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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_twosampletests_mean.wasp
Title produced by softwarePaired and Unpaired Two Samples Tests about the Mean
Date of computationSat, 25 Dec 2010 11:18:55 +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/2010/Dec/25/t12932787200h1ifmeqoqmu3fo.htm/, Retrieved Mon, 29 Apr 2024 01:44:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115366, Retrieved Mon, 29 Apr 2024 01:44:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-11-17 12:33:27] [e45804683e9a4263debf179d74e04a01]
-   PD    [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-25 11:18:55] [82d760768aff8bf374d9817688c406af] [Current]
-   P       [Paired and Unpaired Two Samples Tests about the Mean] [test] [2010-12-26 17:25:41] [d6e648f00513dd750579ba7880c5fbf5]
-   PD        [Paired and Unpaired Two Samples Tests about the Mean] [Anova intrinsieke...] [2010-12-27 14:09:55] [d6e648f00513dd750579ba7880c5fbf5]
-    D        [Paired and Unpaired Two Samples Tests about the Mean] [Anova intrinsieke...] [2010-12-27 14:17:58] [d6e648f00513dd750579ba7880c5fbf5]
-    D        [Paired and Unpaired Two Samples Tests about the Mean] [Anova intrinsieke...] [2010-12-27 14:26:46] [d6e648f00513dd750579ba7880c5fbf5]
- RMPD        [Kendall tau Correlation Matrix] [pearson intrinsiek] [2010-12-27 15:34:42] [d6e648f00513dd750579ba7880c5fbf5]
- RMPD        [Kendall tau Correlation Matrix] [kendall intrinsiek] [2010-12-27 15:39:32] [d6e648f00513dd750579ba7880c5fbf5]
-   PD      [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-27 14:10:54] [e45804683e9a4263debf179d74e04a01]
-   PD      [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-27 14:22:38] [e45804683e9a4263debf179d74e04a01]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	1	27	5	26	49	35
4	1	36	4	25	45	34
5	1	25	4	17	54	13
2	1	27	3	37	36	35
3	2	25	3	35	36	28
5	2	44	3	15	53	32
4	1	50	4	27	46	35
4	1	41	4	36	42	36
4	1	48	5	25	41	27
4	2	43	4	30	45	29
5	2	47	2	27	47	27
4	2	41	3	33	42	28
3	1	44	2	29	45	29
4	2	47	5	30	40	28
3	2	40	3	25	45	30
3	2	46	3	23	40	25
4	1	28	3	26	42	15
3	1	56	3	24	45	33
4	2	49	4	35	47	31
2	2	25	4	39	31	37
4	2	41	4	23	46	37
3	2	26	3	32	34	34
4	1	50	5	29	43	32
4	1	47	4	26	45	21
3	1	52	2	21	42	25
3	2	37	5	35	51	32
2	2	41	3	23	44	28
4	1	45	4	21	47	22
5	2	26	4	28	47	25
4	1	NA	3	30	41	26
2	1	52	4	21	44	34
5	1	46	2	29	51	34
4	1	58	3	28	46	36
3	1	54	5	19	47	36
4	1	29	3	26	46	26
2	2	50	3	33	38	26
3	1	43	2	34	50	34
3	2	30	3	33	48	33
3	2	47	2	40	36	31
5	1	45	3	24	51	33
NA	2	48	1	35	35	22
4	2	48	3	35	49	29
4	2	26	4	32	38	24
4	1	46	5	20	47	37
2	2	NA	3	35	36	32
4	2	50	3	35	47	23
3	1	25	4	21	46	29
4	1	47	2	33	43	35
1	2	47	2	40	53	20
2	1	41	3	22	55	28
2	2	45	2	35	39	26
4	2	41	4	20	55	36
3	2	45	5	28	41	26
4	2	40	3	46	33	33
3	1	29	4	18	52	25
3	2	34	5	22	42	29
5	1	45	5	20	56	32
3	2	52	3	25	46	35
2	2	41	4	31	33	24
1	2	48	3	21	51	31
2	2	45	3	23	46	29
5	1	54	2	26	46	27
4	2	25	3	34	50	29
4	2	26	4	31	46	29
3	1	28	4	23	51	27
4	2	50	4	31	48	34
4	2	48	4	26	44	32
2	2	51	3	36	38	31
3	2	53	3	28	42	31
4	1	37	3	34	39	31
3	1	56	2	25	45	16
2	1	43	3	33	31	25
4	1	34	3	46	29	27
4	1	42	3	24	48	32
3	2	32	3	32	38	28
5	2	31	5	33	55	25
1	1	46	3	42	32	25
3	2	30	5	17	51	36
3	2	47	4	36	53	36
5	2	33	4	40	47	36
2	1	25	4	30	45	27
3	1	25	5	19	33	29
3	2	21	4	33	49	32
4	2	36	5	35	46	29
2	2	50	3	23	42	31
4	2	48	3	15	56	34
3	2	48	2	38	35	27
3	1	25	3	37	40	28
3	1	48	4	23	44	32
2	2	49	5	41	46	33
3	1	27	5	34	46	29
2	1	28	3	38	39	32
4	2	43	2	45	35	35
4	2	48	3	27	48	33
2	2	48	4	46	42	27
1	1	25	1	26	39	16
5	2	49	4	44	39	32
4	1	26	3	36	41	26
4	1	51	3	20	52	32
4	2	25	4	44	45	38
3	1	29	3	27	42	24
3	1	29	4	27	44	26
1	1	43	2	41	33	19
5	2	46	3	30	42	37
3	1	44	3	33	46	25
3	1	25	3	37	45	24
2	1	51	2	30	40	23
4	1	42	5	20	48	28
4	2	53	5	44	32	38
3	1	25	4	20	53	28
4	2	49	2	33	39	28
4	1	51	3	31	45	26
2	2	20	3	23	36	21
3	2	44	3	33	38	35
3	2	38	4	33	49	31
3	1	46	5	32	46	34
4	2	42	4	25	43	30
5	1	29	NA	22	37	30
3	2	46	4	16	48	24
3	2	49	2	36	45	27
2	2	51	3	35	32	26
3	1	38	3	25	46	30
1	1	41	1	27	20	15
4	2	47	3	32	42	28
4	2	44	3	36	45	34
4	2	47	3	51	29	29
3	2	46	3	30	51	26
5	1	44	4	20	55	31
2	2	28	3	29	50	28
2	2	47	4	26	44	33
3	2	28	4	20	41	32
3	1	41	5	40	40	33
2	2	45	4	29	47	31
1	2	46	4	32	42	37
3	1	46	4	33	40	27
5	2	22	3	32	51	19
4	2	33	3	34	43	27
4	1	41	4	24	45	31
4	2	47	5	25	41	38
3	1	25	3	41	41	22
5	2	42	3	39	37	35
3	2	47	3	21	46	35
3	2	50	3	38	38	30
3	1	55	5	28	39	41
3	1	21	3	37	45	25
4	1	NA	3	26	46	28
2	1	52	3	30	39	45
2	2	49	4	25	21	21
4	2	46	4	38	31	33
3	1	NA	4	31	35	25
3	2	45	3	31	49	29
2	2	52	3	27	40	31
3	1	NA	3	21	45	29
3	2	40	4	26	46	31
4	2	49	4	37	45	31
1	1	38	5	28	34	25
1	1	32	5	29	41	27
5	2	46	4	33	43	26
4	2	32	3	41	45	26
3	2	41	3	19	48	23
3	2	43	3	37	43	27
4	1	44	4	36	45	24
3	1	47	5	27	45	35
2	2	28	3	33	34	24
1	1	52	1	29	40	32
1	1	27	2	42	40	24
5	2	45	5	27	55	24
4	1	27	4	47	44	38
3	1	25	4	17	44	36
4	1	28	4	34	48	24
5	1	25	3	32	51	18
4	1	52	4	25	49	34
4	1	44	3	27	33	23
2	2	43	3	37	43	35
3	2	47	4	34	44	22
4	2	52	4	27	44	34
3	2	40	2	37	41	28
4	1	42	3	32	45	34
3	1	45	5	26	44	32
4	1	45	2	29	44	24
1	1	50	5	28	40	34
2	1	49	3	19	48	33
3	1	52	2	46	49	33
3	2	48	3	31	46	29
5	2	51	3	42	49	38
4	2	49	4	33	55	24
3	2	31	4	39	51	25
3	2	43	3	27	46	37
3	2	31	3	35	37	33
3	2	28	4	23	43	30
4	2	43	4	32	41	22
3	2	31	3	22	45	28
2	2	51	3	17	39	24
4	2	58	4	35	38	33
2	2	25	5	34	41	37

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115366&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115366&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115366&T=0

As an alternative you can also use a QR Code:  
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'Gwilym Jenkins' @ 72.249.127.135






Two Sample t-test (paired)
Difference: Mean1 - Mean2-0.22279792746114
t-stat-2.42257525946049
df192
p-value0.0163395217051807
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.404194087882014,-0.0414017670402657]
F-test to compare two variances
F-stat1.22396956625929
df193
p-value0.161267673840292
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.922287340162645,1.62433271486168]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (paired) \tabularnewline
Difference: Mean1 - Mean2 & -0.22279792746114 \tabularnewline
t-stat & -2.42257525946049 \tabularnewline
df & 192 \tabularnewline
p-value & 0.0163395217051807 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.404194087882014,-0.0414017670402657] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 1.22396956625929 \tabularnewline
df & 193 \tabularnewline
p-value & 0.161267673840292 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.922287340162645,1.62433271486168] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115366&T=1

[TABLE]
[ROW][C]Two Sample t-test (paired)[/C][/ROW]
[ROW][C]Difference: Mean1 - Mean2[/C][C]-0.22279792746114[/C][/ROW]
[ROW][C]t-stat[/C][C]-2.42257525946049[/C][/ROW]
[ROW][C]df[/C][C]192[/C][/ROW]
[ROW][C]p-value[/C][C]0.0163395217051807[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][-0.404194087882014,-0.0414017670402657][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]1.22396956625929[/C][/ROW]
[ROW][C]df[/C][C]193[/C][/ROW]
[ROW][C]p-value[/C][C]0.161267673840292[/C][/ROW]
[ROW][C]H0 value[/C][C]1[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][0.922287340162645,1.62433271486168][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115366&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115366&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Two Sample t-test (paired)
Difference: Mean1 - Mean2-0.22279792746114
t-stat-2.42257525946049
df192
p-value0.0163395217051807
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.404194087882014,-0.0414017670402657]
F-test to compare two variances
F-stat1.22396956625929
df193
p-value0.161267673840292
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.922287340162645,1.62433271486168]






Welch Two Sample t-test (paired)
Difference: Mean1 - Mean2-0.22279792746114
t-stat-2.42257525946049
df192
p-value0.0163395217051807
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.404194087882014,-0.0414017670402657]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (paired) \tabularnewline
Difference: Mean1 - Mean2 & -0.22279792746114 \tabularnewline
t-stat & -2.42257525946049 \tabularnewline
df & 192 \tabularnewline
p-value & 0.0163395217051807 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.404194087882014,-0.0414017670402657] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115366&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (paired)[/C][/ROW]
[ROW][C]Difference: Mean1 - Mean2[/C][C]-0.22279792746114[/C][/ROW]
[ROW][C]t-stat[/C][C]-2.42257525946049[/C][/ROW]
[ROW][C]df[/C][C]192[/C][/ROW]
[ROW][C]p-value[/C][C]0.0163395217051807[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][-0.404194087882014,-0.0414017670402657][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115366&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115366&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Welch Two Sample t-test (paired)
Difference: Mean1 - Mean2-0.22279792746114
t-stat-2.42257525946049
df192
p-value0.0163395217051807
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.404194087882014,-0.0414017670402657]






Wicoxon rank sum test with continuity correction (paired)
W3342.5
p-value0.0188234904860226
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.0927835051546392
p-value0.373950001238459
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.324742268041237
p-value2.60556654030353e-09

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (paired) \tabularnewline
W & 3342.5 \tabularnewline
p-value & 0.0188234904860226 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.0927835051546392 \tabularnewline
p-value & 0.373950001238459 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.324742268041237 \tabularnewline
p-value & 2.60556654030353e-09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115366&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (paired)[/C][/ROW]
[ROW][C]W[/C][C]3342.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.0188234904860226[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributions of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.0927835051546392[/C][/ROW]
[ROW][C]p-value[/C][C]0.373950001238459[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.324742268041237[/C][/ROW]
[ROW][C]p-value[/C][C]2.60556654030353e-09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115366&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115366&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Wicoxon rank sum test with continuity correction (paired)
W3342.5
p-value0.0188234904860226
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.0927835051546392
p-value0.373950001238459
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.324742268041237
p-value2.60556654030353e-09


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 4 ; par3 = 0.95 ; par4 = two.sided ; par5 = paired ; par6 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 4 ; par3 = 0.95 ; par4 = two.sided ; par5 = paired ; par6 = 0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #column number of first sample
par2 <- as.numeric(par2) #column number of second sample
par3 <- as.numeric(par3) #confidence (= 1 - alpha)
if (par5 == 'unpaired') paired <- FALSE else paired <- TRUE
par6 <- as.numeric(par6) #H0
z <- t(y)
if (par1 == par2) stop('Please, select two different column numbers')
if (par1 < 1) stop('Please, select a column number greater than zero for the first sample')
if (par2 < 1) stop('Please, select a column number greater than zero for the second sample')
if (par1 > length(z[1,])) stop('The column number for the first sample should be smaller')
if (par2 > length(z[1,])) stop('The column number for the second sample should be smaller')
if (par3 <= 0) stop('The confidence level should be larger than zero')
if (par3 >= 1) stop('The confidence level should be smaller than zero')
(r.t <- t.test(z[,par1],z[,par2],var.equal=TRUE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(v.t <- var.test(z[,par1],z[,par2],conf.level=par3))
(r.w <- t.test(z[,par1],z[,par2],var.equal=FALSE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(w.t <- wilcox.test(z[,par1],z[,par2],alternative=par4,paired=paired,mu=par6,conf.level=par3))
(ks.t <- ks.test(z[,par1],z[,par2],alternative=par4))
m1 <- mean(z[,par1],na.rm=T)
m2 <- mean(z[,par2],na.rm=T)
mdiff <- m1 - m2
newsam1 <- z[!is.na(z[,par1]),par1]
newsam2 <- z[,par2]+mdiff
newsam2 <- newsam2[!is.na(newsam2)]
(ks1.t <- ks.test(newsam1,newsam2,alternative=par4))
mydf <- data.frame(cbind(z[,par1],z[,par2]))
colnames(mydf) <- c('Variable 1','Variable 2')
bitmap(file='test1.png')
boxplot(mydf, notch=TRUE, ylab='value',main=main)
dev.off()
bitmap(file='test2.png')
qqnorm(z[,par1],main='Normal QQplot - Variable 1')
qqline(z[,par1])
dev.off()
bitmap(file='test3.png')
qqnorm(z[,par2],main='Normal QQplot - Variable 2')
qqline(z[,par2])
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.t$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.t$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.t$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.t$conf.int[1],',',r.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-test to compare two variances',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-stat',header=TRUE)
a<-table.element(a,v.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,v.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,v.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,v.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,v.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(v.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',v.t$conf.int[1],',',v.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Welch Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.w$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.w$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.w$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.w$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.w$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.w$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.w$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.w$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.w$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.w$conf.int[1],',',r.w$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Wicoxon rank sum test with continuity correction (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'W',header=TRUE)
a<-table.element(a,w.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,w.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,w.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,w.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributions of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks1.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks1.t$p.value)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')