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Description of Statistical Computation

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 computationSun, 18 Dec 2011 09:00:14 -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/2011/Dec/18/t1324216844o6vsuaxy7y7ej49.htm/, Retrieved Sun, 05 May 2024 20:24:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156879, Retrieved Sun, 05 May 2024 20:24:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

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]
-   PD  [Recursive Partitioning (Regression Trees)] [Workshop 10, Recu...] [2010-12-10 13:08:40] [3635fb7041b1998c5a1332cf9de22bce]
- R       [Recursive Partitioning (Regression Trees)] [] [2011-12-10 13:17:20] [c505444e07acba7694d29053ca5d114e]
-    D        [Recursive Partitioning (Regression Trees)] [] [2011-12-18 14:00:14] [274a40ad31da88f12aea425a159a1f93] [Current]
-               [Recursive Partitioning (Regression Trees)] [] [2011-12-18 14:06:26] [c505444e07acba7694d29053ca5d114e]
-    D            [Recursive Partitioning (Regression Trees)] [] [2011-12-20 09:39:54] [c505444e07acba7694d29053ca5d114e]
- RMPD            [Multiple Regression] [] [2011-12-20 10:14:41] [c505444e07acba7694d29053ca5d114e]
- RM            [Multiple Regression] [] [2011-12-18 14:20:23] [c505444e07acba7694d29053ca5d114e]
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Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
272545	140824
179444	110459
222373	105079
218443	112098
167843	43929
70849	76173
33186	22807
216660	144408
213274	66485
307153	79089
237633	81625
166215	68788
364402	103297
244103	69446
384448	114948
325587	167949
323652	125081
176082	125818
266736	136588
278265	112431
180393	82317
189897	118906
234247	83515
238002	104581
267268	103129
270787	83243
155915	37110
342564	113344
282172	139165
216584	86652
318563	112302
98672	69652
391593	119442
273950	69867
227636	70168
115658	31081
349863	103925
324178	92622
178083	79011
195153	93487
177694	64520
153778	93473
455168	114360
78800	33032
208051	96125
348077	151911
175523	89256
224591	95671
24188	5950
372238	149695
65029	32551
101097	31701
279012	100087
317644	169707
340471	150491
358958	120192
252529	95893
377482	151715
304468	176225
270190	59900
264889	104767
228595	114799
216027	72128
198798	143592
238146	89626
234891	131072
175816	126817
239314	81351
73566	22618
242622	88977
187167	92059
209049	81897
360592	108146
342846	126372
207650	249771
206500	71154
182357	71571
153613	55918
456979	160141
145943	38692
280366	102812
80953	56622
150216	15986
167878	123534
369718	108535
322454	93879
179797	144551
264350	56750
262793	127654
189142	65594
275997	59938
328875	146975
189252	143372
222504	168553
287386	183500
389104	165986
397681	184923
287748	140358
294320	149959
186856	57224
43287	43750
185468	48029
235352	104978
268077	100046
305195	101047
143356	197426
154287	160902
307000	147172
298039	109432
23623	1168
195817	83248
61857	25162
163766	45724
21054	855
252805	101382
31961	14116
317367	89506
240153	135356
175083	116066
152043	144244
38214	8773
216299	102153
357602	117440
198104	104128
410803	134238
316105	134047
397297	279488
187992	79756
102424	66089
286327	102070
409878	146760
143860	154771
391854	165933
157429	64593
258751	92280
282399	67150
217665	128692
367246	124089
239072	125386
173260	37238
323545	140015
168994	150047
253330	154451
301703	156349
246435	84601
384136	68946
46660	6179
116678	52789
206501	100350

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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=156879&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=156879&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156879&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 time3 seconds
R Server'AstonUniversity' @ aston.wessa.net






Goodness of Fit
Correlation0.7101
R-squared0.5042
RMSE68168.5032

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7101 \tabularnewline
R-squared & 0.5042 \tabularnewline
RMSE & 68168.5032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156879&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7101[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5042[/C][/ROW]
[ROW][C]RMSE[/C][C]68168.5032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156879&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156879&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:

Goodness of Fit
Correlation0.7101
R-squared0.5042
RMSE68168.5032






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1272545284084.935064935-11539.9350649351
2179444284084.935064935-104640.935064935
3222373284084.935064935-61711.9350649351
4218443284084.935064935-65641.9350649351
5167843138672.629170.4
670849223908.0625-153059.0625
73318661793.5-28607.5
8216660284084.935064935-67424.9350649351
9213274223908.0625-10634.0625
10307153223908.062583244.9375
11237633223908.062513724.9375
12166215223908.0625-57693.0625
13364402284084.93506493580317.064935065
14244103223908.062520194.9375
15384448284084.935064935100363.064935065
16325587284084.93506493541502.0649350649
17323652284084.93506493539567.0649350649
18176082284084.935064935-108002.935064935
19266736284084.935064935-17348.9350649351
20278265284084.935064935-5819.93506493507
21180393223908.0625-43515.0625
22189897284084.935064935-94187.935064935
23234247223908.062510338.9375
24238002284084.935064935-46082.9350649351
25267268284084.935064935-16816.9350649351
26270787223908.062546878.9375
27155915138672.617242.4
28342564284084.93506493558479.0649350649
29282172284084.935064935-1912.93506493507
30216584223908.0625-7324.0625
31318563284084.93506493534478.0649350649
3298672223908.0625-125236.0625
33391593284084.935064935107508.064935065
34273950223908.062550041.9375
35227636223908.06253727.9375
3611565861793.553864.5
37349863284084.93506493565778.0649350649
38324178223908.0625100269.9375
39178083223908.0625-45825.0625
40195153223908.0625-28755.0625
41177694223908.0625-46214.0625
42153778223908.0625-70130.0625
43455168284084.935064935171083.064935065
447880061793.517006.5
45208051223908.0625-15857.0625
46348077284084.93506493563992.0649350649
47175523223908.0625-48385.0625
48224591223908.0625682.9375
492418861793.5-37605.5
50372238284084.93506493588153.064935065
516502961793.53235.5
5210109761793.539303.5
53279012223908.062555103.9375
54317644284084.93506493533559.0649350649
55340471284084.93506493556386.0649350649
56358958284084.93506493574873.064935065
57252529223908.062528620.9375
58377482284084.93506493593397.064935065
59304468284084.93506493520383.0649350649
60270190223908.062546281.9375
61264889284084.935064935-19195.9350649351
62228595284084.935064935-55489.9350649351
63216027223908.0625-7881.0625
64198798284084.935064935-85286.935064935
65238146223908.062514237.9375
66234891284084.935064935-49193.9350649351
67175816284084.935064935-108268.935064935
68239314223908.062515405.9375
697356661793.511772.5
70242622223908.062518713.9375
71187167223908.0625-36741.0625
72209049223908.0625-14859.0625
73360592284084.93506493576507.064935065
74342846284084.93506493558761.0649350649
75207650284084.935064935-76434.935064935
76206500223908.0625-17408.0625
77182357223908.0625-41551.0625
78153613138672.614940.4
79456979284084.935064935172894.064935065
80145943138672.67270.4
81280366284084.935064935-3718.93506493507
8280953138672.6-57719.6
8315021661793.588422.5
84167878284084.935064935-116206.935064935
85369718284084.93506493585633.064935065
86322454223908.062598545.9375
87179797284084.935064935-104287.935064935
88264350223908.062540441.9375
89262793284084.935064935-21291.9350649351
90189142223908.0625-34766.0625
91275997223908.062552088.9375
92328875284084.93506493544790.0649350649
93189252284084.935064935-94832.935064935
94222504284084.935064935-61580.9350649351
95287386284084.9350649353301.06493506493
96389104284084.935064935105019.064935065
97397681284084.935064935113596.064935065
98287748284084.9350649353663.06493506493
99294320284084.93506493510235.0649350649
100186856223908.0625-37052.0625
10143287138672.6-95385.6
102185468138672.646795.4
103235352284084.935064935-48732.9350649351
104268077223908.062544168.9375
105305195284084.93506493521110.0649350649
106143356284084.935064935-140728.935064935
107154287284084.935064935-129797.935064935
108307000284084.93506493522915.0649350649
109298039284084.93506493513954.0649350649
1102362361793.5-38170.5
111195817223908.0625-28091.0625
1126185761793.563.5
113163766138672.625093.4
1142105461793.5-40739.5
115252805284084.935064935-31279.9350649351
1163196161793.5-29832.5
117317367223908.062593458.9375
118240153284084.935064935-43931.9350649351
119175083284084.935064935-109001.935064935
120152043284084.935064935-132041.935064935
1213821461793.5-23579.5
122216299284084.935064935-67785.9350649351
123357602284084.93506493573517.064935065
124198104284084.935064935-85980.935064935
125410803284084.935064935126718.064935065
126316105284084.93506493532020.0649350649
127397297284084.935064935113212.064935065
128187992223908.0625-35916.0625
129102424223908.0625-121484.0625
130286327284084.9350649352242.06493506493
131409878284084.935064935125793.064935065
132143860284084.935064935-140224.935064935
133391854284084.935064935107769.064935065
134157429223908.0625-66479.0625
135258751223908.062534842.9375
136282399223908.062558490.9375
137217665284084.935064935-66419.9350649351
138367246284084.93506493583161.064935065
139239072284084.935064935-45012.9350649351
140173260138672.634587.4
141323545284084.93506493539460.0649350649
142168994284084.935064935-115090.935064935
143253330284084.935064935-30754.9350649351
144301703284084.93506493517618.0649350649
145246435223908.062522526.9375
146384136223908.0625160227.9375
1474666061793.5-15133.5
148116678138672.6-21994.6
149206501223908.0625-17407.0625

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 272545 & 284084.935064935 & -11539.9350649351 \tabularnewline
2 & 179444 & 284084.935064935 & -104640.935064935 \tabularnewline
3 & 222373 & 284084.935064935 & -61711.9350649351 \tabularnewline
4 & 218443 & 284084.935064935 & -65641.9350649351 \tabularnewline
5 & 167843 & 138672.6 & 29170.4 \tabularnewline
6 & 70849 & 223908.0625 & -153059.0625 \tabularnewline
7 & 33186 & 61793.5 & -28607.5 \tabularnewline
8 & 216660 & 284084.935064935 & -67424.9350649351 \tabularnewline
9 & 213274 & 223908.0625 & -10634.0625 \tabularnewline
10 & 307153 & 223908.0625 & 83244.9375 \tabularnewline
11 & 237633 & 223908.0625 & 13724.9375 \tabularnewline
12 & 166215 & 223908.0625 & -57693.0625 \tabularnewline
13 & 364402 & 284084.935064935 & 80317.064935065 \tabularnewline
14 & 244103 & 223908.0625 & 20194.9375 \tabularnewline
15 & 384448 & 284084.935064935 & 100363.064935065 \tabularnewline
16 & 325587 & 284084.935064935 & 41502.0649350649 \tabularnewline
17 & 323652 & 284084.935064935 & 39567.0649350649 \tabularnewline
18 & 176082 & 284084.935064935 & -108002.935064935 \tabularnewline
19 & 266736 & 284084.935064935 & -17348.9350649351 \tabularnewline
20 & 278265 & 284084.935064935 & -5819.93506493507 \tabularnewline
21 & 180393 & 223908.0625 & -43515.0625 \tabularnewline
22 & 189897 & 284084.935064935 & -94187.935064935 \tabularnewline
23 & 234247 & 223908.0625 & 10338.9375 \tabularnewline
24 & 238002 & 284084.935064935 & -46082.9350649351 \tabularnewline
25 & 267268 & 284084.935064935 & -16816.9350649351 \tabularnewline
26 & 270787 & 223908.0625 & 46878.9375 \tabularnewline
27 & 155915 & 138672.6 & 17242.4 \tabularnewline
28 & 342564 & 284084.935064935 & 58479.0649350649 \tabularnewline
29 & 282172 & 284084.935064935 & -1912.93506493507 \tabularnewline
30 & 216584 & 223908.0625 & -7324.0625 \tabularnewline
31 & 318563 & 284084.935064935 & 34478.0649350649 \tabularnewline
32 & 98672 & 223908.0625 & -125236.0625 \tabularnewline
33 & 391593 & 284084.935064935 & 107508.064935065 \tabularnewline
34 & 273950 & 223908.0625 & 50041.9375 \tabularnewline
35 & 227636 & 223908.0625 & 3727.9375 \tabularnewline
36 & 115658 & 61793.5 & 53864.5 \tabularnewline
37 & 349863 & 284084.935064935 & 65778.0649350649 \tabularnewline
38 & 324178 & 223908.0625 & 100269.9375 \tabularnewline
39 & 178083 & 223908.0625 & -45825.0625 \tabularnewline
40 & 195153 & 223908.0625 & -28755.0625 \tabularnewline
41 & 177694 & 223908.0625 & -46214.0625 \tabularnewline
42 & 153778 & 223908.0625 & -70130.0625 \tabularnewline
43 & 455168 & 284084.935064935 & 171083.064935065 \tabularnewline
44 & 78800 & 61793.5 & 17006.5 \tabularnewline
45 & 208051 & 223908.0625 & -15857.0625 \tabularnewline
46 & 348077 & 284084.935064935 & 63992.0649350649 \tabularnewline
47 & 175523 & 223908.0625 & -48385.0625 \tabularnewline
48 & 224591 & 223908.0625 & 682.9375 \tabularnewline
49 & 24188 & 61793.5 & -37605.5 \tabularnewline
50 & 372238 & 284084.935064935 & 88153.064935065 \tabularnewline
51 & 65029 & 61793.5 & 3235.5 \tabularnewline
52 & 101097 & 61793.5 & 39303.5 \tabularnewline
53 & 279012 & 223908.0625 & 55103.9375 \tabularnewline
54 & 317644 & 284084.935064935 & 33559.0649350649 \tabularnewline
55 & 340471 & 284084.935064935 & 56386.0649350649 \tabularnewline
56 & 358958 & 284084.935064935 & 74873.064935065 \tabularnewline
57 & 252529 & 223908.0625 & 28620.9375 \tabularnewline
58 & 377482 & 284084.935064935 & 93397.064935065 \tabularnewline
59 & 304468 & 284084.935064935 & 20383.0649350649 \tabularnewline
60 & 270190 & 223908.0625 & 46281.9375 \tabularnewline
61 & 264889 & 284084.935064935 & -19195.9350649351 \tabularnewline
62 & 228595 & 284084.935064935 & -55489.9350649351 \tabularnewline
63 & 216027 & 223908.0625 & -7881.0625 \tabularnewline
64 & 198798 & 284084.935064935 & -85286.935064935 \tabularnewline
65 & 238146 & 223908.0625 & 14237.9375 \tabularnewline
66 & 234891 & 284084.935064935 & -49193.9350649351 \tabularnewline
67 & 175816 & 284084.935064935 & -108268.935064935 \tabularnewline
68 & 239314 & 223908.0625 & 15405.9375 \tabularnewline
69 & 73566 & 61793.5 & 11772.5 \tabularnewline
70 & 242622 & 223908.0625 & 18713.9375 \tabularnewline
71 & 187167 & 223908.0625 & -36741.0625 \tabularnewline
72 & 209049 & 223908.0625 & -14859.0625 \tabularnewline
73 & 360592 & 284084.935064935 & 76507.064935065 \tabularnewline
74 & 342846 & 284084.935064935 & 58761.0649350649 \tabularnewline
75 & 207650 & 284084.935064935 & -76434.935064935 \tabularnewline
76 & 206500 & 223908.0625 & -17408.0625 \tabularnewline
77 & 182357 & 223908.0625 & -41551.0625 \tabularnewline
78 & 153613 & 138672.6 & 14940.4 \tabularnewline
79 & 456979 & 284084.935064935 & 172894.064935065 \tabularnewline
80 & 145943 & 138672.6 & 7270.4 \tabularnewline
81 & 280366 & 284084.935064935 & -3718.93506493507 \tabularnewline
82 & 80953 & 138672.6 & -57719.6 \tabularnewline
83 & 150216 & 61793.5 & 88422.5 \tabularnewline
84 & 167878 & 284084.935064935 & -116206.935064935 \tabularnewline
85 & 369718 & 284084.935064935 & 85633.064935065 \tabularnewline
86 & 322454 & 223908.0625 & 98545.9375 \tabularnewline
87 & 179797 & 284084.935064935 & -104287.935064935 \tabularnewline
88 & 264350 & 223908.0625 & 40441.9375 \tabularnewline
89 & 262793 & 284084.935064935 & -21291.9350649351 \tabularnewline
90 & 189142 & 223908.0625 & -34766.0625 \tabularnewline
91 & 275997 & 223908.0625 & 52088.9375 \tabularnewline
92 & 328875 & 284084.935064935 & 44790.0649350649 \tabularnewline
93 & 189252 & 284084.935064935 & -94832.935064935 \tabularnewline
94 & 222504 & 284084.935064935 & -61580.9350649351 \tabularnewline
95 & 287386 & 284084.935064935 & 3301.06493506493 \tabularnewline
96 & 389104 & 284084.935064935 & 105019.064935065 \tabularnewline
97 & 397681 & 284084.935064935 & 113596.064935065 \tabularnewline
98 & 287748 & 284084.935064935 & 3663.06493506493 \tabularnewline
99 & 294320 & 284084.935064935 & 10235.0649350649 \tabularnewline
100 & 186856 & 223908.0625 & -37052.0625 \tabularnewline
101 & 43287 & 138672.6 & -95385.6 \tabularnewline
102 & 185468 & 138672.6 & 46795.4 \tabularnewline
103 & 235352 & 284084.935064935 & -48732.9350649351 \tabularnewline
104 & 268077 & 223908.0625 & 44168.9375 \tabularnewline
105 & 305195 & 284084.935064935 & 21110.0649350649 \tabularnewline
106 & 143356 & 284084.935064935 & -140728.935064935 \tabularnewline
107 & 154287 & 284084.935064935 & -129797.935064935 \tabularnewline
108 & 307000 & 284084.935064935 & 22915.0649350649 \tabularnewline
109 & 298039 & 284084.935064935 & 13954.0649350649 \tabularnewline
110 & 23623 & 61793.5 & -38170.5 \tabularnewline
111 & 195817 & 223908.0625 & -28091.0625 \tabularnewline
112 & 61857 & 61793.5 & 63.5 \tabularnewline
113 & 163766 & 138672.6 & 25093.4 \tabularnewline
114 & 21054 & 61793.5 & -40739.5 \tabularnewline
115 & 252805 & 284084.935064935 & -31279.9350649351 \tabularnewline
116 & 31961 & 61793.5 & -29832.5 \tabularnewline
117 & 317367 & 223908.0625 & 93458.9375 \tabularnewline
118 & 240153 & 284084.935064935 & -43931.9350649351 \tabularnewline
119 & 175083 & 284084.935064935 & -109001.935064935 \tabularnewline
120 & 152043 & 284084.935064935 & -132041.935064935 \tabularnewline
121 & 38214 & 61793.5 & -23579.5 \tabularnewline
122 & 216299 & 284084.935064935 & -67785.9350649351 \tabularnewline
123 & 357602 & 284084.935064935 & 73517.064935065 \tabularnewline
124 & 198104 & 284084.935064935 & -85980.935064935 \tabularnewline
125 & 410803 & 284084.935064935 & 126718.064935065 \tabularnewline
126 & 316105 & 284084.935064935 & 32020.0649350649 \tabularnewline
127 & 397297 & 284084.935064935 & 113212.064935065 \tabularnewline
128 & 187992 & 223908.0625 & -35916.0625 \tabularnewline
129 & 102424 & 223908.0625 & -121484.0625 \tabularnewline
130 & 286327 & 284084.935064935 & 2242.06493506493 \tabularnewline
131 & 409878 & 284084.935064935 & 125793.064935065 \tabularnewline
132 & 143860 & 284084.935064935 & -140224.935064935 \tabularnewline
133 & 391854 & 284084.935064935 & 107769.064935065 \tabularnewline
134 & 157429 & 223908.0625 & -66479.0625 \tabularnewline
135 & 258751 & 223908.0625 & 34842.9375 \tabularnewline
136 & 282399 & 223908.0625 & 58490.9375 \tabularnewline
137 & 217665 & 284084.935064935 & -66419.9350649351 \tabularnewline
138 & 367246 & 284084.935064935 & 83161.064935065 \tabularnewline
139 & 239072 & 284084.935064935 & -45012.9350649351 \tabularnewline
140 & 173260 & 138672.6 & 34587.4 \tabularnewline
141 & 323545 & 284084.935064935 & 39460.0649350649 \tabularnewline
142 & 168994 & 284084.935064935 & -115090.935064935 \tabularnewline
143 & 253330 & 284084.935064935 & -30754.9350649351 \tabularnewline
144 & 301703 & 284084.935064935 & 17618.0649350649 \tabularnewline
145 & 246435 & 223908.0625 & 22526.9375 \tabularnewline
146 & 384136 & 223908.0625 & 160227.9375 \tabularnewline
147 & 46660 & 61793.5 & -15133.5 \tabularnewline
148 & 116678 & 138672.6 & -21994.6 \tabularnewline
149 & 206501 & 223908.0625 & -17407.0625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156879&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]272545[/C][C]284084.935064935[/C][C]-11539.9350649351[/C][/ROW]
[ROW][C]2[/C][C]179444[/C][C]284084.935064935[/C][C]-104640.935064935[/C][/ROW]
[ROW][C]3[/C][C]222373[/C][C]284084.935064935[/C][C]-61711.9350649351[/C][/ROW]
[ROW][C]4[/C][C]218443[/C][C]284084.935064935[/C][C]-65641.9350649351[/C][/ROW]
[ROW][C]5[/C][C]167843[/C][C]138672.6[/C][C]29170.4[/C][/ROW]
[ROW][C]6[/C][C]70849[/C][C]223908.0625[/C][C]-153059.0625[/C][/ROW]
[ROW][C]7[/C][C]33186[/C][C]61793.5[/C][C]-28607.5[/C][/ROW]
[ROW][C]8[/C][C]216660[/C][C]284084.935064935[/C][C]-67424.9350649351[/C][/ROW]
[ROW][C]9[/C][C]213274[/C][C]223908.0625[/C][C]-10634.0625[/C][/ROW]
[ROW][C]10[/C][C]307153[/C][C]223908.0625[/C][C]83244.9375[/C][/ROW]
[ROW][C]11[/C][C]237633[/C][C]223908.0625[/C][C]13724.9375[/C][/ROW]
[ROW][C]12[/C][C]166215[/C][C]223908.0625[/C][C]-57693.0625[/C][/ROW]
[ROW][C]13[/C][C]364402[/C][C]284084.935064935[/C][C]80317.064935065[/C][/ROW]
[ROW][C]14[/C][C]244103[/C][C]223908.0625[/C][C]20194.9375[/C][/ROW]
[ROW][C]15[/C][C]384448[/C][C]284084.935064935[/C][C]100363.064935065[/C][/ROW]
[ROW][C]16[/C][C]325587[/C][C]284084.935064935[/C][C]41502.0649350649[/C][/ROW]
[ROW][C]17[/C][C]323652[/C][C]284084.935064935[/C][C]39567.0649350649[/C][/ROW]
[ROW][C]18[/C][C]176082[/C][C]284084.935064935[/C][C]-108002.935064935[/C][/ROW]
[ROW][C]19[/C][C]266736[/C][C]284084.935064935[/C][C]-17348.9350649351[/C][/ROW]
[ROW][C]20[/C][C]278265[/C][C]284084.935064935[/C][C]-5819.93506493507[/C][/ROW]
[ROW][C]21[/C][C]180393[/C][C]223908.0625[/C][C]-43515.0625[/C][/ROW]
[ROW][C]22[/C][C]189897[/C][C]284084.935064935[/C][C]-94187.935064935[/C][/ROW]
[ROW][C]23[/C][C]234247[/C][C]223908.0625[/C][C]10338.9375[/C][/ROW]
[ROW][C]24[/C][C]238002[/C][C]284084.935064935[/C][C]-46082.9350649351[/C][/ROW]
[ROW][C]25[/C][C]267268[/C][C]284084.935064935[/C][C]-16816.9350649351[/C][/ROW]
[ROW][C]26[/C][C]270787[/C][C]223908.0625[/C][C]46878.9375[/C][/ROW]
[ROW][C]27[/C][C]155915[/C][C]138672.6[/C][C]17242.4[/C][/ROW]
[ROW][C]28[/C][C]342564[/C][C]284084.935064935[/C][C]58479.0649350649[/C][/ROW]
[ROW][C]29[/C][C]282172[/C][C]284084.935064935[/C][C]-1912.93506493507[/C][/ROW]
[ROW][C]30[/C][C]216584[/C][C]223908.0625[/C][C]-7324.0625[/C][/ROW]
[ROW][C]31[/C][C]318563[/C][C]284084.935064935[/C][C]34478.0649350649[/C][/ROW]
[ROW][C]32[/C][C]98672[/C][C]223908.0625[/C][C]-125236.0625[/C][/ROW]
[ROW][C]33[/C][C]391593[/C][C]284084.935064935[/C][C]107508.064935065[/C][/ROW]
[ROW][C]34[/C][C]273950[/C][C]223908.0625[/C][C]50041.9375[/C][/ROW]
[ROW][C]35[/C][C]227636[/C][C]223908.0625[/C][C]3727.9375[/C][/ROW]
[ROW][C]36[/C][C]115658[/C][C]61793.5[/C][C]53864.5[/C][/ROW]
[ROW][C]37[/C][C]349863[/C][C]284084.935064935[/C][C]65778.0649350649[/C][/ROW]
[ROW][C]38[/C][C]324178[/C][C]223908.0625[/C][C]100269.9375[/C][/ROW]
[ROW][C]39[/C][C]178083[/C][C]223908.0625[/C][C]-45825.0625[/C][/ROW]
[ROW][C]40[/C][C]195153[/C][C]223908.0625[/C][C]-28755.0625[/C][/ROW]
[ROW][C]41[/C][C]177694[/C][C]223908.0625[/C][C]-46214.0625[/C][/ROW]
[ROW][C]42[/C][C]153778[/C][C]223908.0625[/C][C]-70130.0625[/C][/ROW]
[ROW][C]43[/C][C]455168[/C][C]284084.935064935[/C][C]171083.064935065[/C][/ROW]
[ROW][C]44[/C][C]78800[/C][C]61793.5[/C][C]17006.5[/C][/ROW]
[ROW][C]45[/C][C]208051[/C][C]223908.0625[/C][C]-15857.0625[/C][/ROW]
[ROW][C]46[/C][C]348077[/C][C]284084.935064935[/C][C]63992.0649350649[/C][/ROW]
[ROW][C]47[/C][C]175523[/C][C]223908.0625[/C][C]-48385.0625[/C][/ROW]
[ROW][C]48[/C][C]224591[/C][C]223908.0625[/C][C]682.9375[/C][/ROW]
[ROW][C]49[/C][C]24188[/C][C]61793.5[/C][C]-37605.5[/C][/ROW]
[ROW][C]50[/C][C]372238[/C][C]284084.935064935[/C][C]88153.064935065[/C][/ROW]
[ROW][C]51[/C][C]65029[/C][C]61793.5[/C][C]3235.5[/C][/ROW]
[ROW][C]52[/C][C]101097[/C][C]61793.5[/C][C]39303.5[/C][/ROW]
[ROW][C]53[/C][C]279012[/C][C]223908.0625[/C][C]55103.9375[/C][/ROW]
[ROW][C]54[/C][C]317644[/C][C]284084.935064935[/C][C]33559.0649350649[/C][/ROW]
[ROW][C]55[/C][C]340471[/C][C]284084.935064935[/C][C]56386.0649350649[/C][/ROW]
[ROW][C]56[/C][C]358958[/C][C]284084.935064935[/C][C]74873.064935065[/C][/ROW]
[ROW][C]57[/C][C]252529[/C][C]223908.0625[/C][C]28620.9375[/C][/ROW]
[ROW][C]58[/C][C]377482[/C][C]284084.935064935[/C][C]93397.064935065[/C][/ROW]
[ROW][C]59[/C][C]304468[/C][C]284084.935064935[/C][C]20383.0649350649[/C][/ROW]
[ROW][C]60[/C][C]270190[/C][C]223908.0625[/C][C]46281.9375[/C][/ROW]
[ROW][C]61[/C][C]264889[/C][C]284084.935064935[/C][C]-19195.9350649351[/C][/ROW]
[ROW][C]62[/C][C]228595[/C][C]284084.935064935[/C][C]-55489.9350649351[/C][/ROW]
[ROW][C]63[/C][C]216027[/C][C]223908.0625[/C][C]-7881.0625[/C][/ROW]
[ROW][C]64[/C][C]198798[/C][C]284084.935064935[/C][C]-85286.935064935[/C][/ROW]
[ROW][C]65[/C][C]238146[/C][C]223908.0625[/C][C]14237.9375[/C][/ROW]
[ROW][C]66[/C][C]234891[/C][C]284084.935064935[/C][C]-49193.9350649351[/C][/ROW]
[ROW][C]67[/C][C]175816[/C][C]284084.935064935[/C][C]-108268.935064935[/C][/ROW]
[ROW][C]68[/C][C]239314[/C][C]223908.0625[/C][C]15405.9375[/C][/ROW]
[ROW][C]69[/C][C]73566[/C][C]61793.5[/C][C]11772.5[/C][/ROW]
[ROW][C]70[/C][C]242622[/C][C]223908.0625[/C][C]18713.9375[/C][/ROW]
[ROW][C]71[/C][C]187167[/C][C]223908.0625[/C][C]-36741.0625[/C][/ROW]
[ROW][C]72[/C][C]209049[/C][C]223908.0625[/C][C]-14859.0625[/C][/ROW]
[ROW][C]73[/C][C]360592[/C][C]284084.935064935[/C][C]76507.064935065[/C][/ROW]
[ROW][C]74[/C][C]342846[/C][C]284084.935064935[/C][C]58761.0649350649[/C][/ROW]
[ROW][C]75[/C][C]207650[/C][C]284084.935064935[/C][C]-76434.935064935[/C][/ROW]
[ROW][C]76[/C][C]206500[/C][C]223908.0625[/C][C]-17408.0625[/C][/ROW]
[ROW][C]77[/C][C]182357[/C][C]223908.0625[/C][C]-41551.0625[/C][/ROW]
[ROW][C]78[/C][C]153613[/C][C]138672.6[/C][C]14940.4[/C][/ROW]
[ROW][C]79[/C][C]456979[/C][C]284084.935064935[/C][C]172894.064935065[/C][/ROW]
[ROW][C]80[/C][C]145943[/C][C]138672.6[/C][C]7270.4[/C][/ROW]
[ROW][C]81[/C][C]280366[/C][C]284084.935064935[/C][C]-3718.93506493507[/C][/ROW]
[ROW][C]82[/C][C]80953[/C][C]138672.6[/C][C]-57719.6[/C][/ROW]
[ROW][C]83[/C][C]150216[/C][C]61793.5[/C][C]88422.5[/C][/ROW]
[ROW][C]84[/C][C]167878[/C][C]284084.935064935[/C][C]-116206.935064935[/C][/ROW]
[ROW][C]85[/C][C]369718[/C][C]284084.935064935[/C][C]85633.064935065[/C][/ROW]
[ROW][C]86[/C][C]322454[/C][C]223908.0625[/C][C]98545.9375[/C][/ROW]
[ROW][C]87[/C][C]179797[/C][C]284084.935064935[/C][C]-104287.935064935[/C][/ROW]
[ROW][C]88[/C][C]264350[/C][C]223908.0625[/C][C]40441.9375[/C][/ROW]
[ROW][C]89[/C][C]262793[/C][C]284084.935064935[/C][C]-21291.9350649351[/C][/ROW]
[ROW][C]90[/C][C]189142[/C][C]223908.0625[/C][C]-34766.0625[/C][/ROW]
[ROW][C]91[/C][C]275997[/C][C]223908.0625[/C][C]52088.9375[/C][/ROW]
[ROW][C]92[/C][C]328875[/C][C]284084.935064935[/C][C]44790.0649350649[/C][/ROW]
[ROW][C]93[/C][C]189252[/C][C]284084.935064935[/C][C]-94832.935064935[/C][/ROW]
[ROW][C]94[/C][C]222504[/C][C]284084.935064935[/C][C]-61580.9350649351[/C][/ROW]
[ROW][C]95[/C][C]287386[/C][C]284084.935064935[/C][C]3301.06493506493[/C][/ROW]
[ROW][C]96[/C][C]389104[/C][C]284084.935064935[/C][C]105019.064935065[/C][/ROW]
[ROW][C]97[/C][C]397681[/C][C]284084.935064935[/C][C]113596.064935065[/C][/ROW]
[ROW][C]98[/C][C]287748[/C][C]284084.935064935[/C][C]3663.06493506493[/C][/ROW]
[ROW][C]99[/C][C]294320[/C][C]284084.935064935[/C][C]10235.0649350649[/C][/ROW]
[ROW][C]100[/C][C]186856[/C][C]223908.0625[/C][C]-37052.0625[/C][/ROW]
[ROW][C]101[/C][C]43287[/C][C]138672.6[/C][C]-95385.6[/C][/ROW]
[ROW][C]102[/C][C]185468[/C][C]138672.6[/C][C]46795.4[/C][/ROW]
[ROW][C]103[/C][C]235352[/C][C]284084.935064935[/C][C]-48732.9350649351[/C][/ROW]
[ROW][C]104[/C][C]268077[/C][C]223908.0625[/C][C]44168.9375[/C][/ROW]
[ROW][C]105[/C][C]305195[/C][C]284084.935064935[/C][C]21110.0649350649[/C][/ROW]
[ROW][C]106[/C][C]143356[/C][C]284084.935064935[/C][C]-140728.935064935[/C][/ROW]
[ROW][C]107[/C][C]154287[/C][C]284084.935064935[/C][C]-129797.935064935[/C][/ROW]
[ROW][C]108[/C][C]307000[/C][C]284084.935064935[/C][C]22915.0649350649[/C][/ROW]
[ROW][C]109[/C][C]298039[/C][C]284084.935064935[/C][C]13954.0649350649[/C][/ROW]
[ROW][C]110[/C][C]23623[/C][C]61793.5[/C][C]-38170.5[/C][/ROW]
[ROW][C]111[/C][C]195817[/C][C]223908.0625[/C][C]-28091.0625[/C][/ROW]
[ROW][C]112[/C][C]61857[/C][C]61793.5[/C][C]63.5[/C][/ROW]
[ROW][C]113[/C][C]163766[/C][C]138672.6[/C][C]25093.4[/C][/ROW]
[ROW][C]114[/C][C]21054[/C][C]61793.5[/C][C]-40739.5[/C][/ROW]
[ROW][C]115[/C][C]252805[/C][C]284084.935064935[/C][C]-31279.9350649351[/C][/ROW]
[ROW][C]116[/C][C]31961[/C][C]61793.5[/C][C]-29832.5[/C][/ROW]
[ROW][C]117[/C][C]317367[/C][C]223908.0625[/C][C]93458.9375[/C][/ROW]
[ROW][C]118[/C][C]240153[/C][C]284084.935064935[/C][C]-43931.9350649351[/C][/ROW]
[ROW][C]119[/C][C]175083[/C][C]284084.935064935[/C][C]-109001.935064935[/C][/ROW]
[ROW][C]120[/C][C]152043[/C][C]284084.935064935[/C][C]-132041.935064935[/C][/ROW]
[ROW][C]121[/C][C]38214[/C][C]61793.5[/C][C]-23579.5[/C][/ROW]
[ROW][C]122[/C][C]216299[/C][C]284084.935064935[/C][C]-67785.9350649351[/C][/ROW]
[ROW][C]123[/C][C]357602[/C][C]284084.935064935[/C][C]73517.064935065[/C][/ROW]
[ROW][C]124[/C][C]198104[/C][C]284084.935064935[/C][C]-85980.935064935[/C][/ROW]
[ROW][C]125[/C][C]410803[/C][C]284084.935064935[/C][C]126718.064935065[/C][/ROW]
[ROW][C]126[/C][C]316105[/C][C]284084.935064935[/C][C]32020.0649350649[/C][/ROW]
[ROW][C]127[/C][C]397297[/C][C]284084.935064935[/C][C]113212.064935065[/C][/ROW]
[ROW][C]128[/C][C]187992[/C][C]223908.0625[/C][C]-35916.0625[/C][/ROW]
[ROW][C]129[/C][C]102424[/C][C]223908.0625[/C][C]-121484.0625[/C][/ROW]
[ROW][C]130[/C][C]286327[/C][C]284084.935064935[/C][C]2242.06493506493[/C][/ROW]
[ROW][C]131[/C][C]409878[/C][C]284084.935064935[/C][C]125793.064935065[/C][/ROW]
[ROW][C]132[/C][C]143860[/C][C]284084.935064935[/C][C]-140224.935064935[/C][/ROW]
[ROW][C]133[/C][C]391854[/C][C]284084.935064935[/C][C]107769.064935065[/C][/ROW]
[ROW][C]134[/C][C]157429[/C][C]223908.0625[/C][C]-66479.0625[/C][/ROW]
[ROW][C]135[/C][C]258751[/C][C]223908.0625[/C][C]34842.9375[/C][/ROW]
[ROW][C]136[/C][C]282399[/C][C]223908.0625[/C][C]58490.9375[/C][/ROW]
[ROW][C]137[/C][C]217665[/C][C]284084.935064935[/C][C]-66419.9350649351[/C][/ROW]
[ROW][C]138[/C][C]367246[/C][C]284084.935064935[/C][C]83161.064935065[/C][/ROW]
[ROW][C]139[/C][C]239072[/C][C]284084.935064935[/C][C]-45012.9350649351[/C][/ROW]
[ROW][C]140[/C][C]173260[/C][C]138672.6[/C][C]34587.4[/C][/ROW]
[ROW][C]141[/C][C]323545[/C][C]284084.935064935[/C][C]39460.0649350649[/C][/ROW]
[ROW][C]142[/C][C]168994[/C][C]284084.935064935[/C][C]-115090.935064935[/C][/ROW]
[ROW][C]143[/C][C]253330[/C][C]284084.935064935[/C][C]-30754.9350649351[/C][/ROW]
[ROW][C]144[/C][C]301703[/C][C]284084.935064935[/C][C]17618.0649350649[/C][/ROW]
[ROW][C]145[/C][C]246435[/C][C]223908.0625[/C][C]22526.9375[/C][/ROW]
[ROW][C]146[/C][C]384136[/C][C]223908.0625[/C][C]160227.9375[/C][/ROW]
[ROW][C]147[/C][C]46660[/C][C]61793.5[/C][C]-15133.5[/C][/ROW]
[ROW][C]148[/C][C]116678[/C][C]138672.6[/C][C]-21994.6[/C][/ROW]
[ROW][C]149[/C][C]206501[/C][C]223908.0625[/C][C]-17407.0625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156879&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156879&T=2

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


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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1272545284084.935064935-11539.9350649351
2179444284084.935064935-104640.935064935
3222373284084.935064935-61711.9350649351
4218443284084.935064935-65641.9350649351
5167843138672.629170.4
670849223908.0625-153059.0625
73318661793.5-28607.5
8216660284084.935064935-67424.9350649351
9213274223908.0625-10634.0625
10307153223908.062583244.9375
11237633223908.062513724.9375
12166215223908.0625-57693.0625
13364402284084.93506493580317.064935065
14244103223908.062520194.9375
15384448284084.935064935100363.064935065
16325587284084.93506493541502.0649350649
17323652284084.93506493539567.0649350649
18176082284084.935064935-108002.935064935
19266736284084.935064935-17348.9350649351
20278265284084.935064935-5819.93506493507
21180393223908.0625-43515.0625
22189897284084.935064935-94187.935064935
23234247223908.062510338.9375
24238002284084.935064935-46082.9350649351
25267268284084.935064935-16816.9350649351
26270787223908.062546878.9375
27155915138672.617242.4
28342564284084.93506493558479.0649350649
29282172284084.935064935-1912.93506493507
30216584223908.0625-7324.0625
31318563284084.93506493534478.0649350649
3298672223908.0625-125236.0625
33391593284084.935064935107508.064935065
34273950223908.062550041.9375
35227636223908.06253727.9375
3611565861793.553864.5
37349863284084.93506493565778.0649350649
38324178223908.0625100269.9375
39178083223908.0625-45825.0625
40195153223908.0625-28755.0625
41177694223908.0625-46214.0625
42153778223908.0625-70130.0625
43455168284084.935064935171083.064935065
447880061793.517006.5
45208051223908.0625-15857.0625
46348077284084.93506493563992.0649350649
47175523223908.0625-48385.0625
48224591223908.0625682.9375
492418861793.5-37605.5
50372238284084.93506493588153.064935065
516502961793.53235.5
5210109761793.539303.5
53279012223908.062555103.9375
54317644284084.93506493533559.0649350649
55340471284084.93506493556386.0649350649
56358958284084.93506493574873.064935065
57252529223908.062528620.9375
58377482284084.93506493593397.064935065
59304468284084.93506493520383.0649350649
60270190223908.062546281.9375
61264889284084.935064935-19195.9350649351
62228595284084.935064935-55489.9350649351
63216027223908.0625-7881.0625
64198798284084.935064935-85286.935064935
65238146223908.062514237.9375
66234891284084.935064935-49193.9350649351
67175816284084.935064935-108268.935064935
68239314223908.062515405.9375
697356661793.511772.5
70242622223908.062518713.9375
71187167223908.0625-36741.0625
72209049223908.0625-14859.0625
73360592284084.93506493576507.064935065
74342846284084.93506493558761.0649350649
75207650284084.935064935-76434.935064935
76206500223908.0625-17408.0625
77182357223908.0625-41551.0625
78153613138672.614940.4
79456979284084.935064935172894.064935065
80145943138672.67270.4
81280366284084.935064935-3718.93506493507
8280953138672.6-57719.6
8315021661793.588422.5
84167878284084.935064935-116206.935064935
85369718284084.93506493585633.064935065
86322454223908.062598545.9375
87179797284084.935064935-104287.935064935
88264350223908.062540441.9375
89262793284084.935064935-21291.9350649351
90189142223908.0625-34766.0625
91275997223908.062552088.9375
92328875284084.93506493544790.0649350649
93189252284084.935064935-94832.935064935
94222504284084.935064935-61580.9350649351
95287386284084.9350649353301.06493506493
96389104284084.935064935105019.064935065
97397681284084.935064935113596.064935065
98287748284084.9350649353663.06493506493
99294320284084.93506493510235.0649350649
100186856223908.0625-37052.0625
10143287138672.6-95385.6
102185468138672.646795.4
103235352284084.935064935-48732.9350649351
104268077223908.062544168.9375
105305195284084.93506493521110.0649350649
106143356284084.935064935-140728.935064935
107154287284084.935064935-129797.935064935
108307000284084.93506493522915.0649350649
109298039284084.93506493513954.0649350649
1102362361793.5-38170.5
111195817223908.0625-28091.0625
1126185761793.563.5
113163766138672.625093.4
1142105461793.5-40739.5
115252805284084.935064935-31279.9350649351
1163196161793.5-29832.5
117317367223908.062593458.9375
118240153284084.935064935-43931.9350649351
119175083284084.935064935-109001.935064935
120152043284084.935064935-132041.935064935
1213821461793.5-23579.5
122216299284084.935064935-67785.9350649351
123357602284084.93506493573517.064935065
124198104284084.935064935-85980.935064935
125410803284084.935064935126718.064935065
126316105284084.93506493532020.0649350649
127397297284084.935064935113212.064935065
128187992223908.0625-35916.0625
129102424223908.0625-121484.0625
130286327284084.9350649352242.06493506493
131409878284084.935064935125793.064935065
132143860284084.935064935-140224.935064935
133391854284084.935064935107769.064935065
134157429223908.0625-66479.0625
135258751223908.062534842.9375
136282399223908.062558490.9375
137217665284084.935064935-66419.9350649351
138367246284084.93506493583161.064935065
139239072284084.935064935-45012.9350649351
140173260138672.634587.4
141323545284084.93506493539460.0649350649
142168994284084.935064935-115090.935064935
143253330284084.935064935-30754.9350649351
144301703284084.93506493517618.0649350649
145246435223908.062522526.9375
146384136223908.0625160227.9375
1474666061793.5-15133.5
148116678138672.6-21994.6
149206501223908.0625-17407.0625


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = none ; par4 = no ;
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')
}