724	110517.535211268	-64793.5352112676
133	43929	86683	-42754
134	43750	54184.4	-10434.4
135	38692	54184.4	-15492.4
136	37238	54184.4	-16946.4
137	37110	54184.4	-17074.4

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 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 3 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code