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Author*The author of this computation has been verified*
R Software ModulePatrick.Wessarwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationTue, 14 Dec 2010 18:51:12 +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/14/t12923528323ju3erq2rysj6fl.htm/, Retrieved Thu, 02 May 2024 18:43:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110025, Retrieved Thu, 02 May 2024 18:43:22 +0000
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Original text written by user:
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Estimated Impact111
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
-   PD    [Kendall tau Correlation Matrix] [Pearson Correlati...] [2010-12-14 18:51:12] [c6b3e187a4a1689d42fffda4bc860ab5] [Current]
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Dataseries X:
6000	10800	10100	16100	17700	13900	17700
6000	10900	10000	15800	17700	13500	19800
6000	11000	10000	16900	17700	13900	19400
6000	11000	10000	17800	17700	13700	18500
6000	11100	10600	17600	17400	13800	18400
6000	11000	12200	18300	17800	15100	18200
6000	11000	12400	18000	17800	15100	18300
6000	11100	13400	15700	17800	14500	19100
6100	11100	13000	14500	17800	13000	18500
6100	11100	10500	14000	18100	12900	18100
6100	11100	10000	15500	18400	14400	18300
6100	11100	10000	15800	18000	14600	17900
6100	11200	10100	15800	17800	15000	18000
6100	11100	10200	15900	17600	13900	18200
6200	11100	10600	18000	17400	14800	18800
6200	11200	10900	19900	17200	15200	20100
6200	11200	10900	20600	17300	16800	19700
6300	11100	11500	20600	17700	17400	19200
6300	11200	12500	20800	18100	17200	19800
6300	11100	13700	20000	18300	17400	20200
6300	11100	15100	18500	18700	18300	19000
6300	11000	13500	17700	18900	19900	19400
6300	11000	13200	17000	18200	18500	19600
6400	11000	13000	16600	17900	16800	18400
6300	11100	13900	16700	17900	16200	18700
6300	11000	14000	17300	18200	16200	18400
6300	11000	13900	19100	18200	16400	20700
6300	10900	14200	20200	18100	15900	20800
6300	11000	14400	20700	18100	16300	21400
6300	11000	14400	21500	17800	16800	21500
6400	11100	14500	21000	18000	15900	20500
6400	11300	13900	16800	17900	15400	20500
6400	11300	14800	16800	18300	15100	19500
6500	11300	13200	16500	18200	15000	20200
6500	11300	12900	17200	18000	17100	20200
6500	11400	13100	17300	18200	16000	18800
6500	11400	12700	17600	18400	15500	19600
6500	11400	13800	18400	18200	16300	19300
6500	11500	13800	19900	18100	16400	20300
6500	11500	14500	20500	17900	16800	21000
6500	11500	15000	21200	18700	17200	19500
6500	11500	16300	21300	18900	17600	20700
6600	11500	17300	20800	19200	18400	20900
6600	11500	18400	18800	19000	18900	20100
6600	11400	17500	18100	19100	18600	19200
6500	11400	13400	18100	19500	18100	19900
6500	11400	13600	18800	20400	18300	21100
6500	11300	13300	18700	19900	17200	20000
6500	11200	13700	18700	19400	15900	20900
6500	11300	13900	19000	19300	16600	20400
6500	11300	14000	20100	18900	15900	20900
6500	11300	14000	20500	18700	16000	20900
6600	11200	14300	21600	18900	15600	21300
6700	11300	15200	21800	19000	16000	21300
6600	11200	15400	21500	19300	16200	21700
6700	11200	18500	21200	19400	16000	21300
6600	11100	18300	20400	18800	16000	20000
6600	11100	12900	20400	18900	16800	20500
6600	11100	12000	20600	19200	17700	20800
6600	11100	12000	19300	19100	17500	20700
7100	11400	12100	18600	18900	17600	21200
7400	11500	12100	19400	18900	18900	21300
7500	11500	11900	23500	19800	18800	21600
7500	11600	11800	24600	20200	19000	22500
7500	11500	11700	25900	20200	19100	22600
7500	11600	12200	26600	19900	19100	23900
7000	11300	12500	24100	19700	18400	23600
6900	11300	13000	21800	19600	16900	22600
6900	11200	13300	21300	19500	16100	22600
6800	11200	11800	21100	19800	16700	22700
6800	11100	11800	21200	20000	18400	22900
6800	11100	11900	21600	20000	18400	22100




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110025&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]2 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=110025&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110025&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=pearson)
MineraalwaterVruchtesappenAppelenSinaasappelenCitroenenPompelmoezenBananen
Mineraalwater10.6790.1870.7450.7750.6920.789
Vruchtesappen0.67910.3090.4480.4980.4950.434
Appelen0.1870.30910.240.2780.3570.227
Sinaasappelen0.7450.4480.2410.6070.6240.83
Citroenen0.7750.4980.2780.60710.6630.724
Pompelmoezen0.6920.4950.3570.6240.66310.572
Bananen0.7890.4340.2270.830.7240.5721

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Mineraalwater & Vruchtesappen & Appelen & Sinaasappelen & Citroenen & Pompelmoezen & Bananen \tabularnewline
Mineraalwater & 1 & 0.679 & 0.187 & 0.745 & 0.775 & 0.692 & 0.789 \tabularnewline
Vruchtesappen & 0.679 & 1 & 0.309 & 0.448 & 0.498 & 0.495 & 0.434 \tabularnewline
Appelen & 0.187 & 0.309 & 1 & 0.24 & 0.278 & 0.357 & 0.227 \tabularnewline
Sinaasappelen & 0.745 & 0.448 & 0.24 & 1 & 0.607 & 0.624 & 0.83 \tabularnewline
Citroenen & 0.775 & 0.498 & 0.278 & 0.607 & 1 & 0.663 & 0.724 \tabularnewline
Pompelmoezen & 0.692 & 0.495 & 0.357 & 0.624 & 0.663 & 1 & 0.572 \tabularnewline
Bananen & 0.789 & 0.434 & 0.227 & 0.83 & 0.724 & 0.572 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110025&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Mineraalwater[/C][C]Vruchtesappen[/C][C]Appelen[/C][C]Sinaasappelen[/C][C]Citroenen[/C][C]Pompelmoezen[/C][C]Bananen[/C][/ROW]
[ROW][C]Mineraalwater[/C][C]1[/C][C]0.679[/C][C]0.187[/C][C]0.745[/C][C]0.775[/C][C]0.692[/C][C]0.789[/C][/ROW]
[ROW][C]Vruchtesappen[/C][C]0.679[/C][C]1[/C][C]0.309[/C][C]0.448[/C][C]0.498[/C][C]0.495[/C][C]0.434[/C][/ROW]
[ROW][C]Appelen[/C][C]0.187[/C][C]0.309[/C][C]1[/C][C]0.24[/C][C]0.278[/C][C]0.357[/C][C]0.227[/C][/ROW]
[ROW][C]Sinaasappelen[/C][C]0.745[/C][C]0.448[/C][C]0.24[/C][C]1[/C][C]0.607[/C][C]0.624[/C][C]0.83[/C][/ROW]
[ROW][C]Citroenen[/C][C]0.775[/C][C]0.498[/C][C]0.278[/C][C]0.607[/C][C]1[/C][C]0.663[/C][C]0.724[/C][/ROW]
[ROW][C]Pompelmoezen[/C][C]0.692[/C][C]0.495[/C][C]0.357[/C][C]0.624[/C][C]0.663[/C][C]1[/C][C]0.572[/C][/ROW]
[ROW][C]Bananen[/C][C]0.789[/C][C]0.434[/C][C]0.227[/C][C]0.83[/C][C]0.724[/C][C]0.572[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110025&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110025&T=1

As an alternative you can also use a QR Code:  

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

Correlations for all pairs of data series (method=pearson)
MineraalwaterVruchtesappenAppelenSinaasappelenCitroenenPompelmoezenBananen
Mineraalwater10.6790.1870.7450.7750.6920.789
Vruchtesappen0.67910.3090.4480.4980.4950.434
Appelen0.1870.30910.240.2780.3570.227
Sinaasappelen0.7450.4480.2410.6070.6240.83
Citroenen0.7750.4980.2780.60710.6630.724
Pompelmoezen0.6920.4950.3570.6240.66310.572
Bananen0.7890.4340.2270.830.7240.5721







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Mineraalwater;Vruchtesappen0.67860.6640.5224
p-value(0)(0)(0)
Mineraalwater;Appelen0.18720.29510.2113
p-value(0.1153)(0.0119)(0.013)
Mineraalwater;Sinaasappelen0.74460.69910.5421
p-value(0)(0)(0)
Mineraalwater;Citroenen0.77460.84910.6896
p-value(0)(0)(0)
Mineraalwater;Pompelmoezen0.69190.67170.5246
p-value(0)(0)(0)
Mineraalwater;Bananen0.78910.79850.6379
p-value(0)(0)(0)
Vruchtesappen;Appelen0.30880.25710.1866
p-value(0.0083)(0.0293)(0.031)
Vruchtesappen;Sinaasappelen0.44820.37740.2832
p-value(1e-04)(0.0011)(0.001)
Vruchtesappen;Citroenen0.49770.51680.3769
p-value(0)(0)(0)
Vruchtesappen;Pompelmoezen0.4950.44570.3478
p-value(0)(1e-04)(1e-04)
Vruchtesappen;Bananen0.43450.42110.3029
p-value(1e-04)(2e-04)(5e-04)
Appelen;Sinaasappelen0.24030.28440.2045
p-value(0.042)(0.0155)(0.012)
Appelen;Citroenen0.27780.27770.1705
p-value(0.0181)(0.0182)(0.0388)
Appelen;Pompelmoezen0.35690.24150.1478
p-value(0.0021)(0.041)(0.0709)
Appelen;Bananen0.22660.26780.1912
p-value(0.0556)(0.0229)(0.0192)
Sinaasappelen;Citroenen0.60690.5790.4195
p-value(0)(0)(0)
Sinaasappelen;Pompelmoezen0.62360.57460.4202
p-value(0)(0)(0)
Sinaasappelen;Bananen0.82960.82290.6318
p-value(0)(0)(0)
Citroenen;Pompelmoezen0.66290.65670.4839
p-value(0)(0)(0)
Citroenen;Bananen0.72370.69220.5096
p-value(0)(0)(0)
Pompelmoezen;Bananen0.57150.53470.386
p-value(0)(0)(0)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Mineraalwater;Vruchtesappen & 0.6786 & 0.664 & 0.5224 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Mineraalwater;Appelen & 0.1872 & 0.2951 & 0.2113 \tabularnewline
p-value & (0.1153) & (0.0119) & (0.013) \tabularnewline
Mineraalwater;Sinaasappelen & 0.7446 & 0.6991 & 0.5421 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Mineraalwater;Citroenen & 0.7746 & 0.8491 & 0.6896 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Mineraalwater;Pompelmoezen & 0.6919 & 0.6717 & 0.5246 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Mineraalwater;Bananen & 0.7891 & 0.7985 & 0.6379 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Vruchtesappen;Appelen & 0.3088 & 0.2571 & 0.1866 \tabularnewline
p-value & (0.0083) & (0.0293) & (0.031) \tabularnewline
Vruchtesappen;Sinaasappelen & 0.4482 & 0.3774 & 0.2832 \tabularnewline
p-value & (1e-04) & (0.0011) & (0.001) \tabularnewline
Vruchtesappen;Citroenen & 0.4977 & 0.5168 & 0.3769 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Vruchtesappen;Pompelmoezen & 0.495 & 0.4457 & 0.3478 \tabularnewline
p-value & (0) & (1e-04) & (1e-04) \tabularnewline
Vruchtesappen;Bananen & 0.4345 & 0.4211 & 0.3029 \tabularnewline
p-value & (1e-04) & (2e-04) & (5e-04) \tabularnewline
Appelen;Sinaasappelen & 0.2403 & 0.2844 & 0.2045 \tabularnewline
p-value & (0.042) & (0.0155) & (0.012) \tabularnewline
Appelen;Citroenen & 0.2778 & 0.2777 & 0.1705 \tabularnewline
p-value & (0.0181) & (0.0182) & (0.0388) \tabularnewline
Appelen;Pompelmoezen & 0.3569 & 0.2415 & 0.1478 \tabularnewline
p-value & (0.0021) & (0.041) & (0.0709) \tabularnewline
Appelen;Bananen & 0.2266 & 0.2678 & 0.1912 \tabularnewline
p-value & (0.0556) & (0.0229) & (0.0192) \tabularnewline
Sinaasappelen;Citroenen & 0.6069 & 0.579 & 0.4195 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Sinaasappelen;Pompelmoezen & 0.6236 & 0.5746 & 0.4202 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Sinaasappelen;Bananen & 0.8296 & 0.8229 & 0.6318 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Citroenen;Pompelmoezen & 0.6629 & 0.6567 & 0.4839 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Citroenen;Bananen & 0.7237 & 0.6922 & 0.5096 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Pompelmoezen;Bananen & 0.5715 & 0.5347 & 0.386 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110025&T=2

[TABLE]
[ROW][C]Correlations for all pairs of data series with p-values[/C][/ROW]
[ROW][C]pair[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]Mineraalwater;Vruchtesappen[/C][C]0.6786[/C][C]0.664[/C][C]0.5224[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Mineraalwater;Appelen[/C][C]0.1872[/C][C]0.2951[/C][C]0.2113[/C][/ROW]
[ROW][C]p-value[/C][C](0.1153)[/C][C](0.0119)[/C][C](0.013)[/C][/ROW]
[ROW][C]Mineraalwater;Sinaasappelen[/C][C]0.7446[/C][C]0.6991[/C][C]0.5421[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Mineraalwater;Citroenen[/C][C]0.7746[/C][C]0.8491[/C][C]0.6896[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Mineraalwater;Pompelmoezen[/C][C]0.6919[/C][C]0.6717[/C][C]0.5246[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Mineraalwater;Bananen[/C][C]0.7891[/C][C]0.7985[/C][C]0.6379[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Vruchtesappen;Appelen[/C][C]0.3088[/C][C]0.2571[/C][C]0.1866[/C][/ROW]
[ROW][C]p-value[/C][C](0.0083)[/C][C](0.0293)[/C][C](0.031)[/C][/ROW]
[ROW][C]Vruchtesappen;Sinaasappelen[/C][C]0.4482[/C][C]0.3774[/C][C]0.2832[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0.0011)[/C][C](0.001)[/C][/ROW]
[ROW][C]Vruchtesappen;Citroenen[/C][C]0.4977[/C][C]0.5168[/C][C]0.3769[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Vruchtesappen;Pompelmoezen[/C][C]0.495[/C][C]0.4457[/C][C]0.3478[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]Vruchtesappen;Bananen[/C][C]0.4345[/C][C]0.4211[/C][C]0.3029[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](2e-04)[/C][C](5e-04)[/C][/ROW]
[ROW][C]Appelen;Sinaasappelen[/C][C]0.2403[/C][C]0.2844[/C][C]0.2045[/C][/ROW]
[ROW][C]p-value[/C][C](0.042)[/C][C](0.0155)[/C][C](0.012)[/C][/ROW]
[ROW][C]Appelen;Citroenen[/C][C]0.2778[/C][C]0.2777[/C][C]0.1705[/C][/ROW]
[ROW][C]p-value[/C][C](0.0181)[/C][C](0.0182)[/C][C](0.0388)[/C][/ROW]
[ROW][C]Appelen;Pompelmoezen[/C][C]0.3569[/C][C]0.2415[/C][C]0.1478[/C][/ROW]
[ROW][C]p-value[/C][C](0.0021)[/C][C](0.041)[/C][C](0.0709)[/C][/ROW]
[ROW][C]Appelen;Bananen[/C][C]0.2266[/C][C]0.2678[/C][C]0.1912[/C][/ROW]
[ROW][C]p-value[/C][C](0.0556)[/C][C](0.0229)[/C][C](0.0192)[/C][/ROW]
[ROW][C]Sinaasappelen;Citroenen[/C][C]0.6069[/C][C]0.579[/C][C]0.4195[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Sinaasappelen;Pompelmoezen[/C][C]0.6236[/C][C]0.5746[/C][C]0.4202[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Sinaasappelen;Bananen[/C][C]0.8296[/C][C]0.8229[/C][C]0.6318[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Citroenen;Pompelmoezen[/C][C]0.6629[/C][C]0.6567[/C][C]0.4839[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Citroenen;Bananen[/C][C]0.7237[/C][C]0.6922[/C][C]0.5096[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Pompelmoezen;Bananen[/C][C]0.5715[/C][C]0.5347[/C][C]0.386[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110025&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110025&T=2

As an alternative you can also use a QR Code:  

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

Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Mineraalwater;Vruchtesappen0.67860.6640.5224
p-value(0)(0)(0)
Mineraalwater;Appelen0.18720.29510.2113
p-value(0.1153)(0.0119)(0.013)
Mineraalwater;Sinaasappelen0.74460.69910.5421
p-value(0)(0)(0)
Mineraalwater;Citroenen0.77460.84910.6896
p-value(0)(0)(0)
Mineraalwater;Pompelmoezen0.69190.67170.5246
p-value(0)(0)(0)
Mineraalwater;Bananen0.78910.79850.6379
p-value(0)(0)(0)
Vruchtesappen;Appelen0.30880.25710.1866
p-value(0.0083)(0.0293)(0.031)
Vruchtesappen;Sinaasappelen0.44820.37740.2832
p-value(1e-04)(0.0011)(0.001)
Vruchtesappen;Citroenen0.49770.51680.3769
p-value(0)(0)(0)
Vruchtesappen;Pompelmoezen0.4950.44570.3478
p-value(0)(1e-04)(1e-04)
Vruchtesappen;Bananen0.43450.42110.3029
p-value(1e-04)(2e-04)(5e-04)
Appelen;Sinaasappelen0.24030.28440.2045
p-value(0.042)(0.0155)(0.012)
Appelen;Citroenen0.27780.27770.1705
p-value(0.0181)(0.0182)(0.0388)
Appelen;Pompelmoezen0.35690.24150.1478
p-value(0.0021)(0.041)(0.0709)
Appelen;Bananen0.22660.26780.1912
p-value(0.0556)(0.0229)(0.0192)
Sinaasappelen;Citroenen0.60690.5790.4195
p-value(0)(0)(0)
Sinaasappelen;Pompelmoezen0.62360.57460.4202
p-value(0)(0)(0)
Sinaasappelen;Bananen0.82960.82290.6318
p-value(0)(0)(0)
Citroenen;Pompelmoezen0.66290.65670.4839
p-value(0)(0)(0)
Citroenen;Bananen0.72370.69220.5096
p-value(0)(0)(0)
Pompelmoezen;Bananen0.57150.53470.386
p-value(0)(0)(0)



Parameters (Session):
par1 = pearson ;
Parameters (R input):
par1 = pearson ;
R code (references can be found in the software module):
panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method=par1)
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
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,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')