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*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: Tue, 15 Dec 2009 05:24:40 -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/15/t1260879956ditzwkry7mznulm.htm/, Retrieved Tue, 15 Dec 2009 13:26:02 +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/15/t1260879956ditzwkry7mznulm.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 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 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 1 44 2 66 3 48 2 43 2 57 1 46 3 54 3 48 2 48 1 62 3 58 2 58 2 62 2 46 1 34 2 66 3 52 2 55 1 55 3 57 1 56 2 55 3 56 1 54 3 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 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 3 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 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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

ANOVA Model
RespVar ~ AgeGroup
means	2	0	0	0	-0.5	0.2	-0.7	0	-1	-0.167	-0.4	0	0	0	-1	0.417	-1	0.167	0.25	0.111	0.286	0.2	0.333	0	1	0	0.5

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
AgeGroup	26	19.893	0.765	1.53	0.064
Residuals	124	62.001	0.5

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
31-30	0	-2.991	2.991	1
32-30	0	-2.317	2.317	1
34-30	0	-2.043	2.043	1
36-30	-0.5	-2.392	1.392	1
37-30	0.2	-1.595	1.995	1
39-30	-0.7	-2.283	0.883	0.996
41-30	0	-1.638	1.638	1
42-30	-1	-3.991	1.991	1
43-30	-0.167	-1.894	1.56	1
44-30	-0.4	-1.983	1.183	1
45-30	0	-2.991	2.991	1
46-30	0	-1.583	1.583	1
48-30	0	-1.583	1.583	1
49-30	-1	-3.991	1.991	1
50-30	0.417	-1.128	1.961	1
51-30	-1	-3.991	1.991	1
52-30	0.167	-1.378	1.711	1
54-30	0.25	-1.388	1.888	1
55-30	0.111	-1.497	1.719	1
56-30	0.286	-1.391	1.963	1
57-30	0.2	-1.595	1.995	1
58-30	0.333	-1.71	2.377	1
59-30	0	-1.677	1.677	1
60-30	1	-0.892	2.892	0.96
62-30	0	-2.043	2.043	1
66-30	0.5	-1.392	2.392	1
32-31	0	-3.277	3.277	1
34-31	0	-3.089	3.089	1
36-31	-0.5	-3.491	2.491	1
37-31	0.2	-2.731	3.131	1
39-31	-0.7	-3.506	2.106	1
41-31	0	-2.838	2.838	1
42-31	-1	-4.784	2.784	1
43-31	-0.167	-3.057	2.723	1
44-31	-0.4	-3.206	2.406	1
45-31	0	-3.784	3.784	1
46-31	0	-2.806	2.806	1
48-31	0	-2.806	2.806	1
49-31	-1	-4.784	2.784	1
50-31	0.417	-2.368	3.201	1
51-31	-1	-4.784	2.784	1
52-31	0.167	-2.618	2.951	1
54-31	0.25	-2.588	3.088	1
55-31	0.111	-2.709	2.931	1
56-31	0.286	-2.574	3.146	1
57-31	0.2	-2.731	3.131	1
58-31	0.333	-2.756	3.423	1
59-31	0	-2.86	2.86	1
60-31	1	-1.991	3.991	1
62-31	0	-3.089	3.089	1
66-31	0.5	-2.491	3.491	1
34-32	0	-2.442	2.442	1
36-32	-0.5	-2.817	1.817	1
37-32	0.2	-2.038	2.438	1
39-32	-0.7	-2.772	1.372	1
41-32	0	-2.115	2.115	1
42-32	-1	-4.277	2.277	1
43-32	-0.167	-2.351	2.018	1
44-32	-0.4	-2.472	1.672	1
45-32	0	-3.277	3.277	1
46-32	0	-2.072	2.072	1
48-32	0	-2.072	2.072	1
49-32	-1	-4.277	2.277	1
50-32	0.417	-1.627	2.46	1
51-32	-1	-4.277	2.277	1
52-32	0.167	-1.877	2.21	1
54-32	0.25	-1.865	2.365	1
55-32	0.111	-1.98	2.203	1
56-32	0.286	-1.859	2.431	1
57-32	0.2	-2.038	2.438	1
58-32	0.333	-2.109	2.776	1
59-32	0	-2.145	2.145	1
60-32	1	-1.317	3.317	0.997
62-32	0	-2.442	2.442	1
66-32	0.5	-1.817	2.817	1
36-34	-0.5	-2.543	1.543	1
37-34	0.2	-1.754	2.154	1
39-34	-0.7	-2.461	1.061	0.999
41-34	0	-1.811	1.811	1
42-34	-1	-4.089	2.089	1
43-34	-0.167	-2.059	1.725	1
44-34	-0.4	-2.161	1.361	1
45-34	0	-3.089	3.089	1
46-34	0	-1.761	1.761	1
48-34	0	-1.761	1.761	1
49-34	-1	-4.089	2.089	1
50-34	0.417	-1.31	2.144	1
51-34	-1	-4.089	2.089	1
52-34	0.167	-1.56	1.894	1
54-34	0.25	-1.561	2.061	1
55-34	0.111	-1.673	1.895	1
56-34	0.286	-1.561	2.132	1
57-34	0.2	-1.754	2.154	1
58-34	0.333	-1.851	2.518	1
59-34	0	-1.846	1.846	1
60-34	1	-1.043	3.043	0.984
62-34	0	-2.185	2.185	1
66-34	0.5	-1.543	2.543	1
37-36	0.7	-1.095	2.495	0.999
39-36	-0.2	-1.783	1.383	1
41-36	0.5	-1.138	2.138	1
42-36	-0.5	-3.491	2.491	1
43-36	0.333	-1.394	2.06	1
44-36	0.1	-1.483	1.683	1
45-36	0.5	-2.491	3.491	1
46-36	0.5	-1.083	2.083	1
48-36	0.5	-1.083	2.083	1
49-36	-0.5	-3.491	2.491	1
50-36	0.917	-0.628	2.461	0.878
51-36	-0.5	-3.491	2.491	1
52-36	0.667	-0.878	2.211	0.997
54-36	0.75	-0.888	2.388	0.993
55-36	0.611	-0.997	2.219	1
56-36	0.786	-0.891	2.463	0.991
57-36	0.7	-1.095	2.495	0.999
58-36	0.833	-1.21	2.877	0.999
59-36	0.5	-1.177	2.177	1
60-36	1.5	-0.392	3.392	0.356
62-36	0.5	-1.543	2.543	1
66-36	1	-0.892	2.892	0.96
39-37	-0.9	-2.365	0.565	0.838
41-37	-0.2	-1.725	1.325	1
42-37	-1.2	-4.131	1.731	0.999
43-37	-0.367	-1.987	1.253	1
44-37	-0.6	-2.065	0.865	0.999
45-37	-0.2	-3.131	2.731	1
46-37	-0.2	-1.665	1.265	1
48-37	-0.2	-1.665	1.265	1
49-37	-1.2	-4.131	1.731	0.999
50-37	0.217	-1.207	1.641	1
51-37	-1.2	-4.131	1.731	0.999
52-37	-0.033	-1.457	1.391	1
54-37	0.05	-1.475	1.575	1
55-37	-0.089	-1.581	1.403	1
56-37	0.086	-1.481	1.652	1
57-37	0	-1.692	1.692	1
58-37	0.133	-1.821	2.087	1
59-37	-0.2	-1.767	1.367	1
60-37	0.8	-0.995	2.595	0.995
62-37	-0.2	-2.154	1.754	1
66-37	0.3	-1.495	2.095	1
41-39	0.7	-0.569	1.969	0.938
42-39	-0.3	-3.106	2.506	1
43-39	0.533	-0.848	1.915	0.999
44-39	0.3	-0.897	1.497	1
45-39	0.7	-2.106	3.506	1
46-39	0.7	-0.497	1.897	0.892
48-39	0.7	-0.497	1.897	0.892
49-39	-0.3	-3.106	2.506	1
50-39	1.117	-0.029	2.262	0.067
51-39	-0.3	-3.106	2.506	1
52-39	0.867	-0.279	2.012	0.454
54-39	0.95	-0.319	2.219	0.477
55-39	0.811	-0.418	2.04	0.73
56-39	0.986	-0.333	2.304	0.479
57-39	0.9	-0.565	2.365	0.838
58-39	1.033	-0.728	2.795	0.89
59-39	0.7	-0.618	2.018	0.959
60-39	1.7	0.117	3.283	0.02
62-39	0.7	-1.061	2.461	0.999
66-39	1.2	-0.383	2.783	0.45
42-41	-1	-3.838	1.838	1
43-41	-0.167	-1.612	1.278	1
44-41	-0.4	-1.669	0.869	1
45-41	0	-2.838	2.838	1
46-41	0	-1.269	1.269	1
48-41	0	-1.269	1.269	1
49-41	-1	-3.838	1.838	1
50-41	0.417	-0.805	1.638	1
51-41	-1	-3.838	1.838	1
52-41	0.167	-1.055	1.388	1
54-41	0.25	-1.088	1.588	1
55-41	0.111	-1.189	1.411	1
56-41	0.286	-1.099	1.67	1
57-41	0.2	-1.325	1.725	1
58-41	0.333	-1.478	2.145	1
59-41	0	-1.385	1.385	1
60-41	1	-0.638	2.638	0.846
62-41	0	-1.811	1.811	1
66-41	0.5	-1.138	2.138	1
43-42	0.833	-2.057	3.723	1
44-42	0.6	-2.206	3.406	1
45-42	1	-2.784	4.784	1
46-42	1	-1.806	3.806	1
48-42	1	-1.806	3.806	1
49-42	0	-3.784	3.784	1
50-42	1.417	-1.368	4.201	0.974
51-42	0	-3.784	3.784	1
52-42	1.167	-1.618	3.951	0.998
54-42	1.25	-1.588	4.088	0.996
55-42	1.111	-1.709	3.931	0.999
56-42	1.286	-1.574	4.146	0.995
57-42	1.2	-1.731	4.131	0.999
58-42	1.333	-1.756	4.423	0.997
59-42	1	-1.86	3.86	1
60-42	2	-0.991	4.991	0.706
62-42	1	-2.089	4.089	1
66-42	1.5	-1.491	4.491	0.978
44-43	-0.233	-1.615	1.148	1
45-43	0.167	-2.723	3.057	1
46-43	0.167	-1.215	1.548	1
48-43	0.167	-1.215	1.548	1
49-43	-0.833	-3.723	2.057	1
50-43	0.583	-0.754	1.921	0.997
51-43	-0.833	-3.723	2.057	1
52-43	0.333	-1.004	1.671	1
54-43	0.417	-1.028	1.862	1
55-43	0.278	-1.132	1.688	1
56-43	0.452	-1.036	1.941	1
57-43	0.367	-1.253	1.987	1
58-43	0.5	-1.392	2.392	1
59-43	0.167	-1.322	1.655	1
60-43	1.167	-0.56	2.894	0.687
62-43	0.167	-1.725	2.059	1
66-43	0.667	-1.06	2.394	0.999
45-44	0.4	-2.406	3.206	1
46-44	0.4	-0.797	1.597	1
48-44	0.4	-0.797	1.597	1
49-44	-0.6	-3.406	2.206	1
50-44	0.817	-0.329	1.962	0.58
51-44	-0.6	-3.406	2.206	1
52-44	0.567	-0.579	1.712	0.982
54-44	0.65	-0.619	1.919	0.972
55-44	0.511	-0.718	1.74	0.998
56-44	0.686	-0.633	2.004	0.967
57-44	0.6	-0.865	2.065	0.999
58-44	0.733	-1.028	2.495	0.998
59-44	0.4	-0.918	1.718	1
60-44	1.4	-0.183	2.983	0.167
62-44	0.4	-1.361	2.161	1
66-44	0.9	-0.683	2.483	0.917
46-45	0	-2.806	2.806	1
48-45	0	-2.806	2.806	1
49-45	-1	-4.784	2.784	1
50-45	0.417	-2.368	3.201	1
51-45	-1	-4.784	2.784	1
52-45	0.167	-2.618	2.951	1
54-45	0.25	-2.588	3.088	1
55-45	0.111	-2.709	2.931	1
56-45	0.286	-2.574	3.146	1
57-45	0.2	-2.731	3.131	1
58-45	0.333	-2.756	3.423	1
59-45	0	-2.86	2.86	1
60-45	1	-1.991	3.991	1
62-45	0	-3.089	3.089	1
66-45	0.5	-2.491	3.491	1
48-46	0	-1.197	1.197	1
49-46	-1	-3.806	1.806	1
50-46	0.417	-0.729	1.562	1
51-46	-1	-3.806	1.806	1
52-46	0.167	-0.979	1.312	1
54-46	0.25	-1.019	1.519	1
55-46	0.111	-1.118	1.34	1
56-46	0.286	-1.033	1.604	1
57-46	0.2	-1.265	1.665	1
58-46	0.333	-1.428	2.095	1
59-46	0	-1.318	1.318	1
60-46	1	-0.583	2.583	0.8
62-46	0	-1.761	1.761	1
66-46	0.5	-1.083	2.083	1
49-48	-1	-3.806	1.806	1
50-48	0.417	-0.729	1.562	1
51-48	-1	-3.806	1.806	1
52-48	0.167	-0.979	1.312	1
54-48	0.25	-1.019	1.519	1
55-48	0.111	-1.118	1.34	1
56-48	0.286	-1.033	1.604	1
57-48	0.2	-1.265	1.665	1
58-48	0.333	-1.428	2.095	1
59-48	0	-1.318	1.318	1
60-48	1	-0.583	2.583	0.8
62-48	0	-1.761	1.761	1
66-48	0.5	-1.083	2.083	1
50-49	1.417	-1.368	4.201	0.974
51-49	0	-3.784	3.784	1
52-49	1.167	-1.618	3.951	0.998
54-49	1.25	-1.588	4.088	0.996
55-49	1.111	-1.709	3.931	0.999
56-49	1.286	-1.574	4.146	0.995
57-49	1.2	-1.731	4.131	0.999
58-49	1.333	-1.756	4.423	0.997
59-49	1	-1.86	3.86	1
60-49	2	-0.991	4.991	0.706
62-49	1	-2.089	4.089	1
66-49	1.5	-1.491	4.491	0.978
51-50	-1.417	-4.201	1.368	0.974
52-50	-0.25	-1.342	0.842	1
54-50	-0.167	-1.388	1.055	1
55-50	-0.306	-1.485	0.874	1
56-50	-0.131	-1.403	1.141	1
57-50	-0.217	-1.641	1.207	1
58-50	-0.083	-1.81	1.644	1
59-50	-0.417	-1.689	0.856	1
60-50	0.583	-0.961	2.128	1
62-50	-0.417	-2.144	1.31	1
66-50	0.083	-1.461	1.628	1
52-51	1.167	-1.618	3.951	0.998
54-51	1.25	-1.588	4.088	0.996
55-51	1.111	-1.709	3.931	0.999
56-51	1.286	-1.574	4.146	0.995
57-51	1.2	-1.731	4.131	0.999
58-51	1.333	-1.756	4.423	0.997
59-51	1	-1.86	3.86	1
60-51	2	-0.991	4.991	0.706
62-51	1	-2.089	4.089	1
66-51	1.5	-1.491	4.491	0.978
54-52	0.083	-1.138	1.305	1
55-52	-0.056	-1.235	1.124	1
56-52	0.119	-1.153	1.391	1
57-52	0.033	-1.391	1.457	1
58-52	0.167	-1.56	1.894	1
59-52	-0.167	-1.439	1.106	1
60-52	0.833	-0.711	2.378	0.951
62-52	-0.167	-1.894	1.56	1
66-52	0.333	-1.211	1.878	1
55-54	-0.139	-1.439	1.161	1
56-54	0.036	-1.349	1.42	1
57-54	-0.05	-1.575	1.475	1
58-54	0.083	-1.728	1.895	1
59-54	-0.25	-1.635	1.135	1
60-54	0.75	-0.888	2.388	0.993
62-54	-0.25	-2.061	1.561	1
66-54	0.25	-1.388	1.888	1
56-55	0.175	-1.174	1.523	1
57-55	0.089	-1.403	1.581	1
58-55	0.222	-1.561	2.006	1
59-55	-0.111	-1.459	1.237	1
60-55	0.889	-0.719	2.497	0.937
62-55	-0.111	-1.895	1.673	1
66-55	0.389	-1.219	1.997	1
57-56	-0.086	-1.652	1.481	1
58-56	0.048	-1.799	1.894	1
59-56	-0.286	-1.716	1.144	1
60-56	0.714	-0.963	2.391	0.998
62-56	-0.286	-2.132	1.561	1
66-56	0.214	-1.463	1.891	1
58-57	0.133	-1.821	2.087	1
59-57	-0.2	-1.767	1.367	1
60-57	0.8	-0.995	2.595	0.995
62-57	-0.2	-2.154	1.754	1
66-57	0.3	-1.495	2.095	1
59-58	-0.333	-2.18	1.513	1
60-58	0.667	-1.377	2.71	1
62-58	-0.333	-2.518	1.851	1
66-58	0.167	-1.877	2.21	1
60-59	1	-0.677	2.677	0.873
62-59	0	-1.846	1.846	1
66-59	0.5	-1.177	2.177	1
62-60	-1	-3.043	1.043	0.984
66-60	-0.5	-2.392	1.392	1
66-62	0.5	-1.543	2.543	1

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	26	1.22	0.233
	124

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/15/t1260879956ditzwkry7mznulm/3gx3j1260879876.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/15/t1260879956ditzwkry7mznulm/3gx3j1260879876.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/15/t1260879956ditzwkry7mznulm/47xi61260879876.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/15/t1260879956ditzwkry7mznulm/47xi61260879876.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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