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week9 ex2pt2

*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 11:53:32 -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/t1260644075zjjre68n8lthomi.htm/, Retrieved Sat, 12 Dec 2009 19:54:38 +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/t1260644075zjjre68n8lthomi.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 «

2 36 88 2 56 94 3 48 90 1 32 73 1 44 68 2 39 80 2 34 86 1 41 86 3 50 91 1 39 79 3 62 96 2 52 92 1 37 72 2 50 96 2 41 70 2 55 86 1 41 87 3 56 88 2 39 79 1 52 90 1 46 95 1 44 85 2 41 90 3 50 115 2 50 84 2 44 79 2 52 94 2 54 97 2 44 86 3 52 111 2 37 87 2 52 98 2 50 87 1 36 68 2 50 88 2 52 82 3 55 111 1 31 75 2 36 94 1 49 95 2 42 80 2 37 95 2 41 68 2 30 94 2 52 88 1 30 84 2 44 101 2 66 98 1 48 78 3 43 109 1 57 102 1 46 81 1 54 97 2 48 75 2 48 97 1 62 101 1 58 101 2 58 95 2 62 95 2 46 95 1 34 90 3 66 107 2 52 92 1 55 86 1 55 70 2 57 95 1 56 96 2 55 91 3 56 87 2 54 92 2 55 97 3 46 102 1 52 91 2 32 68 1 44 88 2 46 97 1 59 90 2 46 101 2 46 94 3 54 101 3 66 109 2 56 100 2 59 103 2 57 94 2 52 97 2 48 85 2 44 75 1 41 77 1 50 87 1 48 78 3 48 108 2 59 97 2 34 105 2 46 106 2 54 107 1 55 95 2 54 107 2 59 115 2 44 101 1 54 85 2 52 90 3 66 115 2 44 95 1 57 97 1 39 112 1 60 97 1 45 77 2 41 90 2 50 94 3 39 103 2 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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

ANOVA Model
MVRBIQ0 ~ MC30VRB
means	1.5	-0.5	0	0.25	0.25	0.3	0.4	0.389	0.5	0.5	0.1	-0.5	0.2	0.4	-0.5	0.667	-0.5	0.417	0.375	0.278	0.5	0.1	0.5	0.357	0.25	0.5	1.25

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
MC30VRB	26	9.983	0.384	0.84	0.688
Residuals	126	57.56	0.457

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
31-30	-0.5	-3.358	2.358	1
32-30	0	-2.214	2.214	1
34-30	0.25	-1.558	2.058	1
36-30	0.25	-1.558	2.058	1
37-30	0.3	-1.415	2.015	1
39-30	0.4	-1.112	1.912	1
41-30	0.389	-1.147	1.925	1
42-30	0.5	-2.358	3.358	1
43-30	0.5	-1.15	2.15	1
44-30	0.1	-1.412	1.612	1
45-30	-0.5	-3.358	2.358	1
46-30	0.2	-1.312	1.712	1
48-30	0.4	-1.112	1.912	1
49-30	-0.5	-3.358	2.358	1
50-30	0.667	-0.809	2.143	0.994
51-30	-0.5	-3.358	2.358	1
52-30	0.417	-1.059	1.893	1
54-30	0.375	-1.19	1.94	1
55-30	0.278	-1.258	1.814	1
56-30	0.5	-1.102	2.102	1
57-30	0.1	-1.615	1.815	1
58-30	0.5	-1.452	2.452	1
59-30	0.357	-1.245	1.959	1
60-30	0.25	-1.558	2.058	1
62-30	0.5	-1.452	2.452	1
66-30	1.25	-0.558	3.058	0.643
32-31	0.5	-2.631	3.631	1
34-31	0.75	-2.108	3.608	1
36-31	0.75	-2.108	3.608	1
37-31	0.8	-2	3.6	1
39-31	0.9	-1.781	3.581	1
41-31	0.889	-1.806	3.584	1
42-31	1	-2.615	4.615	1
43-31	1	-1.761	3.761	1
44-31	0.6	-2.081	3.281	1
45-31	0	-3.615	3.615	1
46-31	0.7	-1.981	3.381	1
48-31	0.9	-1.781	3.581	1
49-31	0	-3.615	3.615	1
50-31	1.167	-1.494	3.827	0.996
51-31	0	-3.615	3.615	1
52-31	0.917	-1.744	3.577	1
54-31	0.875	-1.836	3.586	1
55-31	0.778	-1.917	3.472	1
56-31	1	-1.733	3.733	1
57-31	0.6	-2.2	3.4	1
58-31	1	-1.952	3.952	1
59-31	0.857	-1.876	3.59	1
60-31	0.75	-2.108	3.608	1
62-31	1	-1.952	3.952	1
66-31	1.75	-1.108	4.608	0.843
34-32	0.25	-1.964	2.464	1
36-32	0.25	-1.964	2.464	1
37-32	0.3	-1.839	2.439	1
39-32	0.4	-1.58	2.38	1
41-32	0.389	-1.61	2.387	1
42-32	0.5	-2.631	3.631	1
43-32	0.5	-1.587	2.587	1
44-32	0.1	-1.88	2.08	1
45-32	-0.5	-3.631	2.631	1
46-32	0.2	-1.78	2.18	1
48-32	0.4	-1.58	2.38	1
49-32	-0.5	-3.631	2.631	1
50-32	0.667	-1.286	2.619	1
51-32	-0.5	-3.631	2.631	1
52-32	0.417	-1.536	2.369	1
54-32	0.375	-1.646	2.396	1
55-32	0.278	-1.721	2.276	1
56-32	0.5	-1.55	2.55	1
57-32	0.1	-2.039	2.239	1
58-32	0.5	-1.834	2.834	1
59-32	0.357	-1.693	2.407	1
60-32	0.25	-1.964	2.464	1
62-32	0.5	-1.834	2.834	1
66-32	1.25	-0.964	3.464	0.923
36-34	0	-1.808	1.808	1
37-34	0.05	-1.665	1.765	1
39-34	0.15	-1.362	1.662	1
41-34	0.139	-1.397	1.675	1
42-34	0.25	-2.608	3.108	1
43-34	0.25	-1.4	1.9	1
44-34	-0.15	-1.662	1.362	1
45-34	-0.75	-3.608	2.108	1
46-34	-0.05	-1.562	1.462	1
48-34	0.15	-1.362	1.662	1
49-34	-0.75	-3.608	2.108	1
50-34	0.417	-1.059	1.893	1
51-34	-0.75	-3.608	2.108	1
52-34	0.167	-1.309	1.643	1
54-34	0.125	-1.44	1.69	1
55-34	0.028	-1.508	1.564	1
56-34	0.25	-1.352	1.852	1
57-34	-0.15	-1.865	1.565	1
58-34	0.25	-1.702	2.202	1
59-34	0.107	-1.495	1.709	1
60-34	0	-1.808	1.808	1
62-34	0.25	-1.702	2.202	1
66-34	1	-0.808	2.808	0.937
37-36	0.05	-1.665	1.765	1
39-36	0.15	-1.362	1.662	1
41-36	0.139	-1.397	1.675	1
42-36	0.25	-2.608	3.108	1
43-36	0.25	-1.4	1.9	1
44-36	-0.15	-1.662	1.362	1
45-36	-0.75	-3.608	2.108	1
46-36	-0.05	-1.562	1.462	1
48-36	0.15	-1.362	1.662	1
49-36	-0.75	-3.608	2.108	1
50-36	0.417	-1.059	1.893	1
51-36	-0.75	-3.608	2.108	1
52-36	0.167	-1.309	1.643	1
54-36	0.125	-1.44	1.69	1
55-36	0.028	-1.508	1.564	1
56-36	0.25	-1.352	1.852	1
57-36	-0.15	-1.865	1.565	1
58-36	0.25	-1.702	2.202	1
59-36	0.107	-1.495	1.709	1
60-36	0	-1.808	1.808	1
62-36	0.25	-1.702	2.202	1
66-36	1	-0.808	2.808	0.937
39-37	0.1	-1.3	1.5	1
41-37	0.089	-1.337	1.515	1
42-37	0.2	-2.6	3	1
43-37	0.2	-1.348	1.748	1
44-37	-0.2	-1.6	1.2	1
45-37	-0.8	-3.6	2	1
46-37	-0.1	-1.5	1.3	1
48-37	0.1	-1.3	1.5	1
49-37	-0.8	-3.6	2	1
50-37	0.367	-0.994	1.727	1
51-37	-0.8	-3.6	2	1
52-37	0.117	-1.244	1.477	1
54-37	0.075	-1.382	1.532	1
55-37	-0.022	-1.448	1.404	1
56-37	0.2	-1.297	1.697	1
57-37	-0.2	-1.817	1.417	1
58-37	0.2	-1.667	2.067	1
59-37	0.057	-1.44	1.554	1
60-37	-0.05	-1.765	1.665	1
62-37	0.2	-1.667	2.067	1
66-37	0.95	-0.765	2.665	0.936
41-39	-0.011	-1.186	1.163	1
42-39	0.1	-2.581	2.781	1
43-39	0.1	-1.22	1.42	1
44-39	-0.3	-1.443	0.843	1
45-39	-0.9	-3.581	1.781	1
46-39	-0.2	-1.343	0.943	1
48-39	0	-1.143	1.143	1
49-39	-0.9	-3.581	1.781	1
50-39	0.267	-0.828	1.361	1
51-39	-0.9	-3.581	1.781	1
52-39	0.017	-1.078	1.111	1
54-39	-0.025	-1.238	1.188	1
55-39	-0.122	-1.297	1.052	1
56-39	0.1	-1.16	1.36	1
57-39	-0.3	-1.7	1.1	1
58-39	0.1	-1.583	1.783	1
59-39	-0.043	-1.303	1.217	1
60-39	-0.15	-1.662	1.362	1
62-39	0.1	-1.583	1.783	1
66-39	0.85	-0.662	2.362	0.926
42-41	0.111	-2.584	2.806	1
43-41	0.111	-1.236	1.458	1
44-41	-0.289	-1.463	0.886	1
45-41	-0.889	-3.584	1.806	1
46-41	-0.189	-1.363	0.986	1
48-41	0.011	-1.163	1.186	1
49-41	-0.889	-3.584	1.806	1
50-41	0.278	-0.849	1.405	1
51-41	-0.889	-3.584	1.806	1
52-41	0.028	-1.099	1.155	1
54-41	-0.014	-1.256	1.228	1
55-41	-0.111	-1.316	1.094	1
56-41	0.111	-1.177	1.399	1
57-41	-0.289	-1.715	1.137	1
58-41	0.111	-1.593	1.815	1
59-41	-0.032	-1.32	1.257	1
60-41	-0.139	-1.675	1.397	1
62-41	0.111	-1.593	1.815	1
66-41	0.861	-0.675	2.397	0.928
43-42	0	-2.761	2.761	1
44-42	-0.4	-3.081	2.281	1
45-42	-1	-4.615	2.615	1
46-42	-0.3	-2.981	2.381	1
48-42	-0.1	-2.781	2.581	1
49-42	-1	-4.615	2.615	1
50-42	0.167	-2.494	2.827	1
51-42	-1	-4.615	2.615	1
52-42	-0.083	-2.744	2.577	1
54-42	-0.125	-2.836	2.586	1
55-42	-0.222	-2.917	2.472	1
56-42	0	-2.733	2.733	1
57-42	-0.4	-3.2	2.4	1
58-42	0	-2.952	2.952	1
59-42	-0.143	-2.876	2.59	1
60-42	-0.25	-3.108	2.608	1
62-42	0	-2.952	2.952	1
66-42	0.75	-2.108	3.608	1
44-43	-0.4	-1.72	0.92	1
45-43	-1	-3.761	1.761	1
46-43	-0.3	-1.62	1.02	1
48-43	-0.1	-1.42	1.22	1
49-43	-1	-3.761	1.761	1
50-43	0.167	-1.112	1.445	1
51-43	-1	-3.761	1.761	1
52-43	-0.083	-1.362	1.195	1
54-43	-0.125	-1.506	1.256	1
55-43	-0.222	-1.57	1.125	1
56-43	0	-1.422	1.422	1
57-43	-0.4	-1.948	1.148	1
58-43	0	-1.808	1.808	1
59-43	-0.143	-1.565	1.279	1
60-43	-0.25	-1.9	1.4	1
62-43	0	-1.808	1.808	1
66-43	0.75	-0.9	2.4	0.994
45-44	-0.6	-3.281	2.081	1
46-44	0.1	-1.043	1.243	1
48-44	0.3	-0.843	1.443	1
49-44	-0.6	-3.281	2.081	1
50-44	0.567	-0.528	1.661	0.969
51-44	-0.6	-3.281	2.081	1
52-44	0.317	-0.778	1.411	1
54-44	0.275	-0.938	1.488	1
55-44	0.178	-0.997	1.352	1
56-44	0.4	-0.86	1.66	1
57-44	0	-1.4	1.4	1
58-44	0.4	-1.283	2.083	1
59-44	0.257	-1.003	1.517	1
60-44	0.15	-1.362	1.662	1
62-44	0.4	-1.283	2.083	1
66-44	1.15	-0.362	2.662	0.444
46-45	0.7	-1.981	3.381	1
48-45	0.9	-1.781	3.581	1
49-45	0	-3.615	3.615	1
50-45	1.167	-1.494	3.827	0.996
51-45	0	-3.615	3.615	1
52-45	0.917	-1.744	3.577	1
54-45	0.875	-1.836	3.586	1
55-45	0.778	-1.917	3.472	1
56-45	1	-1.733	3.733	1
57-45	0.6	-2.2	3.4	1
58-45	1	-1.952	3.952	1
59-45	0.857	-1.876	3.59	1
60-45	0.75	-2.108	3.608	1
62-45	1	-1.952	3.952	1
66-45	1.75	-1.108	4.608	0.843
48-46	0.2	-0.943	1.343	1
49-46	-0.7	-3.381	1.981	1
50-46	0.467	-0.628	1.561	0.998
51-46	-0.7	-3.381	1.981	1
52-46	0.217	-0.878	1.311	1
54-46	0.175	-1.038	1.388	1
55-46	0.078	-1.097	1.252	1
56-46	0.3	-0.96	1.56	1
57-46	-0.1	-1.5	1.3	1
58-46	0.3	-1.383	1.983	1
59-46	0.157	-1.103	1.417	1
60-46	0.05	-1.462	1.562	1
62-46	0.3	-1.383	1.983	1
66-46	1.05	-0.462	2.562	0.635
49-48	-0.9	-3.581	1.781	1
50-48	0.267	-0.828	1.361	1
51-48	-0.9	-3.581	1.781	1
52-48	0.017	-1.078	1.111	1
54-48	-0.025	-1.238	1.188	1
55-48	-0.122	-1.297	1.052	1
56-48	0.1	-1.16	1.36	1
57-48	-0.3	-1.7	1.1	1
58-48	0.1	-1.583	1.783	1
59-48	-0.043	-1.303	1.217	1
60-48	-0.15	-1.662	1.362	1
62-48	0.1	-1.583	1.783	1
66-48	0.85	-0.662	2.362	0.926
50-49	1.167	-1.494	3.827	0.996
51-49	0	-3.615	3.615	1
52-49	0.917	-1.744	3.577	1
54-49	0.875	-1.836	3.586	1
55-49	0.778	-1.917	3.472	1
56-49	1	-1.733	3.733	1
57-49	0.6	-2.2	3.4	1
58-49	1	-1.952	3.952	1
59-49	0.857	-1.876	3.59	1
60-49	0.75	-2.108	3.608	1
62-49	1	-1.952	3.952	1
66-49	1.75	-1.108	4.608	0.843
51-50	-1.167	-3.827	1.494	0.996
52-50	-0.25	-1.294	0.794	1
54-50	-0.292	-1.459	0.875	1
55-50	-0.389	-1.516	0.738	1
56-50	-0.167	-1.382	1.049	1
57-50	-0.567	-1.927	0.794	0.998
58-50	-0.167	-1.817	1.483	1
59-50	-0.31	-1.525	0.906	1
60-50	-0.417	-1.893	1.059	1
62-50	-0.167	-1.817	1.483	1
66-50	0.583	-0.893	2.059	0.999
52-51	0.917	-1.744	3.577	1
54-51	0.875	-1.836	3.586	1
55-51	0.778	-1.917	3.472	1
56-51	1	-1.733	3.733	1
57-51	0.6	-2.2	3.4	1
58-51	1	-1.952	3.952	1
59-51	0.857	-1.876	3.59	1
60-51	0.75	-2.108	3.608	1
62-51	1	-1.952	3.952	1
66-51	1.75	-1.108	4.608	0.843
54-52	-0.042	-1.209	1.125	1
55-52	-0.139	-1.266	0.988	1
56-52	0.083	-1.132	1.299	1
57-52	-0.317	-1.677	1.044	1
58-52	0.083	-1.567	1.733	1
59-52	-0.06	-1.275	1.156	1
60-52	-0.167	-1.643	1.309	1
62-52	0.083	-1.567	1.733	1
66-52	0.833	-0.643	2.309	0.923
55-54	-0.097	-1.339	1.145	1
56-54	0.125	-1.198	1.448	1
57-54	-0.275	-1.732	1.182	1
58-54	0.125	-1.606	1.856	1
59-54	-0.018	-1.341	1.305	1
60-54	-0.125	-1.69	1.44	1
62-54	0.125	-1.606	1.856	1
66-54	0.875	-0.69	2.44	0.93
56-55	0.222	-1.066	1.511	1
57-55	-0.178	-1.604	1.248	1
58-55	0.222	-1.482	1.926	1
59-55	0.079	-1.209	1.368	1
60-55	-0.028	-1.564	1.508	1
62-55	0.222	-1.482	1.926	1
66-55	0.972	-0.564	2.508	0.798
57-56	-0.4	-1.897	1.097	1
58-56	0	-1.764	1.764	1
59-56	-0.143	-1.509	1.224	1
60-56	-0.25	-1.852	1.352	1
62-56	0	-1.764	1.764	1
66-56	0.75	-0.852	2.352	0.991
58-57	0.4	-1.467	2.267	1
59-57	0.257	-1.24	1.754	1
60-57	0.15	-1.565	1.865	1
62-57	0.4	-1.467	2.267	1
66-57	1.15	-0.565	2.865	0.702
59-58	-0.143	-1.907	1.621	1
60-58	-0.25	-2.202	1.702	1
62-58	0	-2.087	2.087	1
66-58	0.75	-1.202	2.702	1
60-59	-0.107	-1.709	1.495	1
62-59	0.143	-1.621	1.907	1
66-59	0.893	-0.709	2.495	0.932
62-60	0.25	-1.702	2.202	1
66-60	1	-0.808	2.808	0.937
66-62	0.75	-1.202	2.702	1

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	26	0.549	0.961
	126

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/12/t1260644075zjjre68n8lthomi/3pq1h1260644007.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/12/t1260644075zjjre68n8lthomi/3pq1h1260644007.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/12/t1260644075zjjre68n8lthomi/4876q1260644007.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/12/t1260644075zjjre68n8lthomi/4876q1260644007.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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