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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 computationTue, 11 Dec 2012 20:17:30 -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/2012/Dec/11/t1355275109bv3mzu2t8vbva1k.htm/, Retrieved Fri, 29 Mar 2024 15:30:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198753, Retrieved Fri, 29 Mar 2024 15:30:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111
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]
- R PD  [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2010-12-13 10:44:01] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2010-12-13 14:00:29] [afe9379cca749d06b3d6872e02cc47ed]
- R PD        [Recursive Partitioning (Regression Trees)] [Workshop 10: Recu...] [2012-12-12 01:17:30] [d49503de060cfc16675e2aa8f576ec0a] [Current]
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Dataseries X:
10.81	24563400	-0.2643	24.45	2772.73	0.0373	 115.7	5.98
9.12	14163200	-0.2643	23.62	2151.83	0.0353	 109.2	5.49
11.03	18184800	-0.2643	21.90	1840.26	0.0292	 116.9	5.31
12.74	20810300	-0.1918	27.12	2116.24	0.0327	 109.9	4.8
9.98	12843000	-0.1918	27.70	2110.49	0.0362	 116.1	4.21
11.62	13866700	-0.1918	29.23	2160.54	0.0325	 118.9	3.97
9.40	15119200	-0.2246	26.50	2027.13	0.0272	 116.3	3.77
9.27	8301600	-0.2246	22.84	1805.43	0.0272	 114.0	3.65
7.76	14039600	-0.2246	20.49	1498.80	0.0265	 97.0	3.07
8.78	12139700	0.3654	23.28	1690.20	0.0213	 85.3	2.49
10.65	9649000	0.3654	25.71	1930.58	0.019	 84.9	2.09
10.95	8513600	0.3654	26.52	1950.40	0.0155	 94.6	1.82
12.36	15278600	0.0447	25.51	1934.03	0.0114	 97.8	1.73
10.85	15590900	0.0447	23.36	1731.49	0.0114	 95.0	1.74
11.84	9691100	0.0447	24.15	1845.35	0.0148	 110.7	1.73
12.14	10882700	-0.0312	20.92	1688.23	0.0164	 108.5	1.75
11.65	10294800	-0.0312	20.38	1615.73	0.0118	 110.3	1.75
8.86	16031900	-0.0312	21.90	1463.21	0.0107	 106.3	1.75
7.63	13683600	-0.0048	19.21	1328.26	0.0146	 97.4	1.73
7.38	8677200	-0.0048	19.65	1314.85	0.018	 94.5	1.74
7.25	9874100	-0.0048	17.51	1172.06	0.0151	 93.7	1.75
8.03	10725500	0.0705	21.41	1329.75	0.0203	 79.6	1.75
7.75	8348400	0.0705	23.09	1478.78	0.022	 84.9	1.34
7.16	8046200	0.0705	20.70	1335.51	0.0238	 80.7	1.24
7.18	10862300	-0.0134	19.00	1320.91	0.026	 78.8	1.24
7.51	8100300	-0.0134	19.04	1337.52	0.0298	 64.8	1.26
7.07	7287500	-0.0134	19.45	1341.17	0.0302	 61.4	1.25
7.11	14002500	0.0812	20.54	1464.31	0.0222	 81.0	1.26
8.98	19037900	0.0812	19.77	1595.91	0.0206	 83.6	1.26
9.53	10774600	0.0812	20.60	1622.80	0.0211	 83.5	1.22
10.54	8960600	0.1885	21.21	1735.02	0.0211	 77.0	1.01
11.31	7773300	0.1885	21.30	1810.45	0.0216	 81.7	1.03
10.36	9579700	0.1885	22.33	1786.94	0.0232	 77.0	1.01
11.44	11270700	0.3628	21.12	1932.21	0.0204	 81.7	1.01
10.45	9492800	0.3628	20.77	1960.26	0.0177	 92.5	1
10.69	9136800	0.3628	22.11	2003.37	0.0188	 91.7	0.98
11.28	14487600	0.2942	22.34	2066.15	0.0193	 96.4	1
11.96	10133200	0.2942	21.43	2029.82	0.0169	 88.5	1.01
13.52	18659700	0.2942	20.14	1994.22	0.0174	 88.5	1
12.89	15980700	0.3036	21.11	1920.15	0.0229	 93.0	1
14.03	9732100	0.3036	21.19	1986.74	0.0305	 93.1	1
16.27	14626300	0.3036	23.07	2047.79	0.0327	 102.8	1.03
16.17	16904000	0.3703	23.01	1887.36	0.0299	 105.7	1.26
17.25	13616700	0.3703	22.12	1838.10	0.0265	 98.7	1.43
19.38	13772900	0.3703	22.40	1896.84	0.0254	 96.7	1.61
26.20	28749200	0.7398	22.66	1974.99	0.0319	 92.9	1.76
33.53	31408300	0.7398	24.21	2096.81	0.0352	 92.6	1.93
32.20	26342800	0.7398	24.13	2175.44	0.0326	 102.7	2.16
38.45	48909500	0.6988	23.73	2062.41	0.0297	 105.1	2.28
44.86	41542400	0.6988	22.79	2051.72	0.0301	 104.4	2.5
41.67	24857200	0.6988	21.89	1999.23	0.0315	 103.0	2.63
36.06	34093700	0.7478	22.92	1921.65	0.0351	 97.5	2.79
39.76	22555200	0.7478	23.44	2068.22	0.028	 103.1	3
36.81	19067500	0.7478	22.57	2056.96	0.0253	 106.2	3.04
42.65	19029100	0.5651	23.27	2184.83	0.0317	 103.6	3.26
46.89	15223200	0.5651	24.95	2152.09	0.0364	 105.5	3.5
53.61	21903700	0.5651	23.45	2151.69	0.0469	 87.5	3.62
57.59	33306600	0.6473	23.42	2120.30	0.0435	 85.2	3.78
67.82	23898100	0.6473	25.30	2232.82	0.0346	 98.3	4
71.89	23279600	0.6473	23.90	2205.32	0.0342	 103.8	4.16
75.51	40699800	0.3441	25.73	2305.82	0.0399	 106.8	4.29
68.49	37646000	0.3441	24.64	2281.39	0.036	 102.7	4.49
62.72	37277000	0.3441	24.95	2339.79	0.0336	 107.5	4.59
70.39	39246800	0.2415	22.15	2322.57	0.0355	 109.8	4.79
59.77	27418400	0.2415	20.85	2178.88	0.0417	 104.7	4.94
57.27	30318700	0.2415	21.45	2172.09	0.0432	 105.7	4.99
67.96	32808100	0.3151	22.15	2091.47	0.0415	 107.0	5.24
67.85	28668200	0.3151	23.75	2183.75	0.0382	 100.2	5.25
76.98	32370300	0.3151	25.27	2258.43	0.0206	 105.9	5.25
81.08	24171100	0.239	26.53	2366.71	0.0131	 105.1	5.25
91.66	25009100	0.239	27.22	2431.77	0.0197	 105.3	5.25
84.84	32084300	0.239	27.69	2415.29	0.0254	 110.0	5.24
85.73	50117500	0.2127	28.61	2463.93	0.0208	 110.2	5.25
84.61	27522200	0.2127	26.21	2416.15	0.0242	 111.2	5.26
92.91	26816800	0.2127	25.93	2421.64	0.0278	 108.2	5.26
99.80	25136100	0.273	27.86	2525.09	0.0257	 106.3	5.25
121.19	30295600	0.273	28.65	2604.52	0.0269	 108.5	5.25
122.04	41526100	0.273	27.51	2603.23	0.0269	 105.3	5.25
131.76	43845100	0.3657	27.06	2546.27	0.0236	 111.9	5.26
138.48	39188900	0.3657	26.91	2596.36	0.0197	 105.6	5.02
153.47	40496400	0.3657	27.60	2701.50	0.0276	 99.5	4.94
189.95	37438400	0.4643	34.48	2859.12	0.0354	 95.2	4.76
182.22	46553700	0.4643	31.58	2660.96	0.0431	 87.8	4.49
198.08	31771400	0.4643	33.46	2652.28	0.0408	 90.6	4.24
135.36	62108100	0.5096	30.64	2389.86	0.0428	 87.9	3.94
125.02	46645400	0.5096	25.66	2271.48	0.0403	 76.4	2.98
143.50	42313100	0.5096	26.78	2279.10	0.0398	 65.9	2.61
173.95	38841700	0.3592	26.91	2412.80	0.0394	 62.3	2.28
188.75	32650300	0.3592	26.82	2522.66	0.0418	 57.2	1.98
167.44	34281100	0.3592	26.05	2292.98	0.0502	 50.4	2
158.95	33096200	0.7439	24.36	2325.55	0.056	 51.9	2.01
169.53	23273800	0.7439	25.94	2367.52	0.0537	 58.5	2
113.66	43697600	0.7439	25.37	2091.88	0.0494	 61.4	1.81
107.59	66902300	0.139	21.23	1720.95	0.0366	 38.8	0.97
92.67	44957200	0.139	19.35	1535.57	0.0107	 44.9	0.39
85.35	33800900	0.139	18.61	1577.03	0.0009	 38.6	0.16
90.13	33487900	0.1383	16.37	1476.42	0.0003	 4.0	0.15
89.31	27394900	0.1383	15.56	1377.84	0.0024	 25.3	0.22
105.12	25963400	0.1383	17.70	1528.59	-0.0038	 26.9	0.18
125.83	20952600	0.2874	19.52	1717.30	-0.0074	 40.8	0.15
135.81	17702900	0.2874	20.26	1774.33	-0.0128	 54.8	0.18
142.43	21282100	0.2874	23.05	1835.04	-0.0143	 49.3	0.21
163.39	18449100	0.0596	22.81	1978.50	-0.021	 47.4	0.16
168.21	14415700	0.0596	24.04	2009.06	-0.0148	 54.5	0.16
185.35	17906300	0.0596	25.08	2122.42	-0.0129	 53.4	0.15
188.50	22197500	0.3201	27.04	2045.11	-0.0018	 48.7	0.12
199.91	15856500	0.3201	28.81	2144.60	0.0184	 50.6	0.12
210.73	19068700	0.3201	29.86	2269.15	0.0272	 53.6	0.12
192.06	30855100	0.486	27.61	2147.35	0.0263	 56.5	0.11
204.62	21209000	0.486	28.22	2238.26	0.0214	 46.4	0.13
235.00	19541600	0.486	28.83	2397.96	0.0231	 52.3	0.16
261.09	21955000	0.6129	30.06	2461.19	0.0224	 57.7	0.2
256.88	33725900	0.6129	25.51	2257.04	0.0202	 62.7	0.2
251.53	28192800	0.6129	22.75	2109.24	0.0105	 54.3	0.18
257.25	27377000	0.6665	25.52	2254.70	0.0124	 51.0	0.18
243.10	16228100	0.6665	23.33	2114.03	0.0115	 53.2	0.19
283.75	21278900	0.6665	24.34	2368.62	0.0114	 48.6	0.19




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198753&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198753&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198753&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Goodness of Fit
Correlation0.935
R-squared0.8743
RMSE26.8206

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.935 \tabularnewline
R-squared & 0.8743 \tabularnewline
RMSE & 26.8206 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198753&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.935[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8743[/C][/ROW]
[ROW][C]RMSE[/C][C]26.8206[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198753&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198753&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
Correlation0.935
R-squared0.8743
RMSE26.8206







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
110.8160.485-49.675
29.1211.3089285714286-2.18892857142857
311.0311.3089285714286-0.278928571428573
412.7411.30892857142861.43107142857143
59.9811.3089285714286-1.32892857142857
611.6211.30892857142860.311071428571427
79.411.3089285714286-1.90892857142857
89.2711.3089285714286-2.03892857142857
97.767.716666666666670.043333333333333
108.7811.3089285714286-2.52892857142857
1110.6511.3089285714286-0.658928571428572
1210.9511.3089285714286-0.358928571428573
1312.3611.30892857142861.05107142857143
1410.8511.3089285714286-0.458928571428572
1511.8411.30892857142860.531071428571428
1612.1411.30892857142860.831071428571429
1711.6511.30892857142860.341071428571428
188.867.716666666666671.14333333333333
197.637.71666666666667-0.0866666666666669
207.387.71666666666667-0.336666666666667
217.257.71666666666667-0.466666666666667
228.037.716666666666670.313333333333333
237.757.716666666666670.0333333333333332
247.167.71666666666667-0.556666666666667
257.187.71666666666667-0.536666666666667
267.517.71666666666667-0.206666666666667
277.0798.131-91.061
287.117.71666666666667-0.606666666666666
298.987.716666666666671.26333333333333
309.5311.3089285714286-1.77892857142857
3110.5411.3089285714286-0.768928571428573
3211.3111.30892857142860.00107142857142861
3310.3611.3089285714286-0.948928571428572
3411.4411.30892857142860.131071428571428
3510.4511.3089285714286-0.858928571428573
3610.6911.3089285714286-0.618928571428572
3711.2811.3089285714286-0.0289285714285725
3811.9611.30892857142860.651071428571429
3913.5211.30892857142862.21107142857143
4012.8911.30892857142861.58107142857143
4114.0311.30892857142862.72107142857143
4216.2711.30892857142864.96107142857143
4316.1734.065-17.895
4417.2534.065-16.815
4519.3834.065-14.685
4626.260.485-34.285
4733.5360.485-26.955
4832.260.485-28.285
4938.4560.485-22.035
5044.8660.485-15.625
5141.6760.485-18.815
5236.0660.485-24.425
5339.7634.0655.695
5436.8134.0652.745
5542.6534.0658.585
5646.8934.06512.825
5753.6134.06519.545
5857.5960.485-2.895
5967.8260.4857.33499999999999
6071.8960.48511.405
6175.5160.48515.025
6268.4960.4858.005
6362.7260.4852.235
6470.3960.4859.905
6559.7760.485-0.714999999999996
6657.2760.485-3.215
6767.9660.4857.47499999999999
6867.8560.4857.36499999999999
6976.9860.48516.495
7081.0860.48520.595
7191.66134.148571428571-42.4885714285714
7284.84134.148571428571-49.3085714285714
7385.73134.148571428571-48.4185714285714
7484.6160.48524.125
7592.9160.48532.425
7699.8134.148571428571-34.3485714285714
77121.19134.148571428571-12.9585714285714
78122.04134.148571428571-12.1085714285714
79131.76134.148571428571-2.38857142857142
80138.48134.1485714285714.33142857142857
81153.47134.14857142857119.3214285714286
82189.95134.14857142857155.8014285714286
83182.22134.14857142857148.0714285714286
84198.08134.14857142857163.9314285714286
85135.36134.1485714285711.2114285714286
86125.0260.48564.535
87143.5134.1485714285719.35142857142858
88173.95203.507142857143-29.5571428571429
89188.75203.507142857143-14.7571428571429
90167.44203.507142857143-36.0671428571429
91158.95203.507142857143-44.5571428571429
92169.53203.507142857143-33.9771428571429
93113.66203.507142857143-89.8471428571429
94107.5998.1319.459
9592.6798.131-5.461
9685.3598.131-12.781
9790.1398.131-8.001
9889.3198.131-8.821
99105.1298.1316.989
100125.8398.13127.699
101135.8198.13137.679
102142.4398.13144.299
103163.39203.507142857143-40.1171428571429
104168.21203.507142857143-35.2971428571429
105185.35203.507142857143-18.1571428571429
106188.5203.507142857143-15.0071428571429
107199.91203.507142857143-3.59714285714287
108210.73203.5071428571437.22285714285712
109192.06203.507142857143-11.4471428571429
110204.62203.5071428571431.11285714285714
111235203.50714285714331.4928571428571
112261.09203.50714285714357.5828571428571
113256.88203.50714285714353.3728571428571
114251.53203.50714285714348.0228571428571
115257.25203.50714285714353.7428571428571
116243.1203.50714285714339.5928571428571
117283.75203.50714285714380.2428571428571

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 10.81 & 60.485 & -49.675 \tabularnewline
2 & 9.12 & 11.3089285714286 & -2.18892857142857 \tabularnewline
3 & 11.03 & 11.3089285714286 & -0.278928571428573 \tabularnewline
4 & 12.74 & 11.3089285714286 & 1.43107142857143 \tabularnewline
5 & 9.98 & 11.3089285714286 & -1.32892857142857 \tabularnewline
6 & 11.62 & 11.3089285714286 & 0.311071428571427 \tabularnewline
7 & 9.4 & 11.3089285714286 & -1.90892857142857 \tabularnewline
8 & 9.27 & 11.3089285714286 & -2.03892857142857 \tabularnewline
9 & 7.76 & 7.71666666666667 & 0.043333333333333 \tabularnewline
10 & 8.78 & 11.3089285714286 & -2.52892857142857 \tabularnewline
11 & 10.65 & 11.3089285714286 & -0.658928571428572 \tabularnewline
12 & 10.95 & 11.3089285714286 & -0.358928571428573 \tabularnewline
13 & 12.36 & 11.3089285714286 & 1.05107142857143 \tabularnewline
14 & 10.85 & 11.3089285714286 & -0.458928571428572 \tabularnewline
15 & 11.84 & 11.3089285714286 & 0.531071428571428 \tabularnewline
16 & 12.14 & 11.3089285714286 & 0.831071428571429 \tabularnewline
17 & 11.65 & 11.3089285714286 & 0.341071428571428 \tabularnewline
18 & 8.86 & 7.71666666666667 & 1.14333333333333 \tabularnewline
19 & 7.63 & 7.71666666666667 & -0.0866666666666669 \tabularnewline
20 & 7.38 & 7.71666666666667 & -0.336666666666667 \tabularnewline
21 & 7.25 & 7.71666666666667 & -0.466666666666667 \tabularnewline
22 & 8.03 & 7.71666666666667 & 0.313333333333333 \tabularnewline
23 & 7.75 & 7.71666666666667 & 0.0333333333333332 \tabularnewline
24 & 7.16 & 7.71666666666667 & -0.556666666666667 \tabularnewline
25 & 7.18 & 7.71666666666667 & -0.536666666666667 \tabularnewline
26 & 7.51 & 7.71666666666667 & -0.206666666666667 \tabularnewline
27 & 7.07 & 98.131 & -91.061 \tabularnewline
28 & 7.11 & 7.71666666666667 & -0.606666666666666 \tabularnewline
29 & 8.98 & 7.71666666666667 & 1.26333333333333 \tabularnewline
30 & 9.53 & 11.3089285714286 & -1.77892857142857 \tabularnewline
31 & 10.54 & 11.3089285714286 & -0.768928571428573 \tabularnewline
32 & 11.31 & 11.3089285714286 & 0.00107142857142861 \tabularnewline
33 & 10.36 & 11.3089285714286 & -0.948928571428572 \tabularnewline
34 & 11.44 & 11.3089285714286 & 0.131071428571428 \tabularnewline
35 & 10.45 & 11.3089285714286 & -0.858928571428573 \tabularnewline
36 & 10.69 & 11.3089285714286 & -0.618928571428572 \tabularnewline
37 & 11.28 & 11.3089285714286 & -0.0289285714285725 \tabularnewline
38 & 11.96 & 11.3089285714286 & 0.651071428571429 \tabularnewline
39 & 13.52 & 11.3089285714286 & 2.21107142857143 \tabularnewline
40 & 12.89 & 11.3089285714286 & 1.58107142857143 \tabularnewline
41 & 14.03 & 11.3089285714286 & 2.72107142857143 \tabularnewline
42 & 16.27 & 11.3089285714286 & 4.96107142857143 \tabularnewline
43 & 16.17 & 34.065 & -17.895 \tabularnewline
44 & 17.25 & 34.065 & -16.815 \tabularnewline
45 & 19.38 & 34.065 & -14.685 \tabularnewline
46 & 26.2 & 60.485 & -34.285 \tabularnewline
47 & 33.53 & 60.485 & -26.955 \tabularnewline
48 & 32.2 & 60.485 & -28.285 \tabularnewline
49 & 38.45 & 60.485 & -22.035 \tabularnewline
50 & 44.86 & 60.485 & -15.625 \tabularnewline
51 & 41.67 & 60.485 & -18.815 \tabularnewline
52 & 36.06 & 60.485 & -24.425 \tabularnewline
53 & 39.76 & 34.065 & 5.695 \tabularnewline
54 & 36.81 & 34.065 & 2.745 \tabularnewline
55 & 42.65 & 34.065 & 8.585 \tabularnewline
56 & 46.89 & 34.065 & 12.825 \tabularnewline
57 & 53.61 & 34.065 & 19.545 \tabularnewline
58 & 57.59 & 60.485 & -2.895 \tabularnewline
59 & 67.82 & 60.485 & 7.33499999999999 \tabularnewline
60 & 71.89 & 60.485 & 11.405 \tabularnewline
61 & 75.51 & 60.485 & 15.025 \tabularnewline
62 & 68.49 & 60.485 & 8.005 \tabularnewline
63 & 62.72 & 60.485 & 2.235 \tabularnewline
64 & 70.39 & 60.485 & 9.905 \tabularnewline
65 & 59.77 & 60.485 & -0.714999999999996 \tabularnewline
66 & 57.27 & 60.485 & -3.215 \tabularnewline
67 & 67.96 & 60.485 & 7.47499999999999 \tabularnewline
68 & 67.85 & 60.485 & 7.36499999999999 \tabularnewline
69 & 76.98 & 60.485 & 16.495 \tabularnewline
70 & 81.08 & 60.485 & 20.595 \tabularnewline
71 & 91.66 & 134.148571428571 & -42.4885714285714 \tabularnewline
72 & 84.84 & 134.148571428571 & -49.3085714285714 \tabularnewline
73 & 85.73 & 134.148571428571 & -48.4185714285714 \tabularnewline
74 & 84.61 & 60.485 & 24.125 \tabularnewline
75 & 92.91 & 60.485 & 32.425 \tabularnewline
76 & 99.8 & 134.148571428571 & -34.3485714285714 \tabularnewline
77 & 121.19 & 134.148571428571 & -12.9585714285714 \tabularnewline
78 & 122.04 & 134.148571428571 & -12.1085714285714 \tabularnewline
79 & 131.76 & 134.148571428571 & -2.38857142857142 \tabularnewline
80 & 138.48 & 134.148571428571 & 4.33142857142857 \tabularnewline
81 & 153.47 & 134.148571428571 & 19.3214285714286 \tabularnewline
82 & 189.95 & 134.148571428571 & 55.8014285714286 \tabularnewline
83 & 182.22 & 134.148571428571 & 48.0714285714286 \tabularnewline
84 & 198.08 & 134.148571428571 & 63.9314285714286 \tabularnewline
85 & 135.36 & 134.148571428571 & 1.2114285714286 \tabularnewline
86 & 125.02 & 60.485 & 64.535 \tabularnewline
87 & 143.5 & 134.148571428571 & 9.35142857142858 \tabularnewline
88 & 173.95 & 203.507142857143 & -29.5571428571429 \tabularnewline
89 & 188.75 & 203.507142857143 & -14.7571428571429 \tabularnewline
90 & 167.44 & 203.507142857143 & -36.0671428571429 \tabularnewline
91 & 158.95 & 203.507142857143 & -44.5571428571429 \tabularnewline
92 & 169.53 & 203.507142857143 & -33.9771428571429 \tabularnewline
93 & 113.66 & 203.507142857143 & -89.8471428571429 \tabularnewline
94 & 107.59 & 98.131 & 9.459 \tabularnewline
95 & 92.67 & 98.131 & -5.461 \tabularnewline
96 & 85.35 & 98.131 & -12.781 \tabularnewline
97 & 90.13 & 98.131 & -8.001 \tabularnewline
98 & 89.31 & 98.131 & -8.821 \tabularnewline
99 & 105.12 & 98.131 & 6.989 \tabularnewline
100 & 125.83 & 98.131 & 27.699 \tabularnewline
101 & 135.81 & 98.131 & 37.679 \tabularnewline
102 & 142.43 & 98.131 & 44.299 \tabularnewline
103 & 163.39 & 203.507142857143 & -40.1171428571429 \tabularnewline
104 & 168.21 & 203.507142857143 & -35.2971428571429 \tabularnewline
105 & 185.35 & 203.507142857143 & -18.1571428571429 \tabularnewline
106 & 188.5 & 203.507142857143 & -15.0071428571429 \tabularnewline
107 & 199.91 & 203.507142857143 & -3.59714285714287 \tabularnewline
108 & 210.73 & 203.507142857143 & 7.22285714285712 \tabularnewline
109 & 192.06 & 203.507142857143 & -11.4471428571429 \tabularnewline
110 & 204.62 & 203.507142857143 & 1.11285714285714 \tabularnewline
111 & 235 & 203.507142857143 & 31.4928571428571 \tabularnewline
112 & 261.09 & 203.507142857143 & 57.5828571428571 \tabularnewline
113 & 256.88 & 203.507142857143 & 53.3728571428571 \tabularnewline
114 & 251.53 & 203.507142857143 & 48.0228571428571 \tabularnewline
115 & 257.25 & 203.507142857143 & 53.7428571428571 \tabularnewline
116 & 243.1 & 203.507142857143 & 39.5928571428571 \tabularnewline
117 & 283.75 & 203.507142857143 & 80.2428571428571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198753&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]10.81[/C][C]60.485[/C][C]-49.675[/C][/ROW]
[ROW][C]2[/C][C]9.12[/C][C]11.3089285714286[/C][C]-2.18892857142857[/C][/ROW]
[ROW][C]3[/C][C]11.03[/C][C]11.3089285714286[/C][C]-0.278928571428573[/C][/ROW]
[ROW][C]4[/C][C]12.74[/C][C]11.3089285714286[/C][C]1.43107142857143[/C][/ROW]
[ROW][C]5[/C][C]9.98[/C][C]11.3089285714286[/C][C]-1.32892857142857[/C][/ROW]
[ROW][C]6[/C][C]11.62[/C][C]11.3089285714286[/C][C]0.311071428571427[/C][/ROW]
[ROW][C]7[/C][C]9.4[/C][C]11.3089285714286[/C][C]-1.90892857142857[/C][/ROW]
[ROW][C]8[/C][C]9.27[/C][C]11.3089285714286[/C][C]-2.03892857142857[/C][/ROW]
[ROW][C]9[/C][C]7.76[/C][C]7.71666666666667[/C][C]0.043333333333333[/C][/ROW]
[ROW][C]10[/C][C]8.78[/C][C]11.3089285714286[/C][C]-2.52892857142857[/C][/ROW]
[ROW][C]11[/C][C]10.65[/C][C]11.3089285714286[/C][C]-0.658928571428572[/C][/ROW]
[ROW][C]12[/C][C]10.95[/C][C]11.3089285714286[/C][C]-0.358928571428573[/C][/ROW]
[ROW][C]13[/C][C]12.36[/C][C]11.3089285714286[/C][C]1.05107142857143[/C][/ROW]
[ROW][C]14[/C][C]10.85[/C][C]11.3089285714286[/C][C]-0.458928571428572[/C][/ROW]
[ROW][C]15[/C][C]11.84[/C][C]11.3089285714286[/C][C]0.531071428571428[/C][/ROW]
[ROW][C]16[/C][C]12.14[/C][C]11.3089285714286[/C][C]0.831071428571429[/C][/ROW]
[ROW][C]17[/C][C]11.65[/C][C]11.3089285714286[/C][C]0.341071428571428[/C][/ROW]
[ROW][C]18[/C][C]8.86[/C][C]7.71666666666667[/C][C]1.14333333333333[/C][/ROW]
[ROW][C]19[/C][C]7.63[/C][C]7.71666666666667[/C][C]-0.0866666666666669[/C][/ROW]
[ROW][C]20[/C][C]7.38[/C][C]7.71666666666667[/C][C]-0.336666666666667[/C][/ROW]
[ROW][C]21[/C][C]7.25[/C][C]7.71666666666667[/C][C]-0.466666666666667[/C][/ROW]
[ROW][C]22[/C][C]8.03[/C][C]7.71666666666667[/C][C]0.313333333333333[/C][/ROW]
[ROW][C]23[/C][C]7.75[/C][C]7.71666666666667[/C][C]0.0333333333333332[/C][/ROW]
[ROW][C]24[/C][C]7.16[/C][C]7.71666666666667[/C][C]-0.556666666666667[/C][/ROW]
[ROW][C]25[/C][C]7.18[/C][C]7.71666666666667[/C][C]-0.536666666666667[/C][/ROW]
[ROW][C]26[/C][C]7.51[/C][C]7.71666666666667[/C][C]-0.206666666666667[/C][/ROW]
[ROW][C]27[/C][C]7.07[/C][C]98.131[/C][C]-91.061[/C][/ROW]
[ROW][C]28[/C][C]7.11[/C][C]7.71666666666667[/C][C]-0.606666666666666[/C][/ROW]
[ROW][C]29[/C][C]8.98[/C][C]7.71666666666667[/C][C]1.26333333333333[/C][/ROW]
[ROW][C]30[/C][C]9.53[/C][C]11.3089285714286[/C][C]-1.77892857142857[/C][/ROW]
[ROW][C]31[/C][C]10.54[/C][C]11.3089285714286[/C][C]-0.768928571428573[/C][/ROW]
[ROW][C]32[/C][C]11.31[/C][C]11.3089285714286[/C][C]0.00107142857142861[/C][/ROW]
[ROW][C]33[/C][C]10.36[/C][C]11.3089285714286[/C][C]-0.948928571428572[/C][/ROW]
[ROW][C]34[/C][C]11.44[/C][C]11.3089285714286[/C][C]0.131071428571428[/C][/ROW]
[ROW][C]35[/C][C]10.45[/C][C]11.3089285714286[/C][C]-0.858928571428573[/C][/ROW]
[ROW][C]36[/C][C]10.69[/C][C]11.3089285714286[/C][C]-0.618928571428572[/C][/ROW]
[ROW][C]37[/C][C]11.28[/C][C]11.3089285714286[/C][C]-0.0289285714285725[/C][/ROW]
[ROW][C]38[/C][C]11.96[/C][C]11.3089285714286[/C][C]0.651071428571429[/C][/ROW]
[ROW][C]39[/C][C]13.52[/C][C]11.3089285714286[/C][C]2.21107142857143[/C][/ROW]
[ROW][C]40[/C][C]12.89[/C][C]11.3089285714286[/C][C]1.58107142857143[/C][/ROW]
[ROW][C]41[/C][C]14.03[/C][C]11.3089285714286[/C][C]2.72107142857143[/C][/ROW]
[ROW][C]42[/C][C]16.27[/C][C]11.3089285714286[/C][C]4.96107142857143[/C][/ROW]
[ROW][C]43[/C][C]16.17[/C][C]34.065[/C][C]-17.895[/C][/ROW]
[ROW][C]44[/C][C]17.25[/C][C]34.065[/C][C]-16.815[/C][/ROW]
[ROW][C]45[/C][C]19.38[/C][C]34.065[/C][C]-14.685[/C][/ROW]
[ROW][C]46[/C][C]26.2[/C][C]60.485[/C][C]-34.285[/C][/ROW]
[ROW][C]47[/C][C]33.53[/C][C]60.485[/C][C]-26.955[/C][/ROW]
[ROW][C]48[/C][C]32.2[/C][C]60.485[/C][C]-28.285[/C][/ROW]
[ROW][C]49[/C][C]38.45[/C][C]60.485[/C][C]-22.035[/C][/ROW]
[ROW][C]50[/C][C]44.86[/C][C]60.485[/C][C]-15.625[/C][/ROW]
[ROW][C]51[/C][C]41.67[/C][C]60.485[/C][C]-18.815[/C][/ROW]
[ROW][C]52[/C][C]36.06[/C][C]60.485[/C][C]-24.425[/C][/ROW]
[ROW][C]53[/C][C]39.76[/C][C]34.065[/C][C]5.695[/C][/ROW]
[ROW][C]54[/C][C]36.81[/C][C]34.065[/C][C]2.745[/C][/ROW]
[ROW][C]55[/C][C]42.65[/C][C]34.065[/C][C]8.585[/C][/ROW]
[ROW][C]56[/C][C]46.89[/C][C]34.065[/C][C]12.825[/C][/ROW]
[ROW][C]57[/C][C]53.61[/C][C]34.065[/C][C]19.545[/C][/ROW]
[ROW][C]58[/C][C]57.59[/C][C]60.485[/C][C]-2.895[/C][/ROW]
[ROW][C]59[/C][C]67.82[/C][C]60.485[/C][C]7.33499999999999[/C][/ROW]
[ROW][C]60[/C][C]71.89[/C][C]60.485[/C][C]11.405[/C][/ROW]
[ROW][C]61[/C][C]75.51[/C][C]60.485[/C][C]15.025[/C][/ROW]
[ROW][C]62[/C][C]68.49[/C][C]60.485[/C][C]8.005[/C][/ROW]
[ROW][C]63[/C][C]62.72[/C][C]60.485[/C][C]2.235[/C][/ROW]
[ROW][C]64[/C][C]70.39[/C][C]60.485[/C][C]9.905[/C][/ROW]
[ROW][C]65[/C][C]59.77[/C][C]60.485[/C][C]-0.714999999999996[/C][/ROW]
[ROW][C]66[/C][C]57.27[/C][C]60.485[/C][C]-3.215[/C][/ROW]
[ROW][C]67[/C][C]67.96[/C][C]60.485[/C][C]7.47499999999999[/C][/ROW]
[ROW][C]68[/C][C]67.85[/C][C]60.485[/C][C]7.36499999999999[/C][/ROW]
[ROW][C]69[/C][C]76.98[/C][C]60.485[/C][C]16.495[/C][/ROW]
[ROW][C]70[/C][C]81.08[/C][C]60.485[/C][C]20.595[/C][/ROW]
[ROW][C]71[/C][C]91.66[/C][C]134.148571428571[/C][C]-42.4885714285714[/C][/ROW]
[ROW][C]72[/C][C]84.84[/C][C]134.148571428571[/C][C]-49.3085714285714[/C][/ROW]
[ROW][C]73[/C][C]85.73[/C][C]134.148571428571[/C][C]-48.4185714285714[/C][/ROW]
[ROW][C]74[/C][C]84.61[/C][C]60.485[/C][C]24.125[/C][/ROW]
[ROW][C]75[/C][C]92.91[/C][C]60.485[/C][C]32.425[/C][/ROW]
[ROW][C]76[/C][C]99.8[/C][C]134.148571428571[/C][C]-34.3485714285714[/C][/ROW]
[ROW][C]77[/C][C]121.19[/C][C]134.148571428571[/C][C]-12.9585714285714[/C][/ROW]
[ROW][C]78[/C][C]122.04[/C][C]134.148571428571[/C][C]-12.1085714285714[/C][/ROW]
[ROW][C]79[/C][C]131.76[/C][C]134.148571428571[/C][C]-2.38857142857142[/C][/ROW]
[ROW][C]80[/C][C]138.48[/C][C]134.148571428571[/C][C]4.33142857142857[/C][/ROW]
[ROW][C]81[/C][C]153.47[/C][C]134.148571428571[/C][C]19.3214285714286[/C][/ROW]
[ROW][C]82[/C][C]189.95[/C][C]134.148571428571[/C][C]55.8014285714286[/C][/ROW]
[ROW][C]83[/C][C]182.22[/C][C]134.148571428571[/C][C]48.0714285714286[/C][/ROW]
[ROW][C]84[/C][C]198.08[/C][C]134.148571428571[/C][C]63.9314285714286[/C][/ROW]
[ROW][C]85[/C][C]135.36[/C][C]134.148571428571[/C][C]1.2114285714286[/C][/ROW]
[ROW][C]86[/C][C]125.02[/C][C]60.485[/C][C]64.535[/C][/ROW]
[ROW][C]87[/C][C]143.5[/C][C]134.148571428571[/C][C]9.35142857142858[/C][/ROW]
[ROW][C]88[/C][C]173.95[/C][C]203.507142857143[/C][C]-29.5571428571429[/C][/ROW]
[ROW][C]89[/C][C]188.75[/C][C]203.507142857143[/C][C]-14.7571428571429[/C][/ROW]
[ROW][C]90[/C][C]167.44[/C][C]203.507142857143[/C][C]-36.0671428571429[/C][/ROW]
[ROW][C]91[/C][C]158.95[/C][C]203.507142857143[/C][C]-44.5571428571429[/C][/ROW]
[ROW][C]92[/C][C]169.53[/C][C]203.507142857143[/C][C]-33.9771428571429[/C][/ROW]
[ROW][C]93[/C][C]113.66[/C][C]203.507142857143[/C][C]-89.8471428571429[/C][/ROW]
[ROW][C]94[/C][C]107.59[/C][C]98.131[/C][C]9.459[/C][/ROW]
[ROW][C]95[/C][C]92.67[/C][C]98.131[/C][C]-5.461[/C][/ROW]
[ROW][C]96[/C][C]85.35[/C][C]98.131[/C][C]-12.781[/C][/ROW]
[ROW][C]97[/C][C]90.13[/C][C]98.131[/C][C]-8.001[/C][/ROW]
[ROW][C]98[/C][C]89.31[/C][C]98.131[/C][C]-8.821[/C][/ROW]
[ROW][C]99[/C][C]105.12[/C][C]98.131[/C][C]6.989[/C][/ROW]
[ROW][C]100[/C][C]125.83[/C][C]98.131[/C][C]27.699[/C][/ROW]
[ROW][C]101[/C][C]135.81[/C][C]98.131[/C][C]37.679[/C][/ROW]
[ROW][C]102[/C][C]142.43[/C][C]98.131[/C][C]44.299[/C][/ROW]
[ROW][C]103[/C][C]163.39[/C][C]203.507142857143[/C][C]-40.1171428571429[/C][/ROW]
[ROW][C]104[/C][C]168.21[/C][C]203.507142857143[/C][C]-35.2971428571429[/C][/ROW]
[ROW][C]105[/C][C]185.35[/C][C]203.507142857143[/C][C]-18.1571428571429[/C][/ROW]
[ROW][C]106[/C][C]188.5[/C][C]203.507142857143[/C][C]-15.0071428571429[/C][/ROW]
[ROW][C]107[/C][C]199.91[/C][C]203.507142857143[/C][C]-3.59714285714287[/C][/ROW]
[ROW][C]108[/C][C]210.73[/C][C]203.507142857143[/C][C]7.22285714285712[/C][/ROW]
[ROW][C]109[/C][C]192.06[/C][C]203.507142857143[/C][C]-11.4471428571429[/C][/ROW]
[ROW][C]110[/C][C]204.62[/C][C]203.507142857143[/C][C]1.11285714285714[/C][/ROW]
[ROW][C]111[/C][C]235[/C][C]203.507142857143[/C][C]31.4928571428571[/C][/ROW]
[ROW][C]112[/C][C]261.09[/C][C]203.507142857143[/C][C]57.5828571428571[/C][/ROW]
[ROW][C]113[/C][C]256.88[/C][C]203.507142857143[/C][C]53.3728571428571[/C][/ROW]
[ROW][C]114[/C][C]251.53[/C][C]203.507142857143[/C][C]48.0228571428571[/C][/ROW]
[ROW][C]115[/C][C]257.25[/C][C]203.507142857143[/C][C]53.7428571428571[/C][/ROW]
[ROW][C]116[/C][C]243.1[/C][C]203.507142857143[/C][C]39.5928571428571[/C][/ROW]
[ROW][C]117[/C][C]283.75[/C][C]203.507142857143[/C][C]80.2428571428571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198753&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198753&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
#ActualsForecastsResiduals
110.8160.485-49.675
29.1211.3089285714286-2.18892857142857
311.0311.3089285714286-0.278928571428573
412.7411.30892857142861.43107142857143
59.9811.3089285714286-1.32892857142857
611.6211.30892857142860.311071428571427
79.411.3089285714286-1.90892857142857
89.2711.3089285714286-2.03892857142857
97.767.716666666666670.043333333333333
108.7811.3089285714286-2.52892857142857
1110.6511.3089285714286-0.658928571428572
1210.9511.3089285714286-0.358928571428573
1312.3611.30892857142861.05107142857143
1410.8511.3089285714286-0.458928571428572
1511.8411.30892857142860.531071428571428
1612.1411.30892857142860.831071428571429
1711.6511.30892857142860.341071428571428
188.867.716666666666671.14333333333333
197.637.71666666666667-0.0866666666666669
207.387.71666666666667-0.336666666666667
217.257.71666666666667-0.466666666666667
228.037.716666666666670.313333333333333
237.757.716666666666670.0333333333333332
247.167.71666666666667-0.556666666666667
257.187.71666666666667-0.536666666666667
267.517.71666666666667-0.206666666666667
277.0798.131-91.061
287.117.71666666666667-0.606666666666666
298.987.716666666666671.26333333333333
309.5311.3089285714286-1.77892857142857
3110.5411.3089285714286-0.768928571428573
3211.3111.30892857142860.00107142857142861
3310.3611.3089285714286-0.948928571428572
3411.4411.30892857142860.131071428571428
3510.4511.3089285714286-0.858928571428573
3610.6911.3089285714286-0.618928571428572
3711.2811.3089285714286-0.0289285714285725
3811.9611.30892857142860.651071428571429
3913.5211.30892857142862.21107142857143
4012.8911.30892857142861.58107142857143
4114.0311.30892857142862.72107142857143
4216.2711.30892857142864.96107142857143
4316.1734.065-17.895
4417.2534.065-16.815
4519.3834.065-14.685
4626.260.485-34.285
4733.5360.485-26.955
4832.260.485-28.285
4938.4560.485-22.035
5044.8660.485-15.625
5141.6760.485-18.815
5236.0660.485-24.425
5339.7634.0655.695
5436.8134.0652.745
5542.6534.0658.585
5646.8934.06512.825
5753.6134.06519.545
5857.5960.485-2.895
5967.8260.4857.33499999999999
6071.8960.48511.405
6175.5160.48515.025
6268.4960.4858.005
6362.7260.4852.235
6470.3960.4859.905
6559.7760.485-0.714999999999996
6657.2760.485-3.215
6767.9660.4857.47499999999999
6867.8560.4857.36499999999999
6976.9860.48516.495
7081.0860.48520.595
7191.66134.148571428571-42.4885714285714
7284.84134.148571428571-49.3085714285714
7385.73134.148571428571-48.4185714285714
7484.6160.48524.125
7592.9160.48532.425
7699.8134.148571428571-34.3485714285714
77121.19134.148571428571-12.9585714285714
78122.04134.148571428571-12.1085714285714
79131.76134.148571428571-2.38857142857142
80138.48134.1485714285714.33142857142857
81153.47134.14857142857119.3214285714286
82189.95134.14857142857155.8014285714286
83182.22134.14857142857148.0714285714286
84198.08134.14857142857163.9314285714286
85135.36134.1485714285711.2114285714286
86125.0260.48564.535
87143.5134.1485714285719.35142857142858
88173.95203.507142857143-29.5571428571429
89188.75203.507142857143-14.7571428571429
90167.44203.507142857143-36.0671428571429
91158.95203.507142857143-44.5571428571429
92169.53203.507142857143-33.9771428571429
93113.66203.507142857143-89.8471428571429
94107.5998.1319.459
9592.6798.131-5.461
9685.3598.131-12.781
9790.1398.131-8.001
9889.3198.131-8.821
99105.1298.1316.989
100125.8398.13127.699
101135.8198.13137.679
102142.4398.13144.299
103163.39203.507142857143-40.1171428571429
104168.21203.507142857143-35.2971428571429
105185.35203.507142857143-18.1571428571429
106188.5203.507142857143-15.0071428571429
107199.91203.507142857143-3.59714285714287
108210.73203.5071428571437.22285714285712
109192.06203.507142857143-11.4471428571429
110204.62203.5071428571431.11285714285714
111235203.50714285714331.4928571428571
112261.09203.50714285714357.5828571428571
113256.88203.50714285714353.3728571428571
114251.53203.50714285714348.0228571428571
115257.25203.50714285714353.7428571428571
116243.1203.50714285714339.5928571428571
117283.75203.50714285714380.2428571428571



Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = none ; par3 = ; 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')
}