Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationFri, 12 Dec 2014 12:34:38 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/12/t1418387862q5gnxjx3va2jo6u.htm/, Retrieved Thu, 16 May 2024 22:01:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266610, Retrieved Thu, 16 May 2024 22:01:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [] [2014-12-12 12:34:38] [25032714cc1544fa8afe1ed03205f94e] [Current]
Feedback Forum

Post a new message
Dataseries X:
50 0 2011
68 0 2011
62 0 2011
54 0 2011
71 0 2011
54 0 2011
65 0 2011
73 0 2011
52 0 2011
84 0 2011
42 0 2011
66 0 2011
65 0 2011
78 0 2011
73 0 2011
75 0 2011
72 1 2011
66 0 2011
70 0 2011
61 1 2011
81 0 2011
71 0 2011
69 0 2011
71 0 2011
72 0 2011
68 0 2011
70 0 2011
68 1 2011
61 1 2011
67 0 2011
76 0 2011
70 0 2011
60 0 2011
77 0 2011
72 0 2011
69 0 2011
71 0 2011
62 0 2011
70 0 2011
64 1 2011
58 0 2011
76 0 2011
52 0 2011
59 0 2011
68 0 2011
76 0 2011
65 1 2011
67 0 2011
59 0 2011
69 1 2011
76 0 2011
63 1 2011
75 1 2011
63 1 2011
60 0 2011
73 1 2011
63 0 2011
70 0 2011
75 1 2011
66 0 2011
63 1 2011
63 1 2011
64 0 2011
70 0 2011
75 0 2011
61 0 2011
60 0 2011
62 1 2011
73 0 2011
61 0 2011
66 0 2011
64 1 2011
59 0 2011
64 0 2011
60 1 2011
56 1 2011
66 0 2011
78 0 2011
53 0 2011
67 0 2011
59 1 2011
66 0 2011
68 1 2011
71 0 2011
66 1 2011
73 1 2011
72 1 2011
71 1 2011
59 1 2011
64 1 2011
66 1 2011
78 1 2011
68 1 2011
73 1 2011
62 1 2011
65 1 2011
68 1 2011
65 1 2011
60 1 2011
71 1 2011
65 1 2011
68 1 2011
64 1 2011
74 1 2011
69 1 2011
76 1 2011
68 1 2011
72 1 2011
67 1 2011
63 1 2011
59 1 2011
73 1 2011
66 1 2011
62 1 2011
69 1 2011
66 1 2011
51 0 2012
56 0 2012
67 0 2012
69 0 2012
57 1 2012
56 1 2012
55 0 2012
63 0 2012
67 0 2012
65 0 2012
47 0 2012
76 0 2012
64 0 2012
68 0 2012
64 0 2012
65 0 2012
71 1 2012
63 0 2012
60 0 2012
68 0 2012
72 0 2012
70 0 2012
61 0 2012
61 0 2012
62 0 2012
71 0 2012
71 0 2012
51 0 2012
56 1 2012
70 0 2012
73 0 2012
76 0 2012
59 0 2012
68 0 2012
48 0 2012
52 0 2012
59 0 2012
60 0 2012
59 0 2012
57 0 2012
79 0 2012
60 0 2012
60 0 2012
59 0 2012
62 1 2012
59 1 2012
61 0 2012
71 0 2012
57 1 2012
66 1 2012
63 1 2012
69 1 2012
58 0 2012
59 0 2012
48 1 2012
66 1 2012
73 1 2012
67 1 2012
61 1 2012
68 1 2012
75 1 2012
62 1 2012
69 1 2012
58 0 2012
60 0 2012
74 1 2012
55 0 2012
62 0 2012
63 1 2012
69 0 2012
58 1 2012
58 1 2012
68 0 2012
72 1 2012
62 1 2012
62 1 2012
65 1 2012
69 1 2012
66 1 2012
72 1 2012
62 1 2012
75 1 2012
58 1 2012
66 1 2012
55 1 2012
47 1 2012
72 0 2012
62 1 2012
64 1 2012
64 1 2012
19 0 2012
50 1 2012
68 0 2012
70 1 2012
79 0 2012
69 1 2012
71 0 2012
48 1 2012
66 1 2012
73 1 2012
74 1 2012
66 1 2012
71 0 2012
74 0 2012
78 1 2012
75 0 2012
53 0 2012
60 1 2012
50 0 2012
70 0 2012
69 1 2012
65 1 2012
78 0 2012
78 1 2012
59 0 2012
72 0 2012
70 0 2012
63 1 2012
63 0 2012
71 1 2012
74 0 2012
67 0 2012
66 0 2012
62 0 2012
80 1 2012
73 0 2012
67 0 2012
61 0 2012
73 1 2012
74 0 2012
32 0 2012
69 1 2012
69 0 2012
84 1 2012
64 1 2012
58 1 2012
60 0 2012
59 1 2012
78 1 2012
57 0 2012
60 0 2012
68 0 2012
68 0 2012
73 0 2012
69 0 2012
67 1 2012
60 1 2012
65 0 2012
66 1 2012
74 1 2012
81 0 2012
72 1 2012
55 1 2012
49 1 2012
74 1 2012
53 1 2012
64 1 2012
65 1 2012
57 1 2012
51 1 2012
80 1 2012
67 1 2012
70 1 2012
74 1 2012
75 1 2012
70 1 2012
69 1 2012
65 1 2012
55 0 2012
71 1 2012
65 1 2012
69 0 2014
48 0 2014
69 0 2014
68 0 2014
74 0 2014
67 0 2014
65 0 2014
63 0 2014
74 0 2014
39 0 2014
68 0 2014
69 0 2014
68 1 2014
63 0 2014
67 1 2014
70 0 2014
68 0 2014
66 1 2014
70 0 2014
78 0 2014
59 0 2014
62 0 2014
75 0 2014
74 0 2014
73 0 2014
62 0 2014
69 0 2014
65 0 2014
67 0 2014
73 0 2014
52 0 2014
61 0 2014
53 0 2014
63 0 2014
78 0 2014
65 0 2014
77 0 2014
69 0 2014
68 0 2014
76 0 2014
63 0 2014
41 0 2014
76 0 2014
67 0 2014
69 0 2014
59 1 2014
73 0 2014
72 1 2014
52 1 2014
65 1 2014
63 0 2014
78 0 2014
56 0 2014
68 1 2014
56 0 2014
64 0 2014
68 0 2014
75 0 2014
67 1 2014
55 0 2014
73 1 2014
66 0 2014
75 0 2014
77 0 2014
65 1 2014
75 1 2014
57 1 2014
61 0 2014
71 0 2014
72 0 2014
62 1 2014
66 0 2014
66 0 2014
63 0 2014
60 0 2014
64 0 2014
74 0 2014
59 1 2014
71 0 2014
69 0 2014
63 1 2014
73 1 2014
55 1 2014
77 0 2014
70 0 2014
64 1 2014
78 1 2014
60 1 2014
66 1 2014
77 0 2014
68 1 2014
78 1 2014
68 0 2014
60 1 2014
65 0 2014
64 1 2014
69 0 2014
72 1 2014
50 0 2014
72 0 2014
71 1 2014
80 1 2014
74 1 2014
64 0 2014
69 1 2014
76 0 2014
75 1 2014
79 0 2014
73 1 2014
60 1 2014
76 1 2014
55 0 2014
53 1 2014
62 0 2014
69 0 2014
78 1 2014
68 0 2014
67 1 2014
75 0 2014
59 1 2014
73 1 2014
70 1 2014
59 1 2014
64 0 2014
63 0 2014
67 0 2014
58 0 2014
71 0 2014
79 0 2014
53 0 2014
76 1 2014
66 1 2014
64 1 2014
57 0 2014
67 0 2014
72 1 2014
58 0 2014
74 0 2014
57 1 2014
62 0 2014
74 1 2014
54 0 2014
62 0 2014
66 1 2014
64 0 2014
74 1 2014
71 1 2014
66 0 2014
66 0 2014
63 0 2014
65 1 2014
70 1 2014
66 1 2014
66 0 2014
78 0 2014
77 1 2014
72 1 2014
65 1 2014
67 1 2014
72 1 2014
58 1 2014
84 0 2014
67 0 2014
84 1 2014
58 0 2014
63 1 2014
75 0 2014
55 0 2014
72 0 2014
58 1 2014
69 1 2014
54 0 2014
58 0 2014
67 0 2014
77 0 2014
80 1 2014
67 1 2014
75 1 2014
71 1 2014
72 1 2014
75 1 2014
79 1 2014
76 1 2014
72 0 2014
81 1 2014
52 0 2014
76 0 2014
60 1 2014
72 0 2014
77 0 2014
64 0 2014
67 1 2014
72 1 2014
79 1 2014
40 1 2014
71 0 2014
73 0 2014
75 0 2014
70 1 2014
66 1 2014
66 1 2014
73 1 2014
74 1 2014
58 0 2014
51 0 2014
75 0 2014
70 1 2014
50 1 2014
64 1 2014
77 1 2014




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266610&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266610&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266610&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







ANOVA Model
Response ~ Treatment_A * Treatment_B
means66.687-0.034-2.827-0.0781.551.504

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 66.687 & -0.034 & -2.827 & -0.078 & 1.55 & 1.504 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266610&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]66.687[/C][C]-0.034[/C][C]-2.827[/C][C]-0.078[/C][C]1.55[/C][C]1.504[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266610&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266610&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Model
Response ~ Treatment_A * Treatment_B
means66.687-0.034-2.827-0.0781.551.504







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A1113.705113.7051.7340.188
Treatment_B1717.729358.8655.4740.004
Treatment_A:Treatment_B151.13125.5660.390.677
Residuals49132189.50365.559

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 113.705 & 113.705 & 1.734 & 0.188 \tabularnewline
Treatment_B & 1 & 717.729 & 358.865 & 5.474 & 0.004 \tabularnewline
Treatment_A:Treatment_B & 1 & 51.131 & 25.566 & 0.39 & 0.677 \tabularnewline
Residuals & 491 & 32189.503 & 65.559 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266610&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C][/C][C]1[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]1[/C][C]113.705[/C][C]113.705[/C][C]1.734[/C][C]0.188[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]717.729[/C][C]358.865[/C][C]5.474[/C][C]0.004[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]51.131[/C][C]25.566[/C][C]0.39[/C][C]0.677[/C][/ROW]
[ROW][C]Residuals[/C][C]491[/C][C]32189.503[/C][C]65.559[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266610&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266610&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A1113.705113.7051.7340.188
Treatment_B1717.729358.8655.4740.004
Treatment_A:Treatment_B151.13125.5660.390.677
Residuals49132189.50365.559







Tukey Honest Significant Difference Comparisons
difflwruprp adj
1-00.961-0.4732.3960.188
2012-2011-2.105-4.3950.1840.079
2014-20110.579-1.6232.7810.81
2014-20122.6850.7244.6450.004
1:2011-0:2011-0.034-4.3594.2911
0:2012-0:2011-2.827-6.6510.9970.281
1:2012-0:2011-1.311-5.1452.5230.925
0:2014-0:2011-0.078-3.6483.4931
1:2014-0:20111.393-2.4225.2080.903
0:2012-1:2011-2.793-6.8631.2760.365
1:2012-1:2011-1.277-5.3562.8010.947
0:2014-1:2011-0.044-3.8763.7881
1:2014-1:20111.427-2.6345.4870.916
1:2012-0:20121.516-2.0275.0590.825
0:2014-0:20122.749-0.5076.0050.153
1:2014-0:20124.220.6987.7420.009
0:2014-1:20121.233-2.0344.5010.889
1:2014-1:20122.704-0.8296.2370.244
1:2014-0:20141.471-1.7744.7160.787

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
1-0 & 0.961 & -0.473 & 2.396 & 0.188 \tabularnewline
2012-2011 & -2.105 & -4.395 & 0.184 & 0.079 \tabularnewline
2014-2011 & 0.579 & -1.623 & 2.781 & 0.81 \tabularnewline
2014-2012 & 2.685 & 0.724 & 4.645 & 0.004 \tabularnewline
1:2011-0:2011 & -0.034 & -4.359 & 4.291 & 1 \tabularnewline
0:2012-0:2011 & -2.827 & -6.651 & 0.997 & 0.281 \tabularnewline
1:2012-0:2011 & -1.311 & -5.145 & 2.523 & 0.925 \tabularnewline
0:2014-0:2011 & -0.078 & -3.648 & 3.493 & 1 \tabularnewline
1:2014-0:2011 & 1.393 & -2.422 & 5.208 & 0.903 \tabularnewline
0:2012-1:2011 & -2.793 & -6.863 & 1.276 & 0.365 \tabularnewline
1:2012-1:2011 & -1.277 & -5.356 & 2.801 & 0.947 \tabularnewline
0:2014-1:2011 & -0.044 & -3.876 & 3.788 & 1 \tabularnewline
1:2014-1:2011 & 1.427 & -2.634 & 5.487 & 0.916 \tabularnewline
1:2012-0:2012 & 1.516 & -2.027 & 5.059 & 0.825 \tabularnewline
0:2014-0:2012 & 2.749 & -0.507 & 6.005 & 0.153 \tabularnewline
1:2014-0:2012 & 4.22 & 0.698 & 7.742 & 0.009 \tabularnewline
0:2014-1:2012 & 1.233 & -2.034 & 4.501 & 0.889 \tabularnewline
1:2014-1:2012 & 2.704 & -0.829 & 6.237 & 0.244 \tabularnewline
1:2014-0:2014 & 1.471 & -1.774 & 4.716 & 0.787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266610&T=3

[TABLE]
[ROW][C]Tukey Honest Significant Difference Comparisons[/C][/ROW]
[ROW][C] [/C][C]diff[/C][C]lwr[/C][C]upr[/C][C]p adj[/C][/ROW]
[ROW][C]1-0[/C][C]0.961[/C][C]-0.473[/C][C]2.396[/C][C]0.188[/C][/ROW]
[ROW][C]2012-2011[/C][C]-2.105[/C][C]-4.395[/C][C]0.184[/C][C]0.079[/C][/ROW]
[ROW][C]2014-2011[/C][C]0.579[/C][C]-1.623[/C][C]2.781[/C][C]0.81[/C][/ROW]
[ROW][C]2014-2012[/C][C]2.685[/C][C]0.724[/C][C]4.645[/C][C]0.004[/C][/ROW]
[ROW][C]1:2011-0:2011[/C][C]-0.034[/C][C]-4.359[/C][C]4.291[/C][C]1[/C][/ROW]
[ROW][C]0:2012-0:2011[/C][C]-2.827[/C][C]-6.651[/C][C]0.997[/C][C]0.281[/C][/ROW]
[ROW][C]1:2012-0:2011[/C][C]-1.311[/C][C]-5.145[/C][C]2.523[/C][C]0.925[/C][/ROW]
[ROW][C]0:2014-0:2011[/C][C]-0.078[/C][C]-3.648[/C][C]3.493[/C][C]1[/C][/ROW]
[ROW][C]1:2014-0:2011[/C][C]1.393[/C][C]-2.422[/C][C]5.208[/C][C]0.903[/C][/ROW]
[ROW][C]0:2012-1:2011[/C][C]-2.793[/C][C]-6.863[/C][C]1.276[/C][C]0.365[/C][/ROW]
[ROW][C]1:2012-1:2011[/C][C]-1.277[/C][C]-5.356[/C][C]2.801[/C][C]0.947[/C][/ROW]
[ROW][C]0:2014-1:2011[/C][C]-0.044[/C][C]-3.876[/C][C]3.788[/C][C]1[/C][/ROW]
[ROW][C]1:2014-1:2011[/C][C]1.427[/C][C]-2.634[/C][C]5.487[/C][C]0.916[/C][/ROW]
[ROW][C]1:2012-0:2012[/C][C]1.516[/C][C]-2.027[/C][C]5.059[/C][C]0.825[/C][/ROW]
[ROW][C]0:2014-0:2012[/C][C]2.749[/C][C]-0.507[/C][C]6.005[/C][C]0.153[/C][/ROW]
[ROW][C]1:2014-0:2012[/C][C]4.22[/C][C]0.698[/C][C]7.742[/C][C]0.009[/C][/ROW]
[ROW][C]0:2014-1:2012[/C][C]1.233[/C][C]-2.034[/C][C]4.501[/C][C]0.889[/C][/ROW]
[ROW][C]1:2014-1:2012[/C][C]2.704[/C][C]-0.829[/C][C]6.237[/C][C]0.244[/C][/ROW]
[ROW][C]1:2014-0:2014[/C][C]1.471[/C][C]-1.774[/C][C]4.716[/C][C]0.787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266610&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266610&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Tukey Honest Significant Difference Comparisons
difflwruprp adj
1-00.961-0.4732.3960.188
2012-2011-2.105-4.3950.1840.079
2014-20110.579-1.6232.7810.81
2014-20122.6850.7244.6450.004
1:2011-0:2011-0.034-4.3594.2911
0:2012-0:2011-2.827-6.6510.9970.281
1:2012-0:2011-1.311-5.1452.5230.925
0:2014-0:2011-0.078-3.6483.4931
1:2014-0:20111.393-2.4225.2080.903
0:2012-1:2011-2.793-6.8631.2760.365
1:2012-1:2011-1.277-5.3562.8010.947
0:2014-1:2011-0.044-3.8763.7881
1:2014-1:20111.427-2.6345.4870.916
1:2012-0:20121.516-2.0275.0590.825
0:2014-0:20122.749-0.5076.0050.153
1:2014-0:20124.220.6987.7420.009
0:2014-1:20121.233-2.0344.5010.889
1:2014-1:20122.704-0.8296.2370.244
1:2014-0:20141.471-1.7744.7160.787







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group52.1870.054
491

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 2.187 & 0.054 \tabularnewline
  & 491 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266610&T=4

[TABLE]
[ROW][C]Levenes Test for Homogeneity of Variance[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]Group[/C][C]5[/C][C]2.187[/C][C]0.054[/C][/ROW]
[ROW][C] [/C][C]491[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266610&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266610&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group52.1870.054
491



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
cat3 <- as.numeric(par3)
intercept<-as.logical(par4)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
f2 <- as.character(x[,cat3])
xdf<-data.frame(x1,f1, f2)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
(V3 <-dimnames(y)[[1]][cat3])
names(xdf)<-c('Response', 'Treatment_A', 'Treatment_B')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment_A * Treatment_B- 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment_A * Treatment_B, 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$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],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$coefficients[i], digits=3),,FALSE)
}
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',,TRUE)
a<-table.element(a, 'Df',,FALSE)
a<-table.element(a, 'Sum Sq',,FALSE)
a<-table.element(a, 'Mean Sq',,FALSE)
a<-table.element(a, 'F value',,FALSE)
a<-table.element(a, 'Pr(>F)',,FALSE)
a<-table.row.end(a)
for(i in 1 : length(rownames(anova.xdf))-1){
a<-table.row.start(a)
a<-table.element(a,rownames(anova.xdf)[i] ,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[i], 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$'Df'[i+1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='anovaplot.png')
boxplot(Response ~ Treatment_A + Treatment_B, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups')
dev.off()
bitmap(file='designplot.png')
xdf2 <- xdf # to preserve xdf make copy for function
names(xdf2) <- c(V1, V2, V3)
plot.design(xdf2, main='Design Plot of Group Means')
dev.off()
bitmap(file='interactionplot.png')
interaction.plot(xdf$Treatment_A, xdf$Treatment_B, xdf$Response, xlab=V2, ylab=V1, trace.label=V3, main='Possible Interactions Between Anova Groups')
dev.off()
if(intercept==TRUE){
thsd<-TukeyHSD(aov.xdf)
names(thsd) <- c(V2, V3, paste(V2, ':', V3, sep=''))
bitmap(file='TukeyHSDPlot.png')
layout(matrix(c(1,2,3,3), 2,2))
plot(thsd, las=1)
dev.off()
}
if(intercept==TRUE){
ntables<-length(names(thsd))
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(nt in 1:ntables){
for(i in 1:length(rownames(thsd[[nt]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[nt]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[nt]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
} # end nt
a<-table.end(a)
table.save(a,file='hsdtable.tab')
}#end if hsd tables
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')