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EXERCISE 1 ANOVA

*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: Tue, 30 Nov 2010 12:46:34 +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/30/t1291121152j52umgyilm77egx.htm/, Retrieved Tue, 30 Nov 2010 13:45:53 +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/30/t1291121152j52umgyilm77egx.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 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 1 34 105 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 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	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

ANOVA Model
MOMAGE ~ MC30VRB
means	1.5	-0.5	1.5	0.75	0.5	0.9	0.9	1.056	-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	18.291	0.703	1.058	0.4
Residuals	126	83.762	0.665

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
31-30	-0.5	-3.948	2.948	1
32-30	1.5	-1.171	4.171	0.927
34-30	0.75	-1.431	2.931	1
36-30	0.5	-1.681	2.681	1
37-30	0.9	-1.169	2.969	0.997
39-30	0.9	-0.924	2.724	0.982
41-30	1.056	-0.798	2.909	0.916
42-30	-0.5	-3.948	2.948	1
43-30	1	-0.991	2.991	0.978
44-30	0.6	-1.224	2.424	1
45-30	1.5	-1.948	4.948	0.997
46-30	1.1	-0.724	2.924	0.861
48-30	0.7	-1.124	2.524	1
49-30	0.5	-2.948	3.948	1
50-30	0.333	-1.447	2.114	1
51-30	1.5	-1.948	4.948	0.997
52-30	0.667	-1.114	2.447	1
54-30	0.375	-1.513	2.263	1
55-30	0.833	-1.02	2.686	0.995
56-30	0.643	-1.29	2.576	1
57-30	1.1	-0.969	3.169	0.958
58-30	1.167	-1.189	3.522	0.981
59-30	0.643	-1.29	2.576	1
60-30	1.5	-0.681	3.681	0.653
62-30	0.5	-1.855	2.855	1
66-30	0.5	-1.681	2.681	1
32-31	2	-1.777	5.777	0.96
34-31	1.25	-2.198	4.698	1
36-31	1	-2.448	4.448	1
37-31	1.4	-1.978	4.778	0.998
39-31	1.4	-1.834	4.634	0.997
41-31	1.556	-1.695	4.806	0.988
42-31	0	-4.361	4.361	1
43-31	1.5	-1.831	4.831	0.995
44-31	1.1	-2.134	4.334	1
45-31	2	-2.361	6.361	0.993
46-31	1.6	-1.634	4.834	0.982
48-31	1.2	-2.034	4.434	1
49-31	1	-3.361	5.361	1
50-31	0.833	-2.376	4.043	1
51-31	2	-2.361	6.361	0.993
52-31	1.167	-2.043	4.376	1
54-31	0.875	-2.396	4.146	1
55-31	1.333	-1.917	4.584	0.999
56-31	1.143	-2.154	4.44	1
57-31	1.6	-1.778	4.978	0.989
58-31	1.667	-1.894	5.228	0.991
59-31	1.143	-2.154	4.44	1
60-31	2	-1.448	5.448	0.901
62-31	1	-2.561	4.561	1
66-31	1	-2.448	4.448	1
34-32	-0.75	-3.421	1.921	1
36-32	-1	-3.671	1.671	1
37-32	-0.6	-3.18	1.98	1
39-32	-0.6	-2.989	1.789	1
41-32	-0.444	-2.855	1.966	1
42-32	-2	-5.777	1.777	0.96
43-32	-0.5	-3.018	2.018	1
44-32	-0.9	-3.289	1.489	1
45-32	0	-3.777	3.777	1
46-32	-0.4	-2.789	1.989	1
48-32	-0.8	-3.189	1.589	1
49-32	-1	-4.777	2.777	1
50-32	-1.167	-3.522	1.189	0.981
51-32	0	-3.777	3.777	1
52-32	-0.833	-3.189	1.522	1
54-32	-1.125	-3.563	1.313	0.993
55-32	-0.667	-3.077	1.744	1
56-32	-0.857	-3.33	1.615	1
57-32	-0.4	-2.98	2.18	1
58-32	-0.333	-3.148	2.482	1
59-32	-0.857	-3.33	1.615	1
60-32	0	-2.671	2.671	1
62-32	-1	-3.815	1.815	1
66-32	-1	-3.671	1.671	1
36-34	-0.25	-2.431	1.931	1
37-34	0.15	-1.919	2.219	1
39-34	0.15	-1.674	1.974	1
41-34	0.306	-1.548	2.159	1
42-34	-1.25	-4.698	2.198	1
43-34	0.25	-1.741	2.241	1
44-34	-0.15	-1.974	1.674	1
45-34	0.75	-2.698	4.198	1
46-34	0.35	-1.474	2.174	1
48-34	-0.05	-1.874	1.774	1
49-34	-0.25	-3.698	3.198	1
50-34	-0.417	-2.197	1.364	1
51-34	0.75	-2.698	4.198	1
52-34	-0.083	-1.864	1.697	1
54-34	-0.375	-2.263	1.513	1
55-34	0.083	-1.77	1.936	1
56-34	-0.107	-2.04	1.826	1
57-34	0.35	-1.719	2.419	1
58-34	0.417	-1.939	2.772	1
59-34	-0.107	-2.04	1.826	1
60-34	0.75	-1.431	2.931	1
62-34	-0.25	-2.605	2.105	1
66-34	-0.25	-2.431	1.931	1
37-36	0.4	-1.669	2.469	1
39-36	0.4	-1.424	2.224	1
41-36	0.556	-1.298	2.409	1
42-36	-1	-4.448	2.448	1
43-36	0.5	-1.491	2.491	1
44-36	0.1	-1.724	1.924	1
45-36	1	-2.448	4.448	1
46-36	0.6	-1.224	2.424	1
48-36	0.2	-1.624	2.024	1
49-36	0	-3.448	3.448	1
50-36	-0.167	-1.947	1.614	1
51-36	1	-2.448	4.448	1
52-36	0.167	-1.614	1.947	1
54-36	-0.125	-2.013	1.763	1
55-36	0.333	-1.52	2.186	1
56-36	0.143	-1.79	2.076	1
57-36	0.6	-1.469	2.669	1
58-36	0.667	-1.689	3.022	1
59-36	0.143	-1.79	2.076	1
60-36	1	-1.181	3.181	0.993
62-36	0	-2.355	2.355	1
66-36	0	-2.181	2.181	1
39-37	0	-1.689	1.689	1
41-37	0.156	-1.565	1.876	1
42-37	-1.4	-4.778	1.978	0.998
43-37	0.1	-1.767	1.967	1
44-37	-0.3	-1.989	1.389	1
45-37	0.6	-2.778	3.978	1
46-37	0.2	-1.489	1.889	1
48-37	-0.2	-1.889	1.489	1
49-37	-0.4	-3.778	2.978	1
50-37	-0.567	-2.208	1.075	1
51-37	0.6	-2.778	3.978	1
52-37	-0.233	-1.875	1.408	1
54-37	-0.525	-2.283	1.233	1
55-37	-0.067	-1.787	1.653	1
56-37	-0.257	-2.063	1.549	1
57-37	0.2	-1.75	2.15	1
58-37	0.267	-1.985	2.519	1
59-37	-0.257	-2.063	1.549	1
60-37	0.6	-1.469	2.669	1
62-37	-0.4	-2.652	1.852	1
66-37	-0.4	-2.469	1.669	1
41-39	0.156	-1.261	1.572	1
42-39	-1.4	-4.634	1.834	0.997
43-39	0.1	-1.492	1.692	1
44-39	-0.3	-1.679	1.079	1
45-39	0.6	-2.634	3.834	1
46-39	0.2	-1.179	1.579	1
48-39	-0.2	-1.579	1.179	1
49-39	-0.4	-3.634	2.834	1
50-39	-0.567	-1.887	0.754	0.997
51-39	0.6	-2.634	3.834	1
52-39	-0.233	-1.554	1.087	1
54-39	-0.525	-1.988	0.938	1
55-39	-0.067	-1.484	1.35	1
56-39	-0.257	-1.777	1.263	1
57-39	0.2	-1.489	1.889	1
58-39	0.267	-1.763	2.297	1
59-39	-0.257	-1.777	1.263	1
60-39	0.6	-1.224	2.424	1
62-39	-0.4	-2.43	1.63	1
66-39	-0.4	-2.224	1.424	1
42-41	-1.556	-4.806	1.695	0.988
43-41	-0.056	-1.681	1.57	1
44-41	-0.456	-1.872	0.961	1
45-41	0.444	-2.806	3.695	1
46-41	0.044	-1.372	1.461	1
48-41	-0.356	-1.772	1.061	1
49-41	-0.556	-3.806	2.695	1
50-41	-0.722	-2.082	0.638	0.959
51-41	0.444	-2.806	3.695	1
52-41	-0.389	-1.749	0.971	1
54-41	-0.681	-2.179	0.818	0.994
55-41	-0.222	-1.676	1.232	1
56-41	-0.413	-1.967	1.141	1
57-41	0.044	-1.676	1.765	1
58-41	0.111	-1.945	2.167	1
59-41	-0.413	-1.967	1.141	1
60-41	0.444	-1.409	2.298	1
62-41	-0.556	-2.611	1.5	1
66-41	-0.556	-2.409	1.298	1
43-42	1.5	-1.831	4.831	0.995
44-42	1.1	-2.134	4.334	1
45-42	2	-2.361	6.361	0.993
46-42	1.6	-1.634	4.834	0.982
48-42	1.2	-2.034	4.434	1
49-42	1	-3.361	5.361	1
50-42	0.833	-2.376	4.043	1
51-42	2	-2.361	6.361	0.993
52-42	1.167	-2.043	4.376	1
54-42	0.875	-2.396	4.146	1
55-42	1.333	-1.917	4.584	0.999
56-42	1.143	-2.154	4.44	1
57-42	1.6	-1.778	4.978	0.989
58-42	1.667	-1.894	5.228	0.991
59-42	1.143	-2.154	4.44	1
60-42	2	-1.448	5.448	0.901
62-42	1	-2.561	4.561	1
66-42	1	-2.448	4.448	1
44-43	-0.4	-1.992	1.192	1
45-43	0.5	-2.831	3.831	1
46-43	0.1	-1.492	1.692	1
48-43	-0.3	-1.892	1.292	1
49-43	-0.5	-3.831	2.831	1
50-43	-0.667	-2.209	0.875	0.997
51-43	0.5	-2.831	3.831	1
52-43	-0.333	-1.875	1.209	1
54-43	-0.625	-2.29	1.04	1
55-43	-0.167	-1.792	1.459	1
56-43	-0.357	-2.073	1.359	1
57-43	0.1	-1.767	1.967	1
58-43	0.167	-2.014	2.347	1
59-43	-0.357	-2.073	1.359	1
60-43	0.5	-1.491	2.491	1
62-43	-0.5	-2.681	1.681	1
66-43	-0.5	-2.491	1.491	1
45-44	0.9	-2.334	4.134	1
46-44	0.5	-0.879	1.879	1
48-44	0.1	-1.279	1.479	1
49-44	-0.1	-3.334	3.134	1
50-44	-0.267	-1.587	1.054	1
51-44	0.9	-2.334	4.134	1
52-44	0.067	-1.254	1.387	1
54-44	-0.225	-1.688	1.238	1
55-44	0.233	-1.184	1.65	1
56-44	0.043	-1.477	1.563	1
57-44	0.5	-1.189	2.189	1
58-44	0.567	-1.463	2.597	1
59-44	0.043	-1.477	1.563	1
60-44	0.9	-0.924	2.724	0.982
62-44	-0.1	-2.13	1.93	1
66-44	-0.1	-1.924	1.724	1
46-45	-0.4	-3.634	2.834	1
48-45	-0.8	-4.034	2.434	1
49-45	-1	-5.361	3.361	1
50-45	-1.167	-4.376	2.043	1
51-45	0	-4.361	4.361	1
52-45	-0.833	-4.043	2.376	1
54-45	-1.125	-4.396	2.146	1
55-45	-0.667	-3.917	2.584	1
56-45	-0.857	-4.154	2.44	1
57-45	-0.4	-3.778	2.978	1
58-45	-0.333	-3.894	3.228	1
59-45	-0.857	-4.154	2.44	1
60-45	0	-3.448	3.448	1
62-45	-1	-4.561	2.561	1
66-45	-1	-4.448	2.448	1
48-46	-0.4	-1.779	0.979	1
49-46	-0.6	-3.834	2.634	1
50-46	-0.767	-2.087	0.554	0.9
51-46	0.4	-2.834	3.634	1
52-46	-0.433	-1.754	0.887	1
54-46	-0.725	-2.188	0.738	0.981
55-46	-0.267	-1.684	1.15	1
56-46	-0.457	-1.977	1.063	1
57-46	0	-1.689	1.689	1
58-46	0.067	-1.963	2.097	1
59-46	-0.457	-1.977	1.063	1
60-46	0.4	-1.424	2.224	1
62-46	-0.6	-2.63	1.43	1
66-46	-0.6	-2.424	1.224	1
49-48	-0.2	-3.434	3.034	1
50-48	-0.367	-1.687	0.954	1
51-48	0.8	-2.434	4.034	1
52-48	-0.033	-1.354	1.287	1
54-48	-0.325	-1.788	1.138	1
55-48	0.133	-1.284	1.55	1
56-48	-0.057	-1.577	1.463	1
57-48	0.4	-1.289	2.089	1
58-48	0.467	-1.563	2.497	1
59-48	-0.057	-1.577	1.463	1
60-48	0.8	-1.024	2.624	0.996
62-48	-0.2	-2.23	1.83	1
66-48	-0.2	-2.024	1.624	1
50-49	-0.167	-3.376	3.043	1
51-49	1	-3.361	5.361	1
52-49	0.167	-3.043	3.376	1
54-49	-0.125	-3.396	3.146	1
55-49	0.333	-2.917	3.584	1
56-49	0.143	-3.154	3.44	1
57-49	0.6	-2.778	3.978	1
58-49	0.667	-2.894	4.228	1
59-49	0.143	-3.154	3.44	1
60-49	1	-2.448	4.448	1
62-49	0	-3.561	3.561	1
66-49	0	-3.448	3.448	1
51-50	1.167	-2.043	4.376	1
52-50	0.333	-0.926	1.592	1
54-50	0.042	-1.366	1.449	1
55-50	0.5	-0.86	1.86	1
56-50	0.31	-1.157	1.776	1
57-50	0.767	-0.875	2.408	0.991
58-50	0.833	-1.157	2.824	0.998
59-50	0.31	-1.157	1.776	1
60-50	1.167	-0.614	2.947	0.743
62-50	0.167	-1.824	2.157	1
66-50	0.167	-1.614	1.947	1
52-51	-0.833	-4.043	2.376	1
54-51	-1.125	-4.396	2.146	1
55-51	-0.667	-3.917	2.584	1
56-51	-0.857	-4.154	2.44	1
57-51	-0.4	-3.778	2.978	1
58-51	-0.333	-3.894	3.228	1
59-51	-0.857	-4.154	2.44	1
60-51	0	-3.448	3.448	1
62-51	-1	-4.561	2.561	1
66-51	-1	-4.448	2.448	1
54-52	-0.292	-1.699	1.116	1
55-52	0.167	-1.193	1.527	1
56-52	-0.024	-1.49	1.443	1
57-52	0.433	-1.208	2.075	1
58-52	0.5	-1.491	2.491	1
59-52	-0.024	-1.49	1.443	1
60-52	0.833	-0.947	2.614	0.991
62-52	-0.167	-2.157	1.824	1
66-52	-0.167	-1.947	1.614	1
55-54	0.458	-1.04	1.957	1
56-54	0.268	-1.328	1.864	1
57-54	0.725	-1.033	2.483	0.999
58-54	0.792	-1.296	2.879	1
59-54	0.268	-1.328	1.864	1
60-54	1.125	-0.763	3.013	0.875
62-54	0.125	-1.963	2.213	1
66-54	0.125	-1.763	2.013	1
56-55	-0.19	-1.745	1.364	1
57-55	0.267	-1.453	1.987	1
58-55	0.333	-1.723	2.389	1
59-55	-0.19	-1.745	1.364	1
60-55	0.667	-1.186	2.52	1
62-55	-0.333	-2.389	1.723	1
66-55	-0.333	-2.186	1.52	1
57-56	0.457	-1.349	2.263	1
58-56	0.524	-1.604	2.652	1
59-56	0	-1.648	1.648	1
60-56	0.857	-1.076	2.79	0.996
62-56	-0.143	-2.271	1.985	1
66-56	-0.143	-2.076	1.79	1
58-57	0.067	-2.185	2.319	1
59-57	-0.457	-2.263	1.349	1
60-57	0.4	-1.669	2.469	1
62-57	-0.6	-2.852	1.652	1
66-57	-0.6	-2.669	1.469	1
59-58	-0.524	-2.652	1.604	1
60-58	0.333	-2.022	2.689	1
62-58	-0.667	-3.185	1.851	1
66-58	-0.667	-3.022	1.689	1
60-59	0.857	-1.076	2.79	0.996
62-59	-0.143	-2.271	1.985	1
66-59	-0.143	-2.076	1.79	1
62-60	-1	-3.355	1.355	0.998
66-60	-1	-3.181	1.181	0.993
66-62	0	-2.355	2.355	1

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	26	0.965	0.52
	126

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291121152j52umgyilm77egx/3ddje1291121186.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291121152j52umgyilm77egx/3ddje1291121186.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291121152j52umgyilm77egx/4ddje1291121186.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291121152j52umgyilm77egx/4ddje1291121186.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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