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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationWed, 22 Dec 2010 14:01:34 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/22/t1293026454rtunif1hl23xr2w.htm/, Retrieved Mon, 06 May 2024 05:16:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114215, Retrieved Mon, 06 May 2024 05:16:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
F   PD  [Recursive Partitioning (Regression Trees)] [] [2010-12-14 14:48:39] [de55ccbf69577500a5f46ed42a101114]
-   PD    [Recursive Partitioning (Regression Trees)] [] [2010-12-21 19:59:56] [de55ccbf69577500a5f46ed42a101114]
- RMPD        [Recursive Partitioning (Regression Trees)] [] [2010-12-22 14:01:34] [6b31f806e9ccc1f74a26091056f791cb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8	350	165	3693	11.5
8	318	150	3436	11
8	302	140	3449	10.5
8	429	198	4341	10
8	440	215	4312	8.5
8	455	225	4425	10
8	383	170	3563	10
8	340	160	3609	8
8	455	225	3086	10
4	113	95	2372	15
6	199	97	2774	15.5
4	97	46	1835	20.5
4	110	87	2672	17.5
4	104	95	2375	17.5
4	121	113	2234	12.5
8	360	215	4615	14
8	307	200	4376	15
8	304	193	4732	18.5
4	97	88	2130	14.5
4	113	95	2228	14
6	250	100	3329	15.5
6	232	100	3288	15.5
8	350	165	4209	12
8	318	150	4096	13
8	400	170	4746	12
8	400	175	5140	12
4	140	72	2408	19
6	250	100	3282	15
4	122	86	2220	14
4	116	90	2123	14
4	88	76	2065	14.5
4	71	65	1773	19
4	97	60	1834	19
4	91	70	1955	20.5
4	97,5	80	2126	17
4	122	86	2226	16.5
8	350	165	4274	12
8	318	150	4135	13.5
8	351	153	4129	13
8	429	208	4633	11
8	350	155	4502	13.5
8	400	190	4422	12.5
3	70	97	2330	13.5
8	307	130	4098	14
8	302	140	4294	16
4	121	112	2933	14.5
4	121	76	2511	18
4	122	86	2395	16
4	120	97	2506	14.5
4	98	80	2164	15
8	350	175	4100	13
8	304	150	3672	11.5
8	302	137	4042	14.5
8	318	150	3777	12.5
8	400	150	4464	12
8	351	158	4363	13
8	440	215	4735	11
8	455	225	4951	11
6	225	105	3121	16.5
6	250	100	3278	18
6	250	88	3021	16.5
6	198	95	2904	16
8	400	150	4997	14
8	350	180	4499	12.5
6	232	100	2789	15
4	140	72	2401	19.5
4	108	94	2379	16.5
4	122	85	2310	18.5
6	155	107	2472	14
8	350	145	4082	13
8	400	230	4278	9.5
4	116	75	2158	15.5
4	114	91	2582	14
8	318	150	3399	11
4	121	110	2660	14
8	350	180	3664	11
6	198	95	3102	16.5
6	232	100	2901	16
4	122	80	2451	16.5
4	71	65	1836	21
6	250	100	3781	17
6	258	110	3632	18
8	302	140	4141	14
8	350	150	4699	14.5
8	302	140	4638	16
8	304	150	4257	15.5
4	79	67	1963	15.5
4	97	78	2300	14.5
4	83	61	2003	19
4	90	75	2125	14.5
4	116	75	2246	14
4	120	97	2489	15
4	79	67	2000	16
6	225	95	3264	16
6	250	72	3158	19.5
8	400	170	4668	11.5
8	350	145	4440	14
8	351	148	4657	13.5
6	231	110	3907	21
6	258	110	3730	19
6	225	95	3785	19
8	262	110	3221	13.5
8	302	129	3169	12
4	140	83	2639	17
6	232	100	2914	16
4	134	96	2702	13.5
4	90	71	2223	16.5
6	171	97	2984	14.5
4	115	95	2694	15
4	120	88	2957	17
4	121	115	2671	13.5
4	91	53	1795	17.5
4	116	81	2220	16.9
4	140	92	2572	14.9
4	101	83	2202	15.3
8	305	140	4215	13
8	304	120	3962	13.9
8	351	152	4215	12.8
6	250	105	3353	14.5
6	200	81	3012	17.6
4	85	52	2035	22.2
4	98	60	2164	22.1
6	225	100	3651	17.7
6	250	110	3645	16.2
6	258	95	3193	17.8
4	85	70	1990	17
4	97	75	2155	16.4
4	130	102	3150	15.7
8	318	150	3940	13.2
6	168	120	3820	16.7
8	350	180	4380	12.1
8	302	130	3870	15
8	318	150	3755	14
4	111	80	2155	14.8
4	79	58	1825	18.6
4	85	70	1945	16.8
8	305	145	3880	12.5
8	318	145	4140	13.7
6	231	105	3425	16.9
6	225	100	3630	17.7
8	400	180	4220	11.1
8	350	170	4165	11.4
8	351	149	4335	14.5
4	97	78	1940	14.5
4	97	75	2265	18.2
4	140	89	2755	15.8
4	98	83	2075	15.9
4	97	67	1985	16.4
6	146	97	2815	14.5
4	121	110	2600	12.8
4	90	48	1985	21.5
4	98	66	1800	14.4
4	85	70	2070	18.6
8	318	140	3735	13.2
8	302	139	3570	12.8
6	200	95	3155	18.2
6	200	85	2965	15.8
6	225	100	3430	17.2
6	232	90	3210	17.2
6	200	85	3070	16.7
6	225	110	3620	18.7
8	305	145	3425	13.2
6	231	165	3445	13.4
8	318	140	4080	13.7
4	98	68	2155	16.5
4	119	97	2300	14.7
4	105	75	2230	14.5
4	151	85	2855	17.6
5	131	103	2830	15.9
6	163	125	3140	13.6
6	163	133	3410	15.8
4	89	71	1990	14.9
4	98	68	2135	16.6
6	200	85	2990	18.2
4	140	88	2890	17.3
6	225	110	3360	16.6
8	305	130	3840	15.4
8	351	138	3955	13.2
8	318	135	3830	15.2
8	351	142	4054	14.3
8	267	125	3605	15
4	89	71	1925	14
4	86	65	1975	15.2
4	121	80	2670	15
4	141	71	3190	24.8
8	260	90	3420	22.2
4	105	70	2150	14.9
4	85	65	2020	19.2
4	151	90	2670	16
6	173	115	2595	11.3
4	151	90	2556	13.2
4	98	76	2144	14.7
4	98	70	2120	15.5
4	86	65	2019	16.4
4	140	88	2870	18.1
4	151	90	3003	20.1
4	97	78	2188	15.8
4	134	90	2711	15.5
4	119	92	2434	15
4	108	75	2265	15.2
4	156	105	2800	14.4
4	85	65	2110	19.2
5	121	67	2950	19.9
4	91	67	1850	13.8
4	89	62	1845	15.3
4	122	88	2500	15.1
4	135	84	2490	15.7
4	151	84	2635	16.4
6	173	110	2725	12.6
4	135	84	2385	12.9
4	86	64	1875	16.4
4	81	60	1760	16.1
4	85	65	1975	19.4
4	89	62	2050	17.3
4	105	63	2215	14.9
4	98	65	2045	16.2
4	105	74	2190	14.2
4	119	100	2615	14.8
4	141	80	3230	20.4
6	146	120	2930	13.8
6	231	110	3415	15.8
6	200	88	3060	17.1
6	225	85	3465	16.6
4	112	88	2640	18.6
4	112	88	2395	18
4	135	84	2525	16
4	151	90	2735	18
4	105	74	1980	15.3
4	91	68	1970	17.6
4	105	63	2125	14.7
4	120	88	2160	14.5
4	107	75	2205	14.5
4	91	67	1965	15.7
6	181	110	2945	16.4
6	262	85	3015	17
4	144	96	2665	13.9
4	151	90	2950	17.3
4	140	86	2790	15.6
4	135	84	2295	11.6
4	120	79	2625	18.6
4	72	69	1613	18
4	76	52	1649	17
4	79	58	1755	17
4	81	60	1760	16
4	71	65	1773	19
4	91	53	1795	18
4	91	53	1795	17
4	98	66	1800	14
4	91	60	1800	16
4	97	71	1825	12
4	79	58	1825	19
4	97	60	1834	19
4	97	46	1835	21
4	71	65	1836	21
4	89	62	1845	15
4	91	67	1850	14
4	68	49	1867	20
4	86	64	1875	16
4	98	80	1915	14
4	89	71	1925	14
4	90	70	1937	14
4	90	70	1937	14
4	97	78	1940	15
4	85	70	1945	17
4	97	46	1950	21
4	79	67	1950	19
4	91	70	1955	21
4	79	67	1963	16
4	91	67	1965	15
4	91	67	1965	16
4	89	60	1968	19
4	91	68	1970	18
4	86	65	1975	15
4	85	65	1975	19
4	105	74	1980	15
4	90	48	1985	22
4	97	67	1985	16
4	78	52	1985	19
4	91	68	1985	16
4	89	71	1990	15
4	85	70	1990	17
4	91	67	1995	16
4	79	67	2000	16
4	83	61	2003	19
4	86	65	2019	16
4	85	65	2020	19
4	91	68	2025	18
4	85	52	2035	22
4	98	65	2045	16
4	98	68	2045	19
4	89	62	2050	17
4	98	63	2051	17
4	88	76	2065	15
4	97	67	2065	18
4	85	70	2070	19
4	79	70	2074	20
4	98	83	2075	16
4	90	48	2085	22
4	97	88	2100	17
4	90	75	2108	16
4	86	65	2110	18
4	85	65	2110	19
4	98	70	2120	16
4	116	90	2123	14
3	70	90	2124	14
4	90	75	2125	15
4	105	63	2125	15
4	98	70	2125	17
4	97.5	80	2126	17
4	91	69	2130	15
4	97	52	2130	25
4	97	88	2130	15
4	97	88	2130	15
4	98	68	2135	17
4	98	76	2144	15
4	97	67	2145	18
4	105	70	2150	15
4	111	80	2155	15
4	98	68	2155	17
4	97	75	2155	16
4	116	75	2158	16
4	120	88	2160	15
4	98	80	2164	15
4	98	60	2164	22
4	97	75	2171	16
4	97	78	2188	16
4	96	69	2189	18
4	97	78	2190	14
4	105	74	2190	14
4	105	70	2200	13
4	101	83	2202	15
4	107	75	2205	15
4	107	75	2210	14
4	105	63	2215	15
4	98	83	2219	17
4	122	86	2220	14
4	116	81	2220	17
4	90	71	2223	17
4	122	86	2226	17
4	113	95	2228	14
4	105	75	2230	15
4	121	113	2234	13
4	108	70	2245	17
4	116	75	2246	14
4	97	54	2254	24
4	98	79	2255	18
4	140	90	2264	16
4	98	90	2265	16
4	97	75	2265	18
4	108	75	2265	15
4	113	95	2278	16
4	97	88	2279	19
4	97	92	2288	17
4	107	72	2290	17
4	135	84	2295	12
4	122	96	2300	16
4	97	78	2300	15
4	119	97	2300	15
4	122	85	2310	19
3	70	97	2330	14
4	90	48	2335	24
4	108	75	2350	17
4	135	84	2370	13
4	113	95	2372	15
4	104	95	2375	18
4	108	94	2379	17
4	98	65	2380	21
4	135	84	2385	13
4	108	93	2391	16
4	122	86	2395	16
4	112	88	2395	18
4	140	72	2401	20
4	119	97	2405	15
4	140	72	2408	19
4	140	90	2408	20
3	70	100	2420	13
4	107	90	2430	15
4	119	92	2434	15
4	122	80	2451	17
4	107	86	2464	16
6	155	107	2472	14
4	120	97	2489	15
4	135	84	2490	16
4	122	88	2500	15
4	120	97	2506	15
4	121	76	2511	18
4	134	95	2515	15
4	135	84	2525	16
4	140	75	2542	17
4	120	75	2542	18
4	119	97	2545	17
4	151	90	2556	13
4	134	95	2560	14
4	140	72	2565	14
4	140	92	2572	15
4	112	85	2575	16
4	114	91	2582	14
4	156	92	2585	15
6	200	85	2587	16
4	140	78	2592	19
6	173	115	2595	11
4	121	110	2600	13
4	112	88	2605	20
4	119	100	2615	15
4	156	92	2620	14
4	120	79	2625	19
6	232	100	2634	13
4	151	84	2635	16
4	120	74	2635	18
4	140	83	2639	17
4	112	88	2640	19
6	199	90	2648	15
4	121	110	2660	14
4	144	96	2665	14
4	121	80	2670	15
4	151	90	2670	16
4	121	115	2671	14
4	110	87	2672	18
4	151	90	2678	17
4	115	95	2694	15
6	173	115	2700	13
4	134	96	2702	14
4	134	90	2711	16
4	140	88	2720	15
4	119	82	2720	19
3	80	110	2720	14
6	173	110	2725	13
4	151	90	2735	18
4	151	88	2740	16
4	156	105	2745	17
4	140	89	2755	16
6	199	97	2774	16
6	232	100	2789	15
4	140	86	2790	16
4	121	115	2795	16
4	156	105	2800	14
6	156	122	2807	14
6	146	97	2815	15
5	131	103	2830	16
6	198	95	2833	16
6	232	112	2835	15
4	151	85	2855	18
4	140	92	2865	16
4	121	112	2868	16
4	140	88	2870	18
4	140	88	2890	17
6	168	116	2900	13
6	232	100	2901	16
6	198	95	2904	16
6	168	132	2910	11
6	232	100	2914	16
6	156	108	2930	16
6	146	120	2930	14
4	121	112	2933	15
6	232	100	2945	16
6	181	110	2945	16
4	121	98	2945	15
4	151	90	2950	17
5	121	67	2950	20
4	120	88	2957	17
6	258	110	2962	14
6	200	85	2965	16
4	120	87	2979	20
6	171	97	2984	15
6	200	85	2990	18
4	151	90	3003	20
6	200	81	3012	18
6	262	85	3015	17
6	250	88	3021	17
6	231	110	3039	15
6	200	88	3060	17
6	200	85	3070	17
6	232	90	3085	18
8	455	225	3086	10
6	198	95	3102	17
6	225	105	3121	17
6	250	88	3139	15
6	163	125	3140	14
4	130	102	3150	16
6	200	95	3155	18
6	250	72	3158	20
6	145	76	3160	20
8	302	129	3169	12
4	141	71	3190	25
6	258	95	3193	18
8	302	139	3205	11
6	232	90	3210	17
6	232	90	3211	17
8	262	110	3221	14
4	141	80	3230	20
6	225	100	3233	15
6	231	115	3245	15
4	146	67	3250	22
6	225	95	3264	16
6	232	90	3265	18
4	120	88	3270	22
6	250	100	3278	18
6	250	100	3282	15
6	232	100	3288	16
6	250	88	3302	16
6	250	100	3329	16
6	250	100	3336	17
6	250	105	3353	15
6	225	110	3360	17
8	260	110	3365	16
6	231	105	3380	16
6	225	90	3381	19
8	318	150	3399	11
6	258	120	3410	15
6	163	133	3410	16
6	231	110	3415	16
8	260	90	3420	22
6	231	105	3425	17
8	305	145	3425	13
6	225	100	3430	17
6	250	72	3432	21
8	304	150	3433	12
8	318	150	3436	11
6	225	105	3439	16
6	231	165	3445	13
8	302	140	3449	11
6	250	105	3459	16
6	225	85	3465	17
8	307	130	3504	12
6	250	110	3520	16
6	250	98	3525	19
5	183	77	3530	20
6	231	105	3535	19
8	383	170	3563	10
8	302	139	3570	13
6	250	78	3574	21
8	267	125	3605	15
8	340	160	3609	8
6	225	105	3613	17
6	225	110	3620	19
6	225	100	3630	18
6	258	110	3632	18
6	250	110	3645	16
6	225	100	3651	18
8	350	180	3664	11
8	304	150	3672	12
8	304	150	3672	12
8	350	165	3693	12
8	302	129	3725	13
8	350	105	3725	19
6	258	110	3730	19
8	318	140	3735	13
8	318	150	3755	14
8	400	150	3761	10
8	318	150	3777	13
6	250	100	3781	17
6	225	95	3785	19
6	168	120	3820	17
8	360	175	3821	11
8	318	135	3830	15
8	305	130	3840	15
8	390	190	3850	9
8	302	130	3870	15
8	305	145	3880	13
8	304	150	3892	13
6	250	105	3897	19
8	350	125	3900	17
6	231	110	3907	21
8	318	150	3940	13
8	360	150	3940	13
8	351	138	3955	13
8	304	120	3962	14
8	350	145	3988	13
8	302	137	4042	15
8	351	142	4054	14
8	350	145	4055	12
8	260	110	4060	19
8	318	150	4077	14
8	318	140	4080	14
8	350	145	4082	13
8	318	150	4096	13
8	307	130	4098	14
8	350	175	4100	13
8	351	153	4129	13
8	318	150	4135	14
8	318	145	4140	14
8	302	140	4141	14
8	351	153	4154	14
8	350	170	4165	11
8	318	150	4190	13
8	350	165	4209	12
8	305	140	4215	13
8	351	152	4215	13
8	400	180	4220	11
8	318	150	4237	15
8	304	150	4257	16
8	350	165	4274	12
8	400	230	4278	10
8	302	140	4294	16
8	302	130	4295	15
8	440	215	4312	9
8	400	190	4325	12
8	351	149	4335	15
8	429	198	4341	10
8	454	220	4354	9
8	350	155	4360	15
8	351	158	4363	13
8	307	200	4376	15
8	350	180	4380	12
8	318	210	4382	14
8	400	175	4385	12
8	400	190	4422	13
8	455	225	4425	10
8	350	145	4440	14
8	350	160	4456	14
8	318	150	4457	14
8	400	175	4464	12
8	400	150	4464	12
8	318	150	4498	15
8	350	180	4499	13
8	350	155	4502	14
8	360	215	4615	14
8	429	208	4633	11
8	302	140	4638	16
8	360	170	4654	13
8	351	148	4657	14
8	400	170	4668	12
8	350	150	4699	15
8	304	193	4732	19
8	440	215	4735	11
8	400	170	4746	12
8	400	167	4906	13
8	455	225	4951	11
8	429	198	4952	12
8	383	180	4955	12
8	400	150	4997	14
8	400	175	5140	12

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114215&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114215&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114215&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Model Performance
#Complexitysplitrelative errorCV errorCV S.D.
10.389011.0040.063
20.0710.6110.6160.042
30.05120.5410.5870.039
40.03830.490.5520.037
50.03640.4520.5220.035
60.02350.4160.4780.032
70.02160.3920.4520.028
80.01680.3510.4350.028
90.01390.3350.4150.028
100.012110.3080.4070.027
110.011120.2960.3930.027

\begin{tabular}{lllllllll}
\hline
Model Performance \tabularnewline
# & Complexity & split & relative error & CV error & CV S.D. \tabularnewline
1 & 0.389 & 0 & 1 & 1.004 & 0.063 \tabularnewline
2 & 0.07 & 1 & 0.611 & 0.616 & 0.042 \tabularnewline
3 & 0.051 & 2 & 0.541 & 0.587 & 0.039 \tabularnewline
4 & 0.038 & 3 & 0.49 & 0.552 & 0.037 \tabularnewline
5 & 0.036 & 4 & 0.452 & 0.522 & 0.035 \tabularnewline
6 & 0.023 & 5 & 0.416 & 0.478 & 0.032 \tabularnewline
7 & 0.021 & 6 & 0.392 & 0.452 & 0.028 \tabularnewline
8 & 0.016 & 8 & 0.351 & 0.435 & 0.028 \tabularnewline
9 & 0.013 & 9 & 0.335 & 0.415 & 0.028 \tabularnewline
10 & 0.012 & 11 & 0.308 & 0.407 & 0.027 \tabularnewline
11 & 0.011 & 12 & 0.296 & 0.393 & 0.027 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114215&T=1

[TABLE]
[ROW][C]Model Performance[/C][/ROW]
[ROW][C]#[/C][C]Complexity[/C][C]split[/C][C]relative error[/C][C]CV error[/C][C]CV S.D.[/C][/ROW]
[ROW][C]1[/C][C]0.389[/C][C]0[/C][C]1[/C][C]1.004[/C][C]0.063[/C][/ROW]
[ROW][C]2[/C][C]0.07[/C][C]1[/C][C]0.611[/C][C]0.616[/C][C]0.042[/C][/ROW]
[ROW][C]3[/C][C]0.051[/C][C]2[/C][C]0.541[/C][C]0.587[/C][C]0.039[/C][/ROW]
[ROW][C]4[/C][C]0.038[/C][C]3[/C][C]0.49[/C][C]0.552[/C][C]0.037[/C][/ROW]
[ROW][C]5[/C][C]0.036[/C][C]4[/C][C]0.452[/C][C]0.522[/C][C]0.035[/C][/ROW]
[ROW][C]6[/C][C]0.023[/C][C]5[/C][C]0.416[/C][C]0.478[/C][C]0.032[/C][/ROW]
[ROW][C]7[/C][C]0.021[/C][C]6[/C][C]0.392[/C][C]0.452[/C][C]0.028[/C][/ROW]
[ROW][C]8[/C][C]0.016[/C][C]8[/C][C]0.351[/C][C]0.435[/C][C]0.028[/C][/ROW]
[ROW][C]9[/C][C]0.013[/C][C]9[/C][C]0.335[/C][C]0.415[/C][C]0.028[/C][/ROW]
[ROW][C]10[/C][C]0.012[/C][C]11[/C][C]0.308[/C][C]0.407[/C][C]0.027[/C][/ROW]
[ROW][C]11[/C][C]0.011[/C][C]12[/C][C]0.296[/C][C]0.393[/C][C]0.027[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114215&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114215&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Model Performance
#Complexitysplitrelative errorCV errorCV S.D.
10.389011.0040.063
20.0710.6110.6160.042
30.05120.5410.5870.039
40.03830.490.5520.037
50.03640.4520.5220.035
60.02350.4160.4780.032
70.02160.3920.4520.028
80.01680.3510.4350.028
90.01390.3350.4150.028
100.012110.3080.4070.027
110.011120.2960.3930.027


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Minimum CV Error plus 1 SD ;
Parameters (R input):
par1 = 5 ; par2 = Minimum CV Error plus 1 SD ;
R code (references can be found in the software module):
library(rpart)
library(partykit)
par1 <- as.numeric(par1)
autoprune <- function ( tree, method='Minimum CV'){
xerr <- tree$cptable[,'xerror']
cpmin.id <- which.min(xerr)
if (method == 'Minimum CV Error plus 1 SD'){
xstd <- tree$cptable[,'xstd']
errt <- xerr[cpmin.id] + xstd[cpmin.id]
cpSE1.min <- which.min( errt < xerr )
mycp <- (tree$cptable[,'CP'])[cpSE1.min]
}
if (method == 'Minimum CV') {
mycp <- (tree$cptable[,'CP'])[cpmin.id]
}
return (mycp)
}
conf.multi.mat <- function(true, new)
{
if ( all( is.na(match( levels(true),levels(new) ) )) )
stop ( 'conflict of vector levels')
multi.t <- list()
for (mylev in levels(true) ) {
true.tmp <- true
new.tmp <- new
left.lev <- levels (true.tmp)[- match(mylev,levels(true) ) ]
levels(true.tmp) <- list ( mylev = mylev, all = left.lev )
levels(new.tmp)  <- list ( mylev = mylev, all = left.lev )
curr.t <- conf.mat ( true.tmp , new.tmp )
multi.t[[mylev]] <- curr.t
multi.t[[mylev]]$precision <-
round( curr.t$conf[1,1] / sum( curr.t$conf[1,] ), 2 )
}
return (multi.t)
}
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
m <- rpart(as.data.frame(x1))
par2
if (par2 != 'No') {
mincp <- autoprune(m,method=par2)
print(mincp)
m <- prune(m,cp=mincp)
}
m$cptable
bitmap(file='test1.png')
plot(as.party(m),tp_args=list(id=FALSE))
dev.off()
bitmap(file='test2.png')
plotcp(m)
dev.off()
cbind(y=m$y,pred=predict(m),res=residuals(m))
myr <- residuals(m)
myp <- predict(m)
bitmap(file='test4.png')
op <- par(mfrow=c(2,2))
plot(myr,ylab='residuals')
plot(density(myr),main='Residual Kernel Density')
plot(myp,myr,xlab='predicted',ylab='residuals',main='Predicted vs Residuals')
plot(density(myp),main='Prediction Kernel Density')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Model Performance',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Complexity',header=TRUE)
a<-table.element(a,'split',header=TRUE)
a<-table.element(a,'relative error',header=TRUE)
a<-table.element(a,'CV error',header=TRUE)
a<-table.element(a,'CV S.D.',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$cptable[,1])) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(m$cptable[i,'CP'],3))
a<-table.element(a,m$cptable[i,'nsplit'])
a<-table.element(a,round(m$cptable[i,'rel error'],3))
a<-table.element(a,round(m$cptable[i,'xerror'],3))
a<-table.element(a,round(m$cptable[i,'xstd'],3))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')