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week9 ex1

*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: Chi Square Measure of Association- Free Statistics Software (Calculator)

Date of computation: Sat, 12 Dec 2009 11:11:01 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/12/t1260641627luzlx6aejebuzyd.htm/, Retrieved Sat, 12 Dec 2009 19:13:49 +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/2009/Dec/12/t1260641627luzlx6aejebuzyd.htm/},
    year = {2009},
}
@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 = {2009},
    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 88 3 56 94 3 48 90 3 32 73 1 44 68 2 39 80 3 34 86 3 41 86 2 50 91 1 39 79 1 62 96 3 52 92 3 37 72 3 50 96 1 41 70 3 55 86 3 41 87 3 56 88 2 39 79 2 52 90 1 46 95 1 44 85 3 41 90 1 50 115 1 50 84 3 44 79 1 52 94 2 54 97 3 44 86 2 52 111 2 37 87 3 52 98 1 50 87 3 36 68 3 50 88 1 52 82 2 55 111 1 31 75 1 36 94 2 49 95 1 42 80 1 37 95 3 41 68 2 30 94 2 52 88 1 30 84 1 44 101 1 66 98 2 48 78 3 43 109 3 57 102 3 46 81 3 54 97 2 48 75 2 48 97 3 62 101 3 58 101 2 58 95 2 62 95 3 46 95 2 34 90 3 66 107 3 52 92 2 55 86 2 55 70 3 57 95 3 56 96 2 55 91 1 56 87 2 54 92 2 55 97 2 46 102 2 52 91 3 32 68 2 44 88 3 46 97 1 59 90 3 46 101 3 46 94 1 54 101 1 66 109 2 56 100 1 59 103 3 57 94 1 52 97 3 48 85 2 44 75 3 41 77 1 50 87 1 48 78 2 48 108 3 59 97 3 46 106 1 54 107 2 55 95 2 54 107 3 59 115 3 44 101 1 54 85 3 52 90 3 66 115 3 44 95 2 57 97 3 39 112 3 60 97 3 45 77 3 41 90 1 50 94 3 39 103 1 43 77 3 48 98 3 etc...

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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

ANOVA Model
MOMAGE ~ MC30VRB
means	1.5	-0.5	1.5	1.167	0.5	0.9	0.9	1.25	-0.5	1	0.6	1.5	1.1	0.7	0.5	0.333	1.5	0.667	0.375	0.833	0.643	1.1	1.167	0.643	1.5	0.5	0.5

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
MC30VRB	26	19.971	0.768	1.206	0.245
Residuals	124	78.956	0.637

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
31-30	-0.5	-3.876	2.876	1
32-30	1.5	-1.115	4.115	0.91
34-30	1.167	-1.139	3.473	0.976
36-30	0.5	-1.635	2.635	1
37-30	0.9	-1.125	2.925	0.996
39-30	0.9	-0.886	2.686	0.977
41-30	1.25	-0.599	3.099	0.686
42-30	-0.5	-3.876	2.876	1
43-30	1	-0.949	2.949	0.972
44-30	0.6	-1.186	2.386	1
45-30	1.5	-1.876	4.876	0.996
46-30	1.1	-0.686	2.886	0.835
48-30	0.7	-1.086	2.486	0.999
49-30	0.5	-2.876	3.876	1
50-30	0.333	-1.41	2.076	1
51-30	1.5	-1.876	4.876	0.996
52-30	0.667	-1.076	2.41	1
54-30	0.375	-1.474	2.224	1
55-30	0.833	-0.981	2.648	0.993
56-30	0.643	-1.25	2.535	1
57-30	1.1	-0.925	3.125	0.947
58-30	1.167	-1.139	3.473	0.976
59-30	0.643	-1.25	2.535	1
60-30	1.5	-0.635	3.635	0.61
62-30	0.5	-1.806	2.806	1
66-30	0.5	-1.635	2.635	1
32-31	2	-1.698	5.698	0.95
34-31	1.667	-1.82	5.153	0.988
36-31	1	-2.376	4.376	1
37-31	1.4	-1.907	4.707	0.998
39-31	1.4	-1.767	4.567	0.996
41-31	1.75	-1.452	4.952	0.944
42-31	0	-4.27	4.27	1
43-31	1.5	-1.761	4.761	0.993
44-31	1.1	-2.067	4.267	1
45-31	2	-2.27	6.27	0.991
46-31	1.6	-1.567	4.767	0.976
48-31	1.2	-1.967	4.367	1
49-31	1	-3.27	5.27	1
50-31	0.833	-2.309	3.976	1
51-31	2	-2.27	6.27	0.991
52-31	1.167	-1.976	4.309	1
54-31	0.875	-2.327	4.077	1
55-31	1.333	-1.849	4.516	0.998
56-31	1.143	-2.085	4.371	1
57-31	1.6	-1.707	4.907	0.986
58-31	1.667	-1.82	5.153	0.988
59-31	1.143	-2.085	4.371	1
60-31	2	-1.376	5.376	0.88
62-31	1	-2.486	4.486	1
66-31	1	-2.376	4.376	1
34-32	-0.333	-3.09	2.423	1
36-32	-1	-3.615	1.615	1
37-32	-0.6	-3.126	1.926	1
39-32	-0.6	-2.939	1.739	1
41-32	-0.25	-2.637	2.137	1
42-32	-2	-5.698	1.698	0.95
43-32	-0.5	-2.965	1.965	1
44-32	-0.9	-3.239	1.439	1
45-32	0	-3.698	3.698	1
46-32	-0.4	-2.739	1.939	1
48-32	-0.8	-3.139	1.539	1
49-32	-1	-4.698	2.698	1
50-32	-1.167	-3.473	1.139	0.976
51-32	0	-3.698	3.698	1
52-32	-0.833	-3.139	1.473	1
54-32	-1.125	-3.512	1.262	0.99
55-32	-0.667	-3.027	1.694	1
56-32	-0.857	-3.278	1.564	1
57-32	-0.4	-2.926	2.126	1
58-32	-0.333	-3.09	2.423	1
59-32	-0.857	-3.278	1.564	1
60-32	0	-2.615	2.615	1
62-32	-1	-3.756	1.756	1
66-32	-1	-3.615	1.615	1
36-34	-0.667	-2.973	1.639	1
37-34	-0.267	-2.472	1.938	1
39-34	-0.267	-2.254	1.721	1
41-34	0.083	-1.961	2.127	1
42-34	-1.667	-5.153	1.82	0.988
43-34	-0.167	-2.302	1.968	1
44-34	-0.567	-2.554	1.421	1
45-34	0.333	-3.153	3.82	1
46-34	-0.067	-2.054	1.921	1
48-34	-0.467	-2.454	1.521	1
49-34	-0.667	-4.153	2.82	1
50-34	-0.833	-2.782	1.116	0.997
51-34	0.333	-3.153	3.82	1
52-34	-0.5	-2.449	1.449	1
54-34	-0.792	-2.836	1.252	0.999
55-34	-0.333	-2.346	1.679	1
56-34	-0.524	-2.607	1.56	1
57-34	-0.067	-2.272	2.138	1
58-34	0	-2.465	2.465	1
59-34	-0.524	-2.607	1.56	1
60-34	0.333	-1.973	2.639	1
62-34	-0.667	-3.132	1.799	1
66-34	-0.667	-2.973	1.639	1
37-36	0.4	-1.625	2.425	1
39-36	0.4	-1.386	2.186	1
41-36	0.75	-1.099	2.599	0.999
42-36	-1	-4.376	2.376	1
43-36	0.5	-1.449	2.449	1
44-36	0.1	-1.686	1.886	1
45-36	1	-2.376	4.376	1
46-36	0.6	-1.186	2.386	1
48-36	0.2	-1.586	1.986	1
49-36	0	-3.376	3.376	1
50-36	-0.167	-1.91	1.576	1
51-36	1	-2.376	4.376	1
52-36	0.167	-1.576	1.91	1
54-36	-0.125	-1.974	1.724	1
55-36	0.333	-1.481	2.148	1
56-36	0.143	-1.75	2.035	1
57-36	0.6	-1.425	2.625	1
58-36	0.667	-1.639	2.973	1
59-36	0.143	-1.75	2.035	1
60-36	1	-1.135	3.135	0.991
62-36	0	-2.306	2.306	1
66-36	0	-2.135	2.135	1
39-37	0	-1.654	1.654	1
41-37	0.35	-1.371	2.071	1
42-37	-1.4	-4.707	1.907	0.998
43-37	0.1	-1.728	1.928	1
44-37	-0.3	-1.954	1.354	1
45-37	0.6	-2.707	3.907	1
46-37	0.2	-1.454	1.854	1
48-37	-0.2	-1.854	1.454	1
49-37	-0.4	-3.707	2.907	1
50-37	-0.567	-2.174	1.04	1
51-37	0.6	-2.707	3.907	1
52-37	-0.233	-1.84	1.374	1
54-37	-0.525	-2.246	1.196	1
55-37	-0.067	-1.751	1.617	1
56-37	-0.257	-2.025	1.511	1
57-37	0.2	-1.71	2.11	1
58-37	0.267	-1.938	2.472	1
59-37	-0.257	-2.025	1.511	1
60-37	0.6	-1.425	2.625	1
62-37	-0.4	-2.605	1.805	1
66-37	-0.4	-2.425	1.625	1
41-39	0.35	-1.082	1.782	1
42-39	-1.4	-4.567	1.767	0.996
43-39	0.1	-1.459	1.659	1
44-39	-0.3	-1.65	1.05	1
45-39	0.6	-2.567	3.767	1
46-39	0.2	-1.15	1.55	1
48-39	-0.2	-1.55	1.15	1
49-39	-0.4	-3.567	2.767	1
50-39	-0.567	-1.859	0.726	0.996
51-39	0.6	-2.567	3.767	1
52-39	-0.233	-1.526	1.059	1
54-39	-0.525	-1.957	0.907	1
55-39	-0.067	-1.454	1.321	1
56-39	-0.257	-1.745	1.231	1
57-39	0.2	-1.454	1.854	1
58-39	0.267	-1.721	2.254	1
59-39	-0.257	-1.745	1.231	1
60-39	0.6	-1.186	2.386	1
62-39	-0.4	-2.388	1.588	1
66-39	-0.4	-2.186	1.386	1
42-41	-1.75	-4.952	1.452	0.944
43-41	-0.25	-1.881	1.381	1
44-41	-0.65	-2.082	0.782	0.994
45-41	0.25	-2.952	3.452	1
46-41	-0.15	-1.582	1.282	1
48-41	-0.55	-1.982	0.882	1
49-41	-0.75	-3.952	2.452	1
50-41	-0.917	-2.295	0.461	0.716
51-41	0.25	-2.952	3.452	1
52-41	-0.583	-1.961	0.795	0.998
54-41	-0.875	-2.385	0.635	0.901
55-41	-0.417	-1.884	1.05	1
56-41	-0.607	-2.17	0.955	0.999
57-41	-0.15	-1.871	1.571	1
58-41	-0.083	-2.127	1.961	1
59-41	-0.607	-2.17	0.955	0.999
60-41	0.25	-1.599	2.099	1
62-41	-0.75	-2.794	1.294	1
66-41	-0.75	-2.599	1.099	0.999
43-42	1.5	-1.761	4.761	0.993
44-42	1.1	-2.067	4.267	1
45-42	2	-2.27	6.27	0.991
46-42	1.6	-1.567	4.767	0.976
48-42	1.2	-1.967	4.367	1
49-42	1	-3.27	5.27	1
50-42	0.833	-2.309	3.976	1
51-42	2	-2.27	6.27	0.991
52-42	1.167	-1.976	4.309	1
54-42	0.875	-2.327	4.077	1
55-42	1.333	-1.849	4.516	0.998
56-42	1.143	-2.085	4.371	1
57-42	1.6	-1.707	4.907	0.986
58-42	1.667	-1.82	5.153	0.988
59-42	1.143	-2.085	4.371	1
60-42	2	-1.376	5.376	0.88
62-42	1	-2.486	4.486	1
66-42	1	-2.376	4.376	1
44-43	-0.4	-1.959	1.159	1
45-43	0.5	-2.761	3.761	1
46-43	0.1	-1.459	1.659	1
48-43	-0.3	-1.859	1.259	1
49-43	-0.5	-3.761	2.761	1
50-43	-0.667	-2.176	0.843	0.996
51-43	0.5	-2.761	3.761	1
52-43	-0.333	-1.843	1.176	1
54-43	-0.625	-2.256	1.006	1
55-43	-0.167	-1.758	1.425	1
56-43	-0.357	-2.037	1.323	1
57-43	0.1	-1.728	1.928	1
58-43	0.167	-1.968	2.302	1
59-43	-0.357	-2.037	1.323	1
60-43	0.5	-1.449	2.449	1
62-43	-0.5	-2.635	1.635	1
66-43	-0.5	-2.449	1.449	1
45-44	0.9	-2.267	4.067	1
46-44	0.5	-0.85	1.85	1
48-44	0.1	-1.25	1.45	1
49-44	-0.1	-3.267	3.067	1
50-44	-0.267	-1.559	1.026	1
51-44	0.9	-2.267	4.067	1
52-44	0.067	-1.226	1.359	1
54-44	-0.225	-1.657	1.207	1
55-44	0.233	-1.154	1.621	1
56-44	0.043	-1.445	1.531	1
57-44	0.5	-1.154	2.154	1
58-44	0.567	-1.421	2.554	1
59-44	0.043	-1.445	1.531	1
60-44	0.9	-0.886	2.686	0.977
62-44	-0.1	-2.088	1.888	1
66-44	-0.1	-1.886	1.686	1
46-45	-0.4	-3.567	2.767	1
48-45	-0.8	-3.967	2.367	1
49-45	-1	-5.27	3.27	1
50-45	-1.167	-4.309	1.976	1
51-45	0	-4.27	4.27	1
52-45	-0.833	-3.976	2.309	1
54-45	-1.125	-4.327	2.077	1
55-45	-0.667	-3.849	2.516	1
56-45	-0.857	-4.085	2.371	1
57-45	-0.4	-3.707	2.907	1
58-45	-0.333	-3.82	3.153	1
59-45	-0.857	-4.085	2.371	1
60-45	0	-3.376	3.376	1
62-45	-1	-4.486	2.486	1
66-45	-1	-4.376	2.376	1
48-46	-0.4	-1.75	0.95	1
49-46	-0.6	-3.767	2.567	1
50-46	-0.767	-2.059	0.526	0.879
51-46	0.4	-2.767	3.567	1
52-46	-0.433	-1.726	0.859	1
54-46	-0.725	-2.157	0.707	0.976
55-46	-0.267	-1.654	1.121	1
56-46	-0.457	-1.945	1.031	1
57-46	0	-1.654	1.654	1
58-46	0.067	-1.921	2.054	1
59-46	-0.457	-1.945	1.031	1
60-46	0.4	-1.386	2.186	1
62-46	-0.6	-2.588	1.388	1
66-46	-0.6	-2.386	1.186	1
49-48	-0.2	-3.367	2.967	1
50-48	-0.367	-1.659	0.926	1
51-48	0.8	-2.367	3.967	1
52-48	-0.033	-1.326	1.259	1
54-48	-0.325	-1.757	1.107	1
55-48	0.133	-1.254	1.521	1
56-48	-0.057	-1.545	1.431	1
57-48	0.4	-1.254	2.054	1
58-48	0.467	-1.521	2.454	1
59-48	-0.057	-1.545	1.431	1
60-48	0.8	-0.986	2.586	0.995
62-48	-0.2	-2.188	1.788	1
66-48	-0.2	-1.986	1.586	1
50-49	-0.167	-3.309	2.976	1
51-49	1	-3.27	5.27	1
52-49	0.167	-2.976	3.309	1
54-49	-0.125	-3.327	3.077	1
55-49	0.333	-2.849	3.516	1
56-49	0.143	-3.085	3.371	1
57-49	0.6	-2.707	3.907	1
58-49	0.667	-2.82	4.153	1
59-49	0.143	-3.085	3.371	1
60-49	1	-2.376	4.376	1
62-49	0	-3.486	3.486	1
66-49	0	-3.376	3.376	1
51-50	1.167	-1.976	4.309	1
52-50	0.333	-0.899	1.566	1
54-50	0.042	-1.336	1.42	1
55-50	0.5	-0.831	1.831	1
56-50	0.31	-1.126	1.745	1
57-50	0.767	-0.84	2.374	0.988
58-50	0.833	-1.116	2.782	0.997
59-50	0.31	-1.126	1.745	1
60-50	1.167	-0.576	2.91	0.705
62-50	0.167	-1.782	2.116	1
66-50	0.167	-1.576	1.91	1
52-51	-0.833	-3.976	2.309	1
54-51	-1.125	-4.327	2.077	1
55-51	-0.667	-3.849	2.516	1
56-51	-0.857	-4.085	2.371	1
57-51	-0.4	-3.707	2.907	1
58-51	-0.333	-3.82	3.153	1
59-51	-0.857	-4.085	2.371	1
60-51	0	-3.376	3.376	1
62-51	-1	-4.486	2.486	1
66-51	-1	-4.376	2.376	1
54-52	-0.292	-1.67	1.086	1
55-52	0.167	-1.165	1.498	1
56-52	-0.024	-1.46	1.412	1
57-52	0.433	-1.174	2.04	1
58-52	0.5	-1.449	2.449	1
59-52	-0.024	-1.46	1.412	1
60-52	0.833	-0.91	2.576	0.988
62-52	-0.167	-2.116	1.782	1
66-52	-0.167	-1.91	1.576	1
55-54	0.458	-1.009	1.925	1
56-54	0.268	-1.295	1.83	1
57-54	0.725	-0.996	2.446	0.998
58-54	0.792	-1.252	2.836	0.999
59-54	0.268	-1.295	1.83	1
60-54	1.125	-0.724	2.974	0.85
62-54	0.125	-1.919	2.169	1
66-54	0.125	-1.724	1.974	1
56-55	-0.19	-1.712	1.331	1
57-55	0.267	-1.417	1.951	1
58-55	0.333	-1.679	2.346	1
59-55	-0.19	-1.712	1.331	1
60-55	0.667	-1.148	2.481	1
62-55	-0.333	-2.346	1.679	1
66-55	-0.333	-2.148	1.481	1
57-56	0.457	-1.311	2.225	1
58-56	0.524	-1.56	2.607	1
59-56	0	-1.614	1.614	1
60-56	0.857	-1.035	2.75	0.994
62-56	-0.143	-2.226	1.941	1
66-56	-0.143	-2.035	1.75	1
58-57	0.067	-2.138	2.272	1
59-57	-0.457	-2.225	1.311	1
60-57	0.4	-1.625	2.425	1
62-57	-0.6	-2.805	1.605	1
66-57	-0.6	-2.625	1.425	1
59-58	-0.524	-2.607	1.56	1
60-58	0.333	-1.973	2.639	1
62-58	-0.667	-3.132	1.799	1
66-58	-0.667	-2.973	1.639	1
60-59	0.857	-1.035	2.75	0.994
62-59	-0.143	-2.226	1.941	1
66-59	-0.143	-2.035	1.75	1
62-60	-1	-3.306	1.306	0.997
66-60	-1	-3.135	1.135	0.991
66-62	0	-2.306	2.306	1

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	26	1.087	0.366
	124

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/12/t1260641627luzlx6aejebuzyd/3de891260641457.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/12/t1260641627luzlx6aejebuzyd/3de891260641457.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/12/t1260641627luzlx6aejebuzyd/4u8fw1260641457.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/12/t1260641627luzlx6aejebuzyd/4u8fw1260641457.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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