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Author

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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Fri, 09 Dec 2011 13:45:00 -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/09/t1323456363yiqoz84e6jkov01.htm/, Retrieved Thu, 02 May 2024 23:36:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153430, Retrieved Thu, 02 May 2024 23:36:01 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

149

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)] [Workshop 10 Recur...] [2011-12-09 18:45:00] [5c44e6aad476a1bab98fc6774eca4c08] [Current]
-           [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 18:50:36] [de8512d9b386046939a89973b76869e3] 
-  MP         [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:39:29] [74be16979710d4c4e7c6647856088456] 
- R  D      [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 19:18:30] [de8512d9b386046939a89973b76869e3] 
- R           [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 19:22:17] [de8512d9b386046939a89973b76869e3] 
- R  D          [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 19:32:47] [de8512d9b386046939a89973b76869e3] 
- R               [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 19:38:35] [de8512d9b386046939a89973b76869e3] 
-  MP               [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:43:06] [74be16979710d4c4e7c6647856088456] 
- RMP               [Multiple Regression] [Paper SHW  MLR 2] [2011-12-16 14:52:16] [de8512d9b386046939a89973b76869e3] 
- R P               [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:58:20] [de8512d9b386046939a89973b76869e3] 
- RMP             [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:42:20] [74be16979710d4c4e7c6647856088456] 
-  MP           [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:41:22] [74be16979710d4c4e7c6647856088456] 
- RMP         [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:40:37] [74be16979710d4c4e7c6647856088456] 
-  MP       [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:38:32] [74be16979710d4c4e7c6647856088456] 

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Dataseries X:

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Histogram

Boxplots

391	12	1	0	1168	5841	12
893	22	23	67	22618	23824	10
872	37	32	48	34777	14336	14
1138	25	24	66	94785	61023	9
874	28	23	69	192565	153197	8
1281	83	30	93	140867	68370	16
865	33	14	37	31081	58391	11
1179	39	28	80	49810	46341	19
1654	24	24	69	15986	25157	10
1222	47	25	81	30727	53907	12
1204	32	38	120	92696	20112	11
1054	67	29	107	95364	76669	8
1587	47	30	83	51513	53782	14
1386	71	36	98	40735	55515	13
1373	44	30	90	57793	59238	14
1468	33	25	73	51715	71299	14
1496	67	27	104	106671	71180	17
1425	105	34	120	69094	73815	16
2547	135	37	129	126846	72413	12
1583	43	26	93	116174	95757	14
1324	56	35	95	60578	69107	15
1420	62	33	98	61370	67808	15
1605	106	32	83	65567	84396	14
1383	59	28	90	79892	108016	14
1381	68	28	107	120293	93913	16
1559	81	31	63	87771	115338	16
1439	69	25	60	57635	85584	13
1403	69	28	72	83737	82981	14
1579	44	42	122	74007	82036	15
1111	46	43	139	86687	112494	12
2035	41	21	78	37238	10901	13
2147	73	31	114	82753	98579	15
2515	123	34	120	69112	85646	17
1530	60	38	93	83123	86146	8
1645	47	30	73	64466	89455	16
1626	124	33	118	102860	96971	10
1831	91	39	138	82875	93176	17
1833	114	28	91	92945	85298	14
1644	100	35	71	84651	106175	16
1641	111	31	98	102372	112283	7
1226	41	29	99	95260	129847	15
1424	92	35	116	74163	127748	9
1677	94	35	133	117478	146761	9
1418	79	30	94	112285	146283	13
1929	101	36	117	99052	121527	16
2352	76	29	96	80670	102996	15
2445	98	32	119	55801	77494	12
1638	105	33	120	72654	131741	16
1900	131	39	132	130115	139296	15
1982	93	41	139	109825	102255	8
2352	81	31	94	85323	130767	16
2186	63	31	119	91721	78876	13
1706	102	30	115	133824	136368	15
1659	131	31	90	161647	181248	15
1904	118	33	106	129838	168237	17
2152	77	31	71	101481	112642	13
1764	78	38	123	66198	143983	16
1964	58	29	104	111813	120336	16
1840	88	28	105	95536	132190	18
1944	133	42	110	101338	103950	19
2144	101	44	164	143558	160604	14
2699	98	33	115	76643	142775	15
2312	120	39	117	103772	120691	15
1973	123	35	124	105195	174141	17
2888	110	52	197	115929	146123	11
2527	96	32	120	83122	136815	16
2429	109	37	86	54990	147866	16
2158	100	43	152	93815	132432	16
3004	57	29	107	89691	105805	14
2452	107	33	124	101494	171975	17
2395	116	31	82	91413	209056	16
3261	113	37	133	135777	122037	17
4041	158	47	168	97668	151511	13
2662	84	36	126	79215	159676	12
2833	129	41	144	105547	170875	14
2253	79	39	140	115762	155135	10
2242	80	30	108	67654	127766	20
2970	118	38	111	106117	131722	16
2922	136	45	160	213688	214921	14
4308	76	34	110	100708	79336	16
3201	121	25	91	119182	195663	14

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153430&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153430&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153430&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	3 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Goodness of Fit
Correlation	NA
R-squared	NA
RMSE	2.7973

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & NA \tabularnewline
R-squared & NA \tabularnewline
RMSE & 2.7973 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153430&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]NA[/C][/ROW]
[ROW][C]R-squared[/C][C]NA[/C][/ROW]
[ROW][C]RMSE[/C][C]2.7973[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153430&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153430&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	NA
R-squared	NA
RMSE	2.7973

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	12	13.9506172839506	-1.95061728395062
2	10	13.9506172839506	-3.95061728395062
3	14	13.9506172839506	0.0493827160493829
4	9	13.9506172839506	-4.95061728395062
5	8	13.9506172839506	-5.95061728395062
6	16	13.9506172839506	2.04938271604938
7	11	13.9506172839506	-2.95061728395062
8	19	13.9506172839506	5.04938271604938
9	10	13.9506172839506	-3.95061728395062
10	12	13.9506172839506	-1.95061728395062
11	11	13.9506172839506	-2.95061728395062
12	8	13.9506172839506	-5.95061728395062
13	14	13.9506172839506	0.0493827160493829
14	13	13.9506172839506	-0.950617283950617
15	14	13.9506172839506	0.0493827160493829
16	14	13.9506172839506	0.0493827160493829
17	17	13.9506172839506	3.04938271604938
18	16	13.9506172839506	2.04938271604938
19	12	13.9506172839506	-1.95061728395062
20	14	13.9506172839506	0.0493827160493829
21	15	13.9506172839506	1.04938271604938
22	15	13.9506172839506	1.04938271604938
23	14	13.9506172839506	0.0493827160493829
24	14	13.9506172839506	0.0493827160493829
25	16	13.9506172839506	2.04938271604938
26	16	13.9506172839506	2.04938271604938
27	13	13.9506172839506	-0.950617283950617
28	14	13.9506172839506	0.0493827160493829
29	15	13.9506172839506	1.04938271604938
30	12	13.9506172839506	-1.95061728395062
31	13	13.9506172839506	-0.950617283950617
32	15	13.9506172839506	1.04938271604938
33	17	13.9506172839506	3.04938271604938
34	8	13.9506172839506	-5.95061728395062
35	16	13.9506172839506	2.04938271604938
36	10	13.9506172839506	-3.95061728395062
37	17	13.9506172839506	3.04938271604938
38	14	13.9506172839506	0.0493827160493829
39	16	13.9506172839506	2.04938271604938
40	7	13.9506172839506	-6.95061728395062
41	15	13.9506172839506	1.04938271604938
42	9	13.9506172839506	-4.95061728395062
43	9	13.9506172839506	-4.95061728395062
44	13	13.9506172839506	-0.950617283950617
45	16	13.9506172839506	2.04938271604938
46	15	13.9506172839506	1.04938271604938
47	12	13.9506172839506	-1.95061728395062
48	16	13.9506172839506	2.04938271604938
49	15	13.9506172839506	1.04938271604938
50	8	13.9506172839506	-5.95061728395062
51	16	13.9506172839506	2.04938271604938
52	13	13.9506172839506	-0.950617283950617
53	15	13.9506172839506	1.04938271604938
54	15	13.9506172839506	1.04938271604938
55	17	13.9506172839506	3.04938271604938
56	13	13.9506172839506	-0.950617283950617
57	16	13.9506172839506	2.04938271604938
58	16	13.9506172839506	2.04938271604938
59	18	13.9506172839506	4.04938271604938
60	19	13.9506172839506	5.04938271604938
61	14	13.9506172839506	0.0493827160493829
62	15	13.9506172839506	1.04938271604938
63	15	13.9506172839506	1.04938271604938
64	17	13.9506172839506	3.04938271604938
65	11	13.9506172839506	-2.95061728395062
66	16	13.9506172839506	2.04938271604938
67	16	13.9506172839506	2.04938271604938
68	16	13.9506172839506	2.04938271604938
69	14	13.9506172839506	0.0493827160493829
70	17	13.9506172839506	3.04938271604938
71	16	13.9506172839506	2.04938271604938
72	17	13.9506172839506	3.04938271604938
73	13	13.9506172839506	-0.950617283950617
74	12	13.9506172839506	-1.95061728395062
75	14	13.9506172839506	0.0493827160493829
76	10	13.9506172839506	-3.95061728395062
77	20	13.9506172839506	6.04938271604938
78	16	13.9506172839506	2.04938271604938
79	14	13.9506172839506	0.0493827160493829
80	16	13.9506172839506	2.04938271604938
81	14	13.9506172839506	0.0493827160493829

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 12 & 13.9506172839506 & -1.95061728395062 \tabularnewline
2 & 10 & 13.9506172839506 & -3.95061728395062 \tabularnewline
3 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
4 & 9 & 13.9506172839506 & -4.95061728395062 \tabularnewline
5 & 8 & 13.9506172839506 & -5.95061728395062 \tabularnewline
6 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
7 & 11 & 13.9506172839506 & -2.95061728395062 \tabularnewline
8 & 19 & 13.9506172839506 & 5.04938271604938 \tabularnewline
9 & 10 & 13.9506172839506 & -3.95061728395062 \tabularnewline
10 & 12 & 13.9506172839506 & -1.95061728395062 \tabularnewline
11 & 11 & 13.9506172839506 & -2.95061728395062 \tabularnewline
12 & 8 & 13.9506172839506 & -5.95061728395062 \tabularnewline
13 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
14 & 13 & 13.9506172839506 & -0.950617283950617 \tabularnewline
15 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
16 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
17 & 17 & 13.9506172839506 & 3.04938271604938 \tabularnewline
18 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
19 & 12 & 13.9506172839506 & -1.95061728395062 \tabularnewline
20 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
21 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
22 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
23 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
24 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
25 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
26 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
27 & 13 & 13.9506172839506 & -0.950617283950617 \tabularnewline
28 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
29 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
30 & 12 & 13.9506172839506 & -1.95061728395062 \tabularnewline
31 & 13 & 13.9506172839506 & -0.950617283950617 \tabularnewline
32 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
33 & 17 & 13.9506172839506 & 3.04938271604938 \tabularnewline
34 & 8 & 13.9506172839506 & -5.95061728395062 \tabularnewline
35 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
36 & 10 & 13.9506172839506 & -3.95061728395062 \tabularnewline
37 & 17 & 13.9506172839506 & 3.04938271604938 \tabularnewline
38 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
39 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
40 & 7 & 13.9506172839506 & -6.95061728395062 \tabularnewline
41 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
42 & 9 & 13.9506172839506 & -4.95061728395062 \tabularnewline
43 & 9 & 13.9506172839506 & -4.95061728395062 \tabularnewline
44 & 13 & 13.9506172839506 & -0.950617283950617 \tabularnewline
45 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
46 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
47 & 12 & 13.9506172839506 & -1.95061728395062 \tabularnewline
48 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
49 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
50 & 8 & 13.9506172839506 & -5.95061728395062 \tabularnewline
51 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
52 & 13 & 13.9506172839506 & -0.950617283950617 \tabularnewline
53 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
54 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
55 & 17 & 13.9506172839506 & 3.04938271604938 \tabularnewline
56 & 13 & 13.9506172839506 & -0.950617283950617 \tabularnewline
57 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
58 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
59 & 18 & 13.9506172839506 & 4.04938271604938 \tabularnewline
60 & 19 & 13.9506172839506 & 5.04938271604938 \tabularnewline
61 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
62 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
63 & 15 & 13.9506172839506 & 1.04938271604938 \tabularnewline
64 & 17 & 13.9506172839506 & 3.04938271604938 \tabularnewline
65 & 11 & 13.9506172839506 & -2.95061728395062 \tabularnewline
66 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
67 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
68 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
69 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
70 & 17 & 13.9506172839506 & 3.04938271604938 \tabularnewline
71 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
72 & 17 & 13.9506172839506 & 3.04938271604938 \tabularnewline
73 & 13 & 13.9506172839506 & -0.950617283950617 \tabularnewline
74 & 12 & 13.9506172839506 & -1.95061728395062 \tabularnewline
75 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
76 & 10 & 13.9506172839506 & -3.95061728395062 \tabularnewline
77 & 20 & 13.9506172839506 & 6.04938271604938 \tabularnewline
78 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
79 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
80 & 16 & 13.9506172839506 & 2.04938271604938 \tabularnewline
81 & 14 & 13.9506172839506 & 0.0493827160493829 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153430&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]12[/C][C]13.9506172839506[/C][C]-1.95061728395062[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]13.9506172839506[/C][C]-3.95061728395062[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]13.9506172839506[/C][C]-4.95061728395062[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]13.9506172839506[/C][C]-5.95061728395062[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]13.9506172839506[/C][C]-2.95061728395062[/C][/ROW]
[ROW][C]8[/C][C]19[/C][C]13.9506172839506[/C][C]5.04938271604938[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]13.9506172839506[/C][C]-3.95061728395062[/C][/ROW]
[ROW][C]10[/C][C]12[/C][C]13.9506172839506[/C][C]-1.95061728395062[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.9506172839506[/C][C]-2.95061728395062[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]13.9506172839506[/C][C]-5.95061728395062[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]13.9506172839506[/C][C]-0.950617283950617[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]13.9506172839506[/C][C]3.04938271604938[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]19[/C][C]12[/C][C]13.9506172839506[/C][C]-1.95061728395062[/C][/ROW]
[ROW][C]20[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]25[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]13.9506172839506[/C][C]-0.950617283950617[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]30[/C][C]12[/C][C]13.9506172839506[/C][C]-1.95061728395062[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]13.9506172839506[/C][C]-0.950617283950617[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]13.9506172839506[/C][C]3.04938271604938[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]13.9506172839506[/C][C]-5.95061728395062[/C][/ROW]
[ROW][C]35[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]13.9506172839506[/C][C]-3.95061728395062[/C][/ROW]
[ROW][C]37[/C][C]17[/C][C]13.9506172839506[/C][C]3.04938271604938[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]13.9506172839506[/C][C]-6.95061728395062[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]13.9506172839506[/C][C]-4.95061728395062[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]13.9506172839506[/C][C]-4.95061728395062[/C][/ROW]
[ROW][C]44[/C][C]13[/C][C]13.9506172839506[/C][C]-0.950617283950617[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]13.9506172839506[/C][C]-1.95061728395062[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]13.9506172839506[/C][C]-5.95061728395062[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]52[/C][C]13[/C][C]13.9506172839506[/C][C]-0.950617283950617[/C][/ROW]
[ROW][C]53[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]55[/C][C]17[/C][C]13.9506172839506[/C][C]3.04938271604938[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]13.9506172839506[/C][C]-0.950617283950617[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]59[/C][C]18[/C][C]13.9506172839506[/C][C]4.04938271604938[/C][/ROW]
[ROW][C]60[/C][C]19[/C][C]13.9506172839506[/C][C]5.04938271604938[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]62[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]63[/C][C]15[/C][C]13.9506172839506[/C][C]1.04938271604938[/C][/ROW]
[ROW][C]64[/C][C]17[/C][C]13.9506172839506[/C][C]3.04938271604938[/C][/ROW]
[ROW][C]65[/C][C]11[/C][C]13.9506172839506[/C][C]-2.95061728395062[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]68[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]69[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]13.9506172839506[/C][C]3.04938271604938[/C][/ROW]
[ROW][C]71[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]13.9506172839506[/C][C]3.04938271604938[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]13.9506172839506[/C][C]-0.950617283950617[/C][/ROW]
[ROW][C]74[/C][C]12[/C][C]13.9506172839506[/C][C]-1.95061728395062[/C][/ROW]
[ROW][C]75[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]13.9506172839506[/C][C]-3.95061728395062[/C][/ROW]
[ROW][C]77[/C][C]20[/C][C]13.9506172839506[/C][C]6.04938271604938[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]13.9506172839506[/C][C]2.04938271604938[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]13.9506172839506[/C][C]0.0493827160493829[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153430&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153430&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	12	13.9506172839506	-1.95061728395062
2	10	13.9506172839506	-3.95061728395062
3	14	13.9506172839506	0.0493827160493829
4	9	13.9506172839506	-4.95061728395062
5	8	13.9506172839506	-5.95061728395062
6	16	13.9506172839506	2.04938271604938
7	11	13.9506172839506	-2.95061728395062
8	19	13.9506172839506	5.04938271604938
9	10	13.9506172839506	-3.95061728395062
10	12	13.9506172839506	-1.95061728395062
11	11	13.9506172839506	-2.95061728395062
12	8	13.9506172839506	-5.95061728395062
13	14	13.9506172839506	0.0493827160493829
14	13	13.9506172839506	-0.950617283950617
15	14	13.9506172839506	0.0493827160493829
16	14	13.9506172839506	0.0493827160493829
17	17	13.9506172839506	3.04938271604938
18	16	13.9506172839506	2.04938271604938
19	12	13.9506172839506	-1.95061728395062
20	14	13.9506172839506	0.0493827160493829
21	15	13.9506172839506	1.04938271604938
22	15	13.9506172839506	1.04938271604938
23	14	13.9506172839506	0.0493827160493829
24	14	13.9506172839506	0.0493827160493829
25	16	13.9506172839506	2.04938271604938
26	16	13.9506172839506	2.04938271604938
27	13	13.9506172839506	-0.950617283950617
28	14	13.9506172839506	0.0493827160493829
29	15	13.9506172839506	1.04938271604938
30	12	13.9506172839506	-1.95061728395062
31	13	13.9506172839506	-0.950617283950617
32	15	13.9506172839506	1.04938271604938
33	17	13.9506172839506	3.04938271604938
34	8	13.9506172839506	-5.95061728395062
35	16	13.9506172839506	2.04938271604938
36	10	13.9506172839506	-3.95061728395062
37	17	13.9506172839506	3.04938271604938
38	14	13.9506172839506	0.0493827160493829
39	16	13.9506172839506	2.04938271604938
40	7	13.9506172839506	-6.95061728395062
41	15	13.9506172839506	1.04938271604938
42	9	13.9506172839506	-4.95061728395062
43	9	13.9506172839506	-4.95061728395062
44	13	13.9506172839506	-0.950617283950617
45	16	13.9506172839506	2.04938271604938
46	15	13.9506172839506	1.04938271604938
47	12	13.9506172839506	-1.95061728395062
48	16	13.9506172839506	2.04938271604938
49	15	13.9506172839506	1.04938271604938
50	8	13.9506172839506	-5.95061728395062
51	16	13.9506172839506	2.04938271604938
52	13	13.9506172839506	-0.950617283950617
53	15	13.9506172839506	1.04938271604938
54	15	13.9506172839506	1.04938271604938
55	17	13.9506172839506	3.04938271604938
56	13	13.9506172839506	-0.950617283950617
57	16	13.9506172839506	2.04938271604938
58	16	13.9506172839506	2.04938271604938
59	18	13.9506172839506	4.04938271604938
60	19	13.9506172839506	5.04938271604938
61	14	13.9506172839506	0.0493827160493829
62	15	13.9506172839506	1.04938271604938
63	15	13.9506172839506	1.04938271604938
64	17	13.9506172839506	3.04938271604938
65	11	13.9506172839506	-2.95061728395062
66	16	13.9506172839506	2.04938271604938
67	16	13.9506172839506	2.04938271604938
68	16	13.9506172839506	2.04938271604938
69	14	13.9506172839506	0.0493827160493829
70	17	13.9506172839506	3.04938271604938
71	16	13.9506172839506	2.04938271604938
72	17	13.9506172839506	3.04938271604938
73	13	13.9506172839506	-0.950617283950617
74	12	13.9506172839506	-1.95061728395062
75	14	13.9506172839506	0.0493827160493829
76	10	13.9506172839506	-3.95061728395062
77	20	13.9506172839506	6.04938271604938
78	16	13.9506172839506	2.04938271604938
79	14	13.9506172839506	0.0493827160493829
80	16	13.9506172839506	2.04938271604938
81	14	13.9506172839506	0.0493827160493829

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 = pearson ; par2 = equal ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 7 ; 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