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week 9 Q2 Part 2

*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: Thu, 03 Dec 2009 11:38:09 -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/03/t1259869868cuvknshmtr1nydk.htm/, Retrieved Thu, 03 Dec 2009 20:51:10 +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/2009/Dec/03/t1259869868cuvknshmtr1nydk.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 «

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

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	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

ANOVA Model
childsviq30 ~ Maternalwarmth
means	45.778	2.152	5.222	-43.278	-43.778	-44.778	-44.778	-43.635	-43.278	-43.578	-43.778	-42.778	-33.444	-43.528	-44.178	-43.778	-43.778	-43.778	-43.444	-43.778	-43.778	-43.778	-43.778	-43.778	-42.778

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
Maternalwarmth	24	79518.2	3313.258	61.277	0
Residuals	134	7245.398	54.07

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
2-1	2.152	-4.594	8.899	1
3-1	5.222	-2.255	12.7	0.611
30-1	-43.278	-63.412	-23.144	0
34-1	-43.778	-71.757	-15.799	0
36-1	-44.778	-64.912	-24.644	0
37-1	-44.778	-64.912	-24.644	0
39-1	-43.635	-55.288	-31.982	0
41-1	-43.278	-63.412	-23.144	0
43-1	-43.578	-56.954	-30.201	0
44-1	-43.778	-60.498	-27.057	0
45-1	-42.778	-70.757	-14.799	0
46-1	-33.444	-50.165	-16.724	0
48-1	-43.528	-58.248	-28.808	0
50-1	-44.178	-57.554	-30.801	0
51-1	-43.778	-71.757	-15.799	0
52-1	-43.778	-63.912	-23.644	0
54-1	-43.778	-58.498	-29.058	0
55-1	-43.444	-60.165	-26.724	0
56-1	-43.778	-60.498	-27.057	0
57-1	-43.778	-63.912	-23.644	0
58-1	-43.778	-71.757	-15.799	0
59-1	-43.778	-58.498	-29.058	0
60-1	-43.778	-58.498	-29.058	0
66-1	-42.778	-70.757	-14.799	0
3-2	3.07	-3.677	9.816	0.992
30-2	-45.43	-65.304	-25.556	0
34-2	-45.93	-73.723	-18.138	0
36-2	-46.93	-66.804	-27.056	0
37-2	-46.93	-66.804	-27.056	0
39-2	-45.787	-56.985	-34.59	0
41-2	-45.43	-65.304	-25.556	0
43-2	-45.73	-58.712	-32.748	0
44-2	-45.93	-62.337	-29.524	0
45-2	-44.93	-72.723	-17.138	0
46-2	-35.597	-52.003	-19.19	0
48-2	-45.68	-60.042	-31.318	0
50-2	-46.33	-59.312	-33.348	0
51-2	-45.93	-73.723	-18.138	0
52-2	-45.93	-65.804	-26.056	0
54-2	-45.93	-60.292	-31.568	0
55-2	-45.597	-62.003	-29.19	0
56-2	-45.93	-62.337	-29.524	0
57-2	-45.93	-65.804	-26.056	0
58-2	-45.93	-73.723	-18.138	0
59-2	-45.93	-60.292	-31.568	0
60-2	-45.93	-60.292	-31.568	0
66-2	-44.93	-72.723	-17.138	0
30-3	-48.5	-68.634	-28.366	0
34-3	-49	-76.979	-21.021	0
36-3	-50	-70.134	-29.866	0
37-3	-50	-70.134	-29.866	0
39-3	-48.857	-60.51	-37.204	0
41-3	-48.5	-68.634	-28.366	0
43-3	-48.8	-62.176	-35.424	0
44-3	-49	-65.721	-32.279	0
45-3	-48	-75.979	-20.021	0
46-3	-38.667	-55.387	-21.946	0
48-3	-48.75	-63.47	-34.03	0
50-3	-49.4	-62.776	-36.024	0
51-3	-49	-76.979	-21.021	0
52-3	-49	-69.134	-28.866	0
54-3	-49	-63.72	-34.28	0
55-3	-48.667	-65.387	-31.946	0
56-3	-49	-65.721	-32.279	0
57-3	-49	-69.134	-28.866	0
58-3	-49	-76.979	-21.021	0
59-3	-49	-63.72	-34.28	0
60-3	-49	-63.72	-34.28	0
66-3	-48	-75.979	-20.021	0
34-30	-0.5	-34.15	33.15	1
36-30	-1.5	-28.975	25.975	1
37-30	-1.5	-28.975	25.975	1
39-30	-0.357	-22.386	21.672	1
41-30	0	-27.475	27.475	1
43-30	-0.3	-23.287	22.687	1
44-30	-0.5	-25.581	24.581	1
45-30	0.5	-33.15	34.15	1
46-30	9.833	-15.248	34.914	0.999
48-30	-0.25	-24.044	23.544	1
50-30	-0.9	-23.887	22.087	1
51-30	-0.5	-34.15	33.15	1
52-30	-0.5	-27.975	26.975	1
54-30	-0.5	-24.294	23.294	1
55-30	-0.167	-25.248	24.914	1
56-30	-0.5	-25.581	24.581	1
57-30	-0.5	-27.975	26.975	1
58-30	-0.5	-34.15	33.15	1
59-30	-0.5	-24.294	23.294	1
60-30	-0.5	-24.294	23.294	1
66-30	0.5	-33.15	34.15	1
36-34	-1	-34.65	32.65	1
37-34	-1	-34.65	32.65	1
39-34	0.143	-29.229	29.515	1
41-34	0.5	-33.15	34.15	1
43-34	0.2	-29.897	30.297	1
44-34	0	-31.725	31.725	1
45-34	1	-37.855	39.855	1
46-34	10.333	-21.392	42.058	1
48-34	0.25	-30.468	30.968	1
50-34	-0.4	-30.497	29.697	1
51-34	0	-38.855	38.855	1
52-34	0	-33.65	33.65	1
54-34	0	-30.718	30.718	1
55-34	0.333	-31.392	32.058	1
56-34	0	-31.725	31.725	1
57-34	0	-33.65	33.65	1
58-34	0	-38.855	38.855	1
59-34	0	-30.718	30.718	1
60-34	0	-30.718	30.718	1
66-34	1	-37.855	39.855	1
37-36	0	-27.475	27.475	1
39-36	1.143	-20.886	23.172	1
41-36	1.5	-25.975	28.975	1
43-36	1.2	-21.787	24.187	1
44-36	1	-24.081	26.081	1
45-36	2	-31.65	35.65	1
46-36	11.333	-13.748	36.414	0.993
48-36	1.25	-22.544	25.044	1
50-36	0.6	-22.387	23.587	1
51-36	1	-32.65	34.65	1
52-36	1	-26.475	28.475	1
54-36	1	-22.794	24.794	1
55-36	1.333	-23.748	26.414	1
56-36	1	-24.081	26.081	1
57-36	1	-26.475	28.475	1
58-36	1	-32.65	34.65	1
59-36	1	-22.794	24.794	1
60-36	1	-22.794	24.794	1
66-36	2	-31.65	35.65	1
39-37	1.143	-20.886	23.172	1
41-37	1.5	-25.975	28.975	1
43-37	1.2	-21.787	24.187	1
44-37	1	-24.081	26.081	1
45-37	2	-31.65	35.65	1
46-37	11.333	-13.748	36.414	0.993
48-37	1.25	-22.544	25.044	1
50-37	0.6	-22.387	23.587	1
51-37	1	-32.65	34.65	1
52-37	1	-26.475	28.475	1
54-37	1	-22.794	24.794	1
55-37	1.333	-23.748	26.414	1
56-37	1	-24.081	26.081	1
57-37	1	-26.475	28.475	1
58-37	1	-32.65	34.65	1
59-37	1	-22.794	24.794	1
60-37	1	-22.794	24.794	1
66-37	2	-31.65	35.65	1
41-39	0.357	-21.672	22.386	1
43-39	0.057	-16.03	16.145	1
44-39	-0.143	-19.102	18.817	1
45-39	0.857	-28.515	30.229	1
46-39	10.19	-8.769	29.15	0.946
48-39	0.107	-17.114	17.328	1
50-39	-0.543	-16.63	15.545	1
51-39	-0.143	-29.515	29.229	1
52-39	-0.143	-22.172	21.886	1
54-39	-0.143	-17.364	17.078	1
55-39	0.19	-18.769	19.15	1
56-39	-0.143	-19.102	18.817	1
57-39	-0.143	-22.172	21.886	1
58-39	-0.143	-29.515	29.229	1
59-39	-0.143	-17.364	17.078	1
60-39	-0.143	-17.364	17.078	1
66-39	0.857	-28.515	30.229	1
43-41	-0.3	-23.287	22.687	1
44-41	-0.5	-25.581	24.581	1
45-41	0.5	-33.15	34.15	1
46-41	9.833	-15.248	34.914	0.999
48-41	-0.25	-24.044	23.544	1
50-41	-0.9	-23.887	22.087	1
51-41	-0.5	-34.15	33.15	1
52-41	-0.5	-27.975	26.975	1
54-41	-0.5	-24.294	23.294	1
55-41	-0.167	-25.248	24.914	1
56-41	-0.5	-25.581	24.581	1
57-41	-0.5	-27.975	26.975	1
58-41	-0.5	-34.15	33.15	1
59-41	-0.5	-24.294	23.294	1
60-41	-0.5	-24.294	23.294	1
66-41	0.5	-33.15	34.15	1
44-43	-0.2	-20.265	19.865	1
45-43	0.8	-29.297	30.897	1
46-43	10.133	-9.931	30.198	0.972
48-43	0.05	-18.381	18.481	1
50-43	-0.6	-17.977	16.777	1
51-43	-0.2	-30.297	29.897	1
52-43	-0.2	-23.187	22.787	1
54-43	-0.2	-18.631	18.231	1
55-43	0.133	-19.931	20.198	1
56-43	-0.2	-20.265	19.865	1
57-43	-0.2	-23.187	22.787	1
58-43	-0.2	-30.297	29.897	1
59-43	-0.2	-18.631	18.231	1
60-43	-0.2	-18.631	18.231	1
66-43	0.8	-29.297	30.897	1
45-44	1	-30.725	32.725	1
46-44	10.333	-12.1	32.766	0.991
48-44	0.25	-20.734	21.234	1
50-44	-0.4	-20.465	19.665	1
51-44	0	-31.725	31.725	1
52-44	0	-25.081	25.081	1
54-44	0	-20.984	20.984	1
55-44	0.333	-22.1	22.766	1
56-44	0	-22.433	22.433	1
57-44	0	-25.081	25.081	1
58-44	0	-31.725	31.725	1
59-44	0	-20.984	20.984	1
60-44	0	-20.984	20.984	1
66-44	1	-30.725	32.725	1
46-45	9.333	-22.392	41.058	1
48-45	-0.75	-31.468	29.968	1
50-45	-1.4	-31.497	28.697	1
51-45	-1	-39.855	37.855	1
52-45	-1	-34.65	32.65	1
54-45	-1	-31.718	29.718	1
55-45	-0.667	-32.392	31.058	1
56-45	-1	-32.725	30.725	1
57-45	-1	-34.65	32.65	1
58-45	-1	-39.855	37.855	1
59-45	-1	-31.718	29.718	1
60-45	-1	-31.718	29.718	1
66-45	0	-38.855	38.855	1
48-46	-10.083	-31.068	10.901	0.984
50-46	-10.733	-30.798	9.331	0.949
51-46	-10.333	-42.058	21.392	1
52-46	-10.333	-35.414	14.748	0.998
54-46	-10.333	-31.318	10.651	0.979
55-46	-10	-32.433	12.433	0.994
56-46	-10.333	-32.766	12.1	0.991
57-46	-10.333	-35.414	14.748	0.998
58-46	-10.333	-42.058	21.392	1
59-46	-10.333	-31.318	10.651	0.979
60-46	-10.333	-31.318	10.651	0.979
66-46	-9.333	-41.058	22.392	1
50-48	-0.65	-19.081	17.781	1
51-48	-0.25	-30.968	30.468	1
52-48	-0.25	-24.044	23.544	1
54-48	-0.25	-19.678	19.178	1
55-48	0.083	-20.901	21.068	1
56-48	-0.25	-21.234	20.734	1
57-48	-0.25	-24.044	23.544	1
58-48	-0.25	-30.968	30.468	1
59-48	-0.25	-19.678	19.178	1
60-48	-0.25	-19.678	19.178	1
66-48	0.75	-29.968	31.468	1
51-50	0.4	-29.697	30.497	1
52-50	0.4	-22.587	23.387	1
54-50	0.4	-18.031	18.831	1
55-50	0.733	-19.331	20.798	1
56-50	0.4	-19.665	20.465	1
57-50	0.4	-22.587	23.387	1
58-50	0.4	-29.697	30.497	1
59-50	0.4	-18.031	18.831	1
60-50	0.4	-18.031	18.831	1
66-50	1.4	-28.697	31.497	1
52-51	0	-33.65	33.65	1
54-51	0	-30.718	30.718	1
55-51	0.333	-31.392	32.058	1
56-51	0	-31.725	31.725	1
57-51	0	-33.65	33.65	1
58-51	0	-38.855	38.855	1
59-51	0	-30.718	30.718	1
60-51	0	-30.718	30.718	1
66-51	1	-37.855	39.855	1
54-52	0	-23.794	23.794	1
55-52	0.333	-24.748	25.414	1
56-52	0	-25.081	25.081	1
57-52	0	-27.475	27.475	1
58-52	0	-33.65	33.65	1
59-52	0	-23.794	23.794	1
60-52	0	-23.794	23.794	1
66-52	1	-32.65	34.65	1
55-54	0.333	-20.651	21.318	1
56-54	0	-20.984	20.984	1
57-54	0	-23.794	23.794	1
58-54	0	-30.718	30.718	1
59-54	0	-19.428	19.428	1
60-54	0	-19.428	19.428	1
66-54	1	-29.718	31.718	1
56-55	-0.333	-22.766	22.1	1
57-55	-0.333	-25.414	24.748	1
58-55	-0.333	-32.058	31.392	1
59-55	-0.333	-21.318	20.651	1
60-55	-0.333	-21.318	20.651	1
66-55	0.667	-31.058	32.392	1
57-56	0	-25.081	25.081	1
58-56	0	-31.725	31.725	1
59-56	0	-20.984	20.984	1
60-56	0	-20.984	20.984	1
66-56	1	-30.725	32.725	1
58-57	0	-33.65	33.65	1
59-57	0	-23.794	23.794	1
60-57	0	-23.794	23.794	1
66-57	1	-32.65	34.65	1
59-58	0	-30.718	30.718	1
60-58	0	-30.718	30.718	1
66-58	1	-37.855	39.855	1
60-59	0	-19.428	19.428	1
66-59	1	-29.718	31.718	1
66-60	1	-29.718	31.718	1

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	24	3.072	0
	134

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259869868cuvknshmtr1nydk/3ld6g1259865480.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259869868cuvknshmtr1nydk/3ld6g1259865480.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259869868cuvknshmtr1nydk/41jdi1259865480.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259869868cuvknshmtr1nydk/41jdi1259865480.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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