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5

*The author of this computation has been verified*

R Software Module: Ian.Holliday/rwasp_One Factor ANOVA.wasp (opens new window with default values)

Title produced by software: One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)

Date of computation: Mon, 29 Nov 2010 22:34:29 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/29/t12910701708iofpoxd8328uci.htm/, Retrieved Mon, 29 Nov 2010 23:36:10 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/29/t12910701708iofpoxd8328uci.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1 36 1 36 2 56 2 48 2 32 1 44 2 39 2 34 3 41 3 50 1 39 3 62 2 52 3 37 2 50 1 41 2 55 2 41 3 56 2 39 1 52 2 46 2 44 2 48 2 41 3 50 3 50 2 44 1 52 2 54 2 44 3 52 2 37 3 52 3 50 1 36 1 50 3 52 3 55 2 31 1 36 1 49 1 42 2 37 2 41 1 30 1 52 3 30 2 41 1 44 2 66 3 48 2 43 2 57 1 46 3 54 3 48 2 48 1 52 1 62 3 58 2 58 2 62 2 48 2 46 1 34 2 66 3 52 2 55 1 55 3 57 5 56 1 55 3 56 1 54 2 55 2 46 1 52 2 32 1 44 2 46 2 59 3 46 3 46 3 54 3 66 2 56 2 59 2 57 3 52 1 48 1 44 2 41 1 50 3 48 2 48 2 59 2 46 2 54 2 55 3 54 2 59 2 44 3 54 3 52 3 66 2 44 2 57 1 39 3 60 2 45 2 41 2 50 2 39 2 43 1 48 2 37 2 58 1 46 1 43 2 44 3 34 1 30 3 50 1 39 2 37 2 55 3 48 1 39 3 36 2 43 3 50 2 55 2 43 3 60 2 48 3 30 2 43 1 39 2 52 1 39 1 39 1 56 1 59 2 46 2 57 2 50 1 54 3 50 3 60 3 59 2 41 1 48 2 59 3 60 2 56 2 56 1 51

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

ANOVA Model
MWARM30 ~ MC30VRB
means	2	0	0	0	-0.6	0.2	-0.7	0	-1	-0.167	-0.4	0	0	0.077	-1	0.417	-1	0.077	0.25	-0.111	0.5	0.2	0.333	0	1	0	0.5

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
MC30VRB	26	21.991	0.846	1.562	0.055
Residuals	131	70.952	0.542

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
31-30	0	-3.109	3.109	1
32-30	0	-2.409	2.409	1
34-30	0	-2.124	2.124	1
36-30	-0.6	-2.466	1.266	1
37-30	0.2	-1.666	2.066	1
39-30	-0.7	-2.345	0.945	0.998
41-30	0	-1.671	1.671	1
42-30	-1	-4.109	2.109	1
43-30	-0.167	-1.962	1.629	1
44-30	-0.4	-2.045	1.245	1
45-30	0	-3.109	3.109	1
46-30	0	-1.645	1.645	1
48-30	0.077	-1.513	1.667	1
49-30	-1	-4.109	2.109	1
50-30	0.417	-1.189	2.022	1
51-30	-1	-4.109	2.109	1
52-30	0.077	-1.513	1.667	1
54-30	0.25	-1.453	1.953	1
55-30	-0.111	-1.782	1.56	1
56-30	0.5	-1.203	2.203	1
57-30	0.2	-1.666	2.066	1
58-30	0.333	-1.791	2.457	1
59-30	0	-1.743	1.743	1
60-30	1	-0.967	2.967	0.975
62-30	0	-2.124	2.124	1
66-30	0.5	-1.467	2.467	1
32-31	0	-3.406	3.406	1
34-31	0	-3.211	3.211	1
36-31	-0.6	-3.647	2.447	1
37-31	0.2	-2.847	3.247	1
39-31	-0.7	-3.617	2.217	1
41-31	0	-2.932	2.932	1
42-31	-1	-4.933	2.933	1
43-31	-0.167	-3.171	2.837	1
44-31	-0.4	-3.317	2.517	1
45-31	0	-3.933	3.933	1
46-31	0	-2.917	2.917	1
48-31	0.077	-2.809	2.963	1
49-31	-1	-4.933	2.933	1
50-31	0.417	-2.478	3.311	1
51-31	-1	-4.933	2.933	1
52-31	0.077	-2.809	2.963	1
54-31	0.25	-2.7	3.2	1
55-31	-0.111	-3.043	2.82	1
56-31	0.5	-2.45	3.45	1
57-31	0.2	-2.847	3.247	1
58-31	0.333	-2.878	3.545	1
59-31	0	-2.973	2.973	1
60-31	1	-2.109	4.109	1
62-31	0	-3.211	3.211	1
66-31	0.5	-2.609	3.609	1
34-32	0	-2.539	2.539	1
36-32	-0.6	-2.927	1.727	1
37-32	0.2	-2.127	2.527	1
39-32	-0.7	-2.854	1.454	1
41-32	0	-2.174	2.174	1
42-32	-1	-4.406	2.406	1
43-32	-0.167	-2.437	2.104	1
44-32	-0.4	-2.554	1.754	1
45-32	0	-3.406	3.406	1
46-32	0	-2.154	2.154	1
48-32	0.077	-2.036	2.189	1
49-32	-1	-4.406	2.406	1
50-32	0.417	-1.707	2.541	1
51-32	-1	-4.406	2.406	1
52-32	0.077	-2.036	2.189	1
54-32	0.25	-1.949	2.449	1
55-32	-0.111	-2.285	2.063	1
56-32	0.5	-1.699	2.699	1
57-32	0.2	-2.127	2.527	1
58-32	0.333	-2.205	2.872	1
59-32	0	-2.23	2.23	1
60-32	1	-1.409	3.409	0.998
62-32	0	-2.539	2.539	1
66-32	0.5	-1.909	2.909	1
36-34	-0.6	-2.631	1.431	1
37-34	0.2	-1.831	2.231	1
39-34	-0.7	-2.531	1.131	1
41-34	0	-1.854	1.854	1
42-34	-1	-4.211	2.211	1
43-34	-0.167	-2.133	1.8	1
44-34	-0.4	-2.231	1.431	1
45-34	0	-3.211	3.211	1
46-34	0	-1.831	1.831	1
48-34	0.077	-1.704	1.858	1
49-34	-1	-4.211	2.211	1
50-34	0.417	-1.379	2.212	1
51-34	-1	-4.211	2.211	1
52-34	0.077	-1.704	1.858	1
54-34	0.25	-1.633	2.133	1
55-34	-0.111	-1.965	1.743	1
56-34	0.5	-1.383	2.383	1
57-34	0.2	-1.831	2.231	1
58-34	0.333	-1.937	2.604	1
59-34	0	-1.919	1.919	1
60-34	1	-1.124	3.124	0.99
62-34	0	-2.271	2.271	1
66-34	0.5	-1.624	2.624	1
37-36	0.8	-0.959	2.559	0.994
39-36	-0.1	-1.623	1.423	1
41-36	0.6	-0.951	2.151	0.999
42-36	-0.4	-3.447	2.647	1
43-36	0.433	-1.251	2.117	1
44-36	0.2	-1.323	1.723	1
45-36	0.6	-2.447	3.647	1
46-36	0.6	-0.923	2.123	0.999
48-36	0.677	-0.787	2.14	0.992
49-36	-0.4	-3.447	2.647	1
50-36	1.017	-0.464	2.497	0.658
51-36	-0.4	-3.447	2.647	1
52-36	0.677	-0.787	2.14	0.992
54-36	0.85	-0.735	2.435	0.955
55-36	0.489	-1.062	2.04	1
56-36	1.1	-0.485	2.685	0.638
57-36	0.8	-0.959	2.559	0.994
58-36	0.933	-1.098	2.964	0.993
59-36	0.6	-1.028	2.228	1
60-36	1.6	-0.266	3.466	0.214
62-36	0.6	-1.431	2.631	1
66-36	1.1	-0.766	2.966	0.886
39-37	-0.9	-2.423	0.623	0.884
41-37	-0.2	-1.751	1.351	1
42-37	-1.2	-4.247	1.847	0.999
43-37	-0.367	-2.051	1.317	1
44-37	-0.6	-2.123	0.923	0.999
45-37	-0.2	-3.247	2.847	1
46-37	-0.2	-1.723	1.323	1
48-37	-0.123	-1.587	1.34	1
49-37	-1.2	-4.247	1.847	0.999
50-37	0.217	-1.264	1.697	1
51-37	-1.2	-4.247	1.847	0.999
52-37	-0.123	-1.587	1.34	1
54-37	0.05	-1.535	1.635	1
55-37	-0.311	-1.862	1.24	1
56-37	0.3	-1.285	1.885	1
57-37	0	-1.759	1.759	1
58-37	0.133	-1.898	2.164	1
59-37	-0.2	-1.828	1.428	1
60-37	0.8	-1.066	2.666	0.997
62-37	-0.2	-2.231	1.831	1
66-37	0.3	-1.566	2.166	1
41-39	0.7	-0.578	1.978	0.944
42-39	-0.3	-3.217	2.617	1
43-39	0.533	-0.903	1.97	1
44-39	0.3	-0.944	1.544	1
45-39	0.7	-2.217	3.617	1
46-39	0.7	-0.544	1.944	0.926
48-39	0.777	-0.393	1.947	0.721
49-39	-0.3	-3.217	2.617	1
50-39	1.117	-0.074	2.307	0.099
51-39	-0.3	-3.217	2.617	1
52-39	0.777	-0.393	1.947	0.721
54-39	0.95	-0.369	2.269	0.562
55-39	0.589	-0.689	1.867	0.993
56-39	1.2	-0.119	2.519	0.132
57-39	0.9	-0.623	2.423	0.884
58-39	1.033	-0.797	2.864	0.924
59-39	0.7	-0.671	2.071	0.974
60-39	1.7	0.055	3.345	0.034
62-39	0.7	-1.131	2.531	1
66-39	1.2	-0.445	2.845	0.535
42-41	-1	-3.932	1.932	1
43-41	-0.167	-1.632	1.299	1
44-41	-0.4	-1.678	0.878	1
45-41	0	-2.932	2.932	1
46-41	0	-1.278	1.278	1
48-41	0.077	-1.129	1.283	1
49-41	-1	-3.932	1.932	1
50-41	0.417	-0.81	1.643	1
51-41	-1	-3.932	1.932	1
52-41	0.077	-1.129	1.283	1
54-41	0.25	-1.101	1.601	1
55-41	-0.111	-1.422	1.2	1
56-41	0.5	-0.851	1.851	1
57-41	0.2	-1.351	1.751	1
58-41	0.333	-1.521	2.187	1
59-41	0	-1.402	1.402	1
60-41	1	-0.671	2.671	0.871
62-41	0	-1.854	1.854	1
66-41	0.5	-1.171	2.171	1
43-42	0.833	-2.171	3.837	1
44-42	0.6	-2.317	3.517	1
45-42	1	-2.933	4.933	1
46-42	1	-1.917	3.917	1
48-42	1.077	-1.809	3.963	1
49-42	0	-3.933	3.933	1
50-42	1.417	-1.478	4.311	0.984
51-42	0	-3.933	3.933	1
52-42	1.077	-1.809	3.963	1
54-42	1.25	-1.7	4.2	0.998
55-42	0.889	-2.043	3.82	1
56-42	1.5	-1.45	4.45	0.975
57-42	1.2	-1.847	4.247	0.999
58-42	1.333	-1.878	4.545	0.998
59-42	1	-1.973	3.973	1
60-42	2	-1.109	5.109	0.775
62-42	1	-2.211	4.211	1
66-42	1.5	-1.609	4.609	0.987
44-43	-0.233	-1.67	1.203	1
45-43	0.167	-2.837	3.171	1
46-43	0.167	-1.27	1.603	1
48-43	0.244	-1.129	1.616	1
49-43	-0.833	-3.837	2.171	1
50-43	0.583	-0.807	1.974	0.998
51-43	-0.833	-3.837	2.171	1
52-43	0.244	-1.129	1.616	1
54-43	0.417	-1.085	1.919	1
55-43	0.056	-1.41	1.521	1
56-43	0.667	-0.835	2.169	0.996
57-43	0.367	-1.317	2.051	1
58-43	0.5	-1.467	2.467	1
59-43	0.167	-1.381	1.714	1
60-43	1.167	-0.629	2.962	0.758
62-43	0.167	-1.8	2.133	1
66-43	0.667	-1.129	2.462	1
45-44	0.4	-2.517	3.317	1
46-44	0.4	-0.844	1.644	1
48-44	0.477	-0.693	1.647	0.999
49-44	-0.6	-3.517	2.317	1
50-44	0.817	-0.374	2.007	0.661
51-44	-0.6	-3.517	2.317	1
52-44	0.477	-0.693	1.647	0.999
54-44	0.65	-0.669	1.969	0.983
55-44	0.289	-0.989	1.567	1
56-44	0.9	-0.419	2.219	0.671
57-44	0.6	-0.923	2.123	0.999
58-44	0.733	-1.097	2.564	0.999
59-44	0.4	-0.971	1.771	1
60-44	1.4	-0.245	3.045	0.227
62-44	0.4	-1.431	2.231	1
66-44	0.9	-0.745	2.545	0.944
46-45	0	-2.917	2.917	1
48-45	0.077	-2.809	2.963	1
49-45	-1	-4.933	2.933	1
50-45	0.417	-2.478	3.311	1
51-45	-1	-4.933	2.933	1
52-45	0.077	-2.809	2.963	1
54-45	0.25	-2.7	3.2	1
55-45	-0.111	-3.043	2.82	1
56-45	0.5	-2.45	3.45	1
57-45	0.2	-2.847	3.247	1
58-45	0.333	-2.878	3.545	1
59-45	0	-2.973	2.973	1
60-45	1	-2.109	4.109	1
62-45	0	-3.211	3.211	1
66-45	0.5	-2.609	3.609	1
48-46	0.077	-1.093	1.247	1
49-46	-1	-3.917	1.917	1
50-46	0.417	-0.774	1.607	1
51-46	-1	-3.917	1.917	1
52-46	0.077	-1.093	1.247	1
54-46	0.25	-1.069	1.569	1
55-46	-0.111	-1.389	1.167	1
56-46	0.5	-0.819	1.819	1
57-46	0.2	-1.323	1.723	1
58-46	0.333	-1.497	2.164	1
59-46	0	-1.371	1.371	1
60-46	1	-0.645	2.645	0.853
62-46	0	-1.831	1.831	1
66-46	0.5	-1.145	2.145	1
49-48	-1.077	-3.963	1.809	1
50-48	0.34	-0.774	1.453	1
51-48	-1.077	-3.963	1.809	1
52-48	0	-1.091	1.091	1
54-48	0.173	-1.077	1.423	1
55-48	-0.188	-1.394	1.018	1
56-48	0.423	-0.827	1.673	1
57-48	0.123	-1.34	1.587	1
58-48	0.256	-1.525	2.038	1
59-48	-0.077	-1.381	1.227	1
60-48	0.923	-0.667	2.513	0.901
62-48	-0.077	-1.858	1.704	1
66-48	0.423	-1.167	2.013	1
50-49	1.417	-1.478	4.311	0.984
51-49	0	-3.933	3.933	1
52-49	1.077	-1.809	3.963	1
54-49	1.25	-1.7	4.2	0.998
55-49	0.889	-2.043	3.82	1
56-49	1.5	-1.45	4.45	0.975
57-49	1.2	-1.847	4.247	0.999
58-49	1.333	-1.878	4.545	0.998
59-49	1	-1.973	3.973	1
60-49	2	-1.109	5.109	0.775
62-49	1	-2.211	4.211	1
66-49	1.5	-1.609	4.609	0.987
51-50	-1.417	-4.311	1.478	0.984
52-50	-0.34	-1.453	0.774	1
54-50	-0.167	-1.436	1.103	1
55-50	-0.528	-1.754	0.699	0.997
56-50	0.083	-1.186	1.353	1
57-50	-0.217	-1.697	1.264	1
58-50	-0.083	-1.879	1.712	1
59-50	-0.417	-1.739	0.906	1
60-50	0.583	-1.022	2.189	1
62-50	-0.417	-2.212	1.379	1
66-50	0.083	-1.522	1.689	1
52-51	1.077	-1.809	3.963	1
54-51	1.25	-1.7	4.2	0.998
55-51	0.889	-2.043	3.82	1
56-51	1.5	-1.45	4.45	0.975
57-51	1.2	-1.847	4.247	0.999
58-51	1.333	-1.878	4.545	0.998
59-51	1	-1.973	3.973	1
60-51	2	-1.109	5.109	0.775
62-51	1	-2.211	4.211	1
66-51	1.5	-1.609	4.609	0.987
54-52	0.173	-1.077	1.423	1
55-52	-0.188	-1.394	1.018	1
56-52	0.423	-0.827	1.673	1
57-52	0.123	-1.34	1.587	1
58-52	0.256	-1.525	2.038	1
59-52	-0.077	-1.381	1.227	1
60-52	0.923	-0.667	2.513	0.901
62-52	-0.077	-1.858	1.704	1
66-52	0.423	-1.167	2.013	1
55-54	-0.361	-1.713	0.99	1
56-54	0.25	-1.141	1.641	1
57-54	-0.05	-1.635	1.535	1
58-54	0.083	-1.8	1.966	1
59-54	-0.25	-1.689	1.189	1
60-54	0.75	-0.953	2.453	0.996
62-54	-0.25	-2.133	1.633	1
66-54	0.25	-1.453	1.953	1
56-55	0.611	-0.74	1.963	0.994
57-55	0.311	-1.24	1.862	1
58-55	0.444	-1.41	2.299	1
59-55	0.111	-1.29	1.513	1
60-55	1.111	-0.56	2.782	0.719
62-55	0.111	-1.743	1.965	1
66-55	0.611	-1.06	2.282	1
57-56	-0.3	-1.885	1.285	1
58-56	-0.167	-2.05	1.716	1
59-56	-0.5	-1.939	0.939	1
60-56	0.5	-1.203	2.203	1
62-56	-0.5	-2.383	1.383	1
66-56	0	-1.703	1.703	1
58-57	0.133	-1.898	2.164	1
59-57	-0.2	-1.828	1.428	1
60-57	0.8	-1.066	2.666	0.997
62-57	-0.2	-2.231	1.831	1
66-57	0.3	-1.566	2.166	1
59-58	-0.333	-2.253	1.586	1
60-58	0.667	-1.457	2.791	1
62-58	-0.333	-2.604	1.937	1
66-58	0.167	-1.957	2.291	1
60-59	1	-0.743	2.743	0.911
62-59	0	-1.919	1.919	1
66-59	0.5	-1.243	2.243	1
62-60	-1	-3.124	1.124	0.99
66-60	-0.5	-2.467	1.467	1
66-62	0.5	-1.624	2.624	1

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	26	1.216	0.235
	131

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/29/t12910701708iofpoxd8328uci/38ujt1291070061.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t12910701708iofpoxd8328uci/38ujt1291070061.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t12910701708iofpoxd8328uci/4j30e1291070061.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t12910701708iofpoxd8328uci/4j30e1291070061.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = TRUE ;

Parameters (R input):

par1 = 1 ; par2 = 2 ; par3 = TRUE ;

R code (references can be found in the software module):

cat1 <- as.numeric(par1) #

cat2<- as.numeric(par2) #

intercept<-as.logical(par3)

x <- t(x)

x1<-as.numeric(x[,cat1])

f1<-as.character(x[,cat2])

xdf<-data.frame(x1,f1)

(V1<-dimnames(y)[[1]][cat1])

(V2<-dimnames(y)[[1]][cat2])

names(xdf)<-c('Response', 'Treatment')

if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment - 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment, data = xdf) )

(aov.xdf<-aov(lmxdf) )

(anova.xdf<-anova(lmxdf) )

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'ANOVA Model', length(lmxdf$coefficients)+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, paste(V1, ' ~ ', V2), length(lmxdf$coefficients)+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'means',,TRUE)

for(i in 1:length(lmxdf$coefficients)){

a<-table.element(a, round(lmxdf$coefficients[i], digits=3),,FALSE)

}

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, ' ',,TRUE)

a<-table.element(a, 'Df',,FALSE)

a<-table.element(a, 'Sum Sq',,FALSE)

a<-table.element(a, 'Mean Sq',,FALSE)

a<-table.element(a, 'F value',,FALSE)

a<-table.element(a, 'Pr(>F)',,FALSE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, V2,,TRUE)

a<-table.element(a, anova.xdf$Df[1],,FALSE)

a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE)

a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE)

a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE)

a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residuals',,TRUE)

a<-table.element(a, anova.xdf$Df[2],,FALSE)

a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE)

a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3),,FALSE)

a<-table.element(a, ' ',,FALSE)

a<-table.element(a, ' ',,FALSE)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

bitmap(file='anovaplot.png')

boxplot(Response ~ Treatment, data=xdf, xlab=V2, ylab=V1)

dev.off()

if(intercept==TRUE){

thsd<-TukeyHSD(aov.xdf)

bitmap(file='TukeyHSDPlot.png')

plot(thsd)

dev.off()

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Tukey Honest Significant Difference Comparisons', 5,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, ' ', 1, TRUE)

for(i in 1:4){

a<-table.element(a,colnames(thsd[[1]])[i], 1, TRUE)

}

a<-table.row.end(a)

for(i in 1:length(rownames(thsd[[1]]))){

a<-table.row.start(a)

a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE)

for(j in 1:4){

a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE)

}

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

}

if(intercept==FALSE){

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'TukeyHSD Message', 1,TRUE)

a<-table.row.end(a)

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Must Include Intercept to use Tukey Test ', 1, FALSE)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable2.tab')

}

library(car)

lt.lmxdf<-levene.test(lmxdf)

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Levenes Test for Homogeneity of Variance', 4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,' ', 1, TRUE)

for (i in 1:3){

a<-table.element(a,names(lt.lmxdf)[i], 1, FALSE)

}

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Group', 1, TRUE)

for (i in 1:3){

a<-table.element(a,round(lt.lmxdf[[i]][1], digits=3), 1, FALSE)

}

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,' ', 1, TRUE)

a<-table.element(a,lt.lmxdf[[1]][2], 1, FALSE)

a<-table.element(a,' ', 1, FALSE)

a<-table.element(a,' ', 1, FALSE)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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