# HG changeset patch
# User iuc
# Date 1420826167 18000
# Node ID 2e7bc1bb2dbe7c04ae9fa64ae931749157d50ea0
# Parent ffcdde989859f4a088c5f9155d059ed631467545
Uploaded
diff -r ffcdde989859 -r 2e7bc1bb2dbe cca.xml
--- a/cca.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/cca.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,97 +1,101 @@
-
-
-
-
- statistic_tools_macros.xml
-
-
- cca.py
- $input1
- $x_cols
- $y_cols
- $x_scale
- $y_scale
- $std_scores
- $out_file1
- $out_file2
-
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-
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-.. class:: infomark
-
-**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
-
------
-
-.. class:: infomark
-
-**What it does**
-
-This tool uses functions from 'yacca' library from R statistical package to perform Canonical Correlation Analysis (CCA) on the input data.
-It outputs two files, one containing the summary statistics of the performed CCA, and the other containing helioplots, which display structural loadings of X and Y variables on different canonical components.
-
-*Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.*
-
------
-
-.. class:: warningmark
-
-**Note**
-
-- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results.
-
-- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
-
-- The summary statistics in the output are described below:
-
- - correlation: Canonical correlation between the canonical variates (i.e. transformed variables)
- - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation
- - p-value: denotes significance of canonical correlations
- - Coefficients: represent the coefficients of X and Y variables on each canonical variate
- - Loadings: represent the correlations between the original variables in each set and their respective canonical variates
- - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates
-
-
-
+
+
+
+
+ statistic_tools_macros.xml
+
+
+
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+
+
+
+
+
+
+
+
+Convert characters*
+
+-----
+
+.. class:: infomark
+
+**What it does**
+
+This tool uses functions from 'yacca' library from R statistical package to perform Canonical Correlation Analysis (CCA) on the input data.
+It outputs two files, one containing the summary statistics of the performed CCA, and the other containing helioplots, which display structural loadings of X and Y variables on different canonical components.
+
+*Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.*
+
+-----
+
+.. class:: warningmark
+
+**Note**
+
+- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results.
+
+- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
+
+- The summary statistics in the output are described below:
+
+ - correlation: Canonical correlation between the canonical variates (i.e. transformed variables)
+ - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation
+ - p-value: denotes significance of canonical correlations
+ - Coefficients: represent the coefficients of X and Y variables on each canonical variate
+ - Loadings: represent the correlations between the original variables in each set and their respective canonical variates
+ - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates
+
+]]>
+
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe cor.xml
--- a/cor.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/cor.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,102 +1,112 @@
-
- for numeric columns
-
-
- statistic_tools_macros.xml
-
- cor.py $input1 $out_file1 $numeric_columns $method
-
-
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-
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-
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-
-
-.. class:: infomark
-
-**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert*
-
-.. class:: warningmark
-
-Missing data ("nan") removed from each pairwise comparison
-
------
-
-**Syntax**
-
-This tool computes the matrix of correlation coefficients between numeric columns.
-
-- All invalid, blank and comment lines are skipped when performing computations. The number of skipped lines is displayed in the resulting history item.
-
-- **Pearson's Correlation** reflects the degree of linear relationship between two variables. It ranges from +1 to -1. A correlation of +1 means that there is a perfect positive linear relationship between variables. The formula for Pearson's correlation is:
-
- .. image:: $PATH_TO_IMAGES/pearson.png
-
- where n is the number of items
-
-- **Kendall's rank correlation** is used to measure the degree of correspondence between two rankings and assessing the significance of this correspondence. The formula for Kendall's rank correlation is:
-
- .. image:: $PATH_TO_IMAGES/kendall.png
-
- where n is the number of items, and P is the sum.
-
-- **Spearman's rank correlation** assesses how well an arbitrary monotonic function could describe the relationship between two variables, without making any assumptions about the frequency distribution of the variables. The formula for Spearman's rank correlation is
-
- .. image:: $PATH_TO_IMAGES/spearman.png
-
- where D is the difference between the ranks of corresponding values of X and Y, and N is the number of pairs of values.
-
------
-
-**Example**
-
-- Input file::
-
- #Person Height Self Esteem
- 1 68 4.1
- 2 71 4.6
- 3 62 3.8
- 4 75 4.4
- 5 58 3.2
- 6 60 3.1
- 7 67 3.8
- 8 68 4.1
- 9 71 4.3
- 10 69 3.7
- 11 68 3.5
- 12 67 3.2
- 13 63 3.7
- 14 62 3.3
- 15 60 3.4
- 16 63 4.0
- 17 65 4.1
- 18 67 3.8
- 19 63 3.4
- 20 61 3.6
-
-- Computing the correlation coefficients between columns 2 and 3 of the above file (using Pearson's Correlation), the output is::
-
- 1.0 0.730635686279
- 0.730635686279 1.0
-
- So the correlation for our twenty cases is .73, which is a fairly strong positive relationship.
-
-
+
+ for numeric columns
+
+
+ statistic_tools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Convert*
+
+.. class:: warningmark
+
+Missing data ("nan") removed from each pairwise comparison
+
+-----
+
+**Syntax**
+
+This tool computes the matrix of correlation coefficients between numeric columns.
+
+- All invalid, blank and comment lines are skipped when performing computations. The number of skipped lines is displayed in the resulting history item.
+
+- **Pearson's Correlation** reflects the degree of linear relationship between two variables. It ranges from +1 to -1. A correlation of +1 means that there is a perfect positive linear relationship between variables. The formula for Pearson's correlation is:
+
+ .. image:: $PATH_TO_IMAGES/pearson.png
+
+ where n is the number of items
+
+- **Kendall's rank correlation** is used to measure the degree of correspondence between two rankings and assessing the significance of this correspondence. The formula for Kendall's rank correlation is:
+
+ .. image:: $PATH_TO_IMAGES/kendall.png
+
+ where n is the number of items, and P is the sum.
+
+- **Spearman's rank correlation** assesses how well an arbitrary monotonic function could describe the relationship between two variables, without making any assumptions about the frequency distribution of the variables. The formula for Spearman's rank correlation is
+
+ .. image:: $PATH_TO_IMAGES/spearman.png
+
+ where D is the difference between the ranks of corresponding values of X and Y, and N is the number of pairs of values.
+
+-----
+
+**Example**
+
+- Input file::
+
+ #Person Height Self Esteem
+ 1 68 4.1
+ 2 71 4.6
+ 3 62 3.8
+ 4 75 4.4
+ 5 58 3.2
+ 6 60 3.1
+ 7 67 3.8
+ 8 68 4.1
+ 9 71 4.3
+ 10 69 3.7
+ 11 68 3.5
+ 12 67 3.2
+ 13 63 3.7
+ 14 62 3.3
+ 15 60 3.4
+ 16 63 4.0
+ 17 65 4.1
+ 18 67 3.8
+ 19 63 3.4
+ 20 61 3.6
+
+- Computing the correlation coefficients between columns 2 and 3 of the above file (using Pearson's Correlation), the output is::
+
+ 1.0 0.730635686279
+ 0.730635686279 1.0
+
+ So the correlation for our twenty cases is .73, which is a fairly strong positive relationship.
+]]>
+
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe gsummary.xml
--- a/gsummary.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/gsummary.xml Fri Jan 09 12:56:07 2015 -0500
@@ -4,7 +4,14 @@
statistic_tools_macros.xml
- gsummary.py $input $out_file1 "$cond"
+
+
+
@@ -22,6 +29,7 @@
+Convert delimiters to TAB*
.. class:: infomark
@@ -73,5 +81,6 @@
#sum mean stdev 0% 25% 50% 75% 100%
29250.000 7312.500 7198.636 1700.000 1895.000 5280.000 10697.500 16990.000
+]]>
diff -r ffcdde989859 -r 2e7bc1bb2dbe histogram.xml
--- a/histogram.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/histogram.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,77 +1,91 @@
-
- of a numeric column
-
-
- statistic_tools_macros.xml
-
- histogram.py $input $out_file1 $numerical_column "$title" "$xlab" $breaks $density $frequency
-
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-.. class:: infomark
-
-**TIP:** To remove comment lines that do not begin with a *#* character, use *Text Manipulation->Remove beginning*
-
- .. class:: infomark
-
-**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert*
-
------
-
-**Syntax**
-
-This tool computes a histogram of the numerical values in a column of a dataset.
-
-- All invalid, blank and comment lines in the dataset are skipped. The number of skipped lines is displayed in the resulting history item.
-- **Column for x axis** - only numerical columns are possible.
-- **Number of breaks(bars)** - breakpoints between histogram cells. Value of '0' will determine breaks automatically.
-- **Plot title** - the histogram title.
-- **Label for x axis** - the label of the x axis for the histogram.
-- **Include smoothed density** - if checked, the resulting graph will join the given corresponding points with line segments.
-
------
-
-**Example**
-
-- Input file::
-
- 1 68 4.1
- 2 71 4.6
- 3 62 3.8
- 4 75 4.4
- 5 58 3.2
- 6 60 3.1
- 7 67 3.8
- 8 68 4.1
- 9 71 4.3
- 10 69 3.7
-
-- Create a histogram on column 2 of the above dataset.
-
-.. image:: $PATH_TO_IMAGES/histogram2.png
-
-
-
+
+ of a numeric column
+
+
+ statistic_tools_macros.xml
+
+
+
+
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+
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+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+Remove beginning*
+
+ .. class:: infomark
+
+**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert*
+
+-----
+
+**Syntax**
+
+This tool computes a histogram of the numerical values in a column of a dataset.
+
+- All invalid, blank and comment lines in the dataset are skipped. The number of skipped lines is displayed in the resulting history item.
+- **Column for x axis** - only numerical columns are possible.
+- **Number of breaks(bars)** - breakpoints between histogram cells. Value of '0' will determine breaks automatically.
+- **Plot title** - the histogram title.
+- **Label for x axis** - the label of the x axis for the histogram.
+- **Include smoothed density** - if checked, the resulting graph will join the given corresponding points with line segments.
+
+-----
+
+**Example**
+
+- Input file::
+
+ 1 68 4.1
+ 2 71 4.6
+ 3 62 3.8
+ 4 75 4.4
+ 5 58 3.2
+ 6 60 3.1
+ 7 67 3.8
+ 8 68 4.1
+ 9 71 4.3
+ 10 69 3.7
+
+- Create a histogram on column 2 of the above dataset.
+
+.. image:: $PATH_TO_IMAGES/histogram2.png
+
+]]>
+
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe kcca.xml
--- a/kcca.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/kcca.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,151 +1,155 @@
-
-
-
-
- statistic_tools_macros.xml
-
-
- kcca.py
- --input=$input1
- --output1=$out_file1
- --x_cols=$x_cols
- --y_cols=$y_cols
- --kernel=$kernelChoice.kernel
- --features=$features
- #if $kernelChoice.kernel == "rbfdot" or $kernelChoice.kernel == "anovadot":
- --sigma=$kernelChoice.sigma
- --degree="None"
- --scale="None"
- --offset="None"
- --order="None"
- #elif $kernelChoice.kernel == "polydot":
- --sigma="None"
- --degree=$kernelChoice.degree
- --scale=$kernelChoice.scale
- --offset=$kernelChoice.offset
- --order="None"
- #elif $kernelChoice.kernel == "tanhdot":
- --sigma="None"
- --degree="None"
- --scale=$kernelChoice.scale
- --offset=$kernelChoice.offset
- --order="None"
- #elif $kernelChoice.kernel == "besseldot":
- --sigma=$kernelChoice.sigma
- --degree=$kernelChoice.degree
- --scale="None"
- --offset="None"
- --order=$kernelChoice.order
- #elif $kernelChoice.kernel == "anovadot":
- --sigma=$kernelChoice.sigma
- --degree=$kernelChoice.degree
- --scale="None"
- --offset="None"
- --order="None"
- #else:
- --sigma="None"
- --degree="None"
- --scale="None"
- --offset="None"
- --order="None"
- #end if
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-.. class:: infomark
-
-**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
-
------
-
-.. class:: infomark
-
-**What it does**
-
-This tool uses functions from 'kernlab' library from R statistical package to perform Kernel Canonical Correlation Analysis (kCCA) on the input data.
-
-*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/*
-
------
-
-.. class:: warningmark
-
-**Note**
-
-This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
-
-
-
+
+
+
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+ statistic_tools_macros.xml
+
+
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+
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+
+
+
+
+
+
+
+
+Convert characters*
+
+-----
+
+.. class:: infomark
+
+**What it does**
+
+This tool uses functions from 'kernlab' library from R statistical package to perform Kernel Canonical Correlation Analysis (kCCA) on the input data.
+
+*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/*
+
+-----
+
+.. class:: warningmark
+
+**Note**
+
+This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
+
+]]>
+
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe kpca.xml
--- a/kpca.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/kpca.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,141 +1,145 @@
-
-
-
-
- statistic_tools_macros.xml
-
-
- kpca.py
- --input=$input1
- --output1=$out_file1
- --output2=$out_file2
- --var_cols=$var_cols
- --kernel=$kernelChoice.kernel
- --features=$features
- #if $kernelChoice.kernel == "rbfdot" or $kernelChoice.kernel == "anovadot":
- --sigma=$kernelChoice.sigma
- --degree="None"
- --scale="None"
- --offset="None"
- --order="None"
- #elif $kernelChoice.kernel == "polydot":
- --sigma="None"
- --degree=$kernelChoice.degree
- --scale=$kernelChoice.scale
- --offset=$kernelChoice.offset
- --order="None"
- #elif $kernelChoice.kernel == "tanhdot":
- --sigma="None"
- --degree="None"
- --scale=$kernelChoice.scale
- --offset=$kernelChoice.offset
- --order="None"
- #elif $kernelChoice.kernel == "besseldot":
- --sigma=$kernelChoice.sigma
- --degree=$kernelChoice.degree
- --scale="None"
- --offset="None"
- --order=$kernelChoice.order
- #elif $kernelChoice.kernel == "anovadot":
- --sigma=$kernelChoice.sigma
- --degree=$kernelChoice.degree
- --scale="None"
- --offset="None"
- --order="None"
- #else:
- --sigma="None"
- --degree="None"
- --scale="None"
- --offset="None"
- --order="None"
- #end if
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-.. class:: infomark
-
-**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
-
------
-
-.. class:: infomark
-
-**What it does**
-
-This tool uses functions from 'kernlab' library from R statistical package to perform Kernel Principal Component Analysis (kPCA) on the input data. It outputs two files, one containing the summary statistics of the performed kPCA, and the other containing a scatterplot matrix of rotated values reported by kPCA.
-
-*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/*
-
------
-
-.. class:: warningmark
-
-**Note**
-
-This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
-
-
-
+
+
+
+
+ statistic_tools_macros.xml
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+Convert characters*
+
+-----
+
+.. class:: infomark
+
+**What it does**
+
+This tool uses functions from 'kernlab' library from R statistical package to perform Kernel Principal Component Analysis (kPCA) on the input data. It outputs two files, one containing the summary statistics of the performed kPCA, and the other containing a scatterplot matrix of rotated values reported by kPCA.
+
+*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/*
+
+-----
+
+.. class:: warningmark
+
+**Note**
+
+This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
+
+]]>
+
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe linear_regression.xml
--- a/linear_regression.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/linear_regression.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,72 +1,76 @@
-
-
-
-
- statistic_tools_macros.xml
-
-
- linear_regression.py
- $input1
- $response_col
- $predictor_cols
- $out_file1
- $out_file2
- 1>/dev/null
-
-
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-
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-
-
-.. class:: infomark
-
-**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
-
------
-
-.. class:: infomark
-
-**What it does**
-
-This tool uses the 'lm' function from R statistical package to perform linear regression on the input data. It outputs two files, one containing the summary statistics of the performed regression, and the other containing diagnostic plots to check whether model assumptions are satisfied.
-
-*R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.*
-
------
-
-.. class:: warningmark
-
-**Note**
-
-- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results.
-
-- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
-
-- The summary statistics in the output are described below:
-
- - sigma: the square root of the estimated variance of the random error (standard error of the residiuals)
- - R-squared: the fraction of variance explained by the model
- - Adjusted R-squared: the above R-squared statistic adjusted, penalizing for the number of the predictors (p)
- - p-value: p-value for the t-test of the null hypothesis that the corresponding slope is equal to zero against the two-sided alternative.
-
-
-
-
+
+
+
+
+ statistic_tools_macros.xml
+
+
+/dev/null
+]]>
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Convert characters*
+
+-----
+
+.. class:: infomark
+
+**What it does**
+
+This tool uses the 'lm' function from R statistical package to perform linear regression on the input data. It outputs two files, one containing the summary statistics of the performed regression, and the other containing diagnostic plots to check whether model assumptions are satisfied.
+
+*R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.*
+
+-----
+
+.. class:: warningmark
+
+**Note**
+
+- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results.
+
+- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
+
+- The summary statistics in the output are described below:
+
+ - sigma: the square root of the estimated variance of the random error (standard error of the residiuals)
+ - R-squared: the fraction of variance explained by the model
+ - Adjusted R-squared: the above R-squared statistic adjusted, penalizing for the number of the predictors (p)
+ - p-value: p-value for the t-test of the null hypothesis that the corresponding slope is equal to zero against the two-sided alternative.
+
+
+]]>
+
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe logistic_regression_vif.xml
--- a/logistic_regression_vif.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/logistic_regression_vif.xml Fri Jan 09 12:56:07 2015 -0500
@@ -5,12 +5,14 @@
statistic_tools_macros.xml
- logistic_regression_vif.py
+/dev/null
+]]>
@@ -33,11 +35,12 @@
+Convert characters*
-----
@@ -67,9 +70,10 @@
- Coefficient indicates log ratio of (probability to be class 1 / probability to be class 0)
- This tool also provides **Variance Inflation Factor or VIF** which quantifies the level of multicollinearity. The tool will automatic generate VIF if the model has more than one predictor. The higher the VIF, the higher is the multicollinearity. Multicollinearity will inflate standard error and reduce level of significance of the predictor. In the worst case, it can reverse direction of slope for highly correlated predictors if one of them is significant. A general thumb-rule is to use those predictors having VIF lower than 10 or 5.
-- **vif** is calculated by
+- **vif** is calculated by
- First, regressing each predictor over all other predictors, and recording R-squared for each regression.
- Second, computing vif as 1/(1- R_squared)
+]]>
diff -r ffcdde989859 -r 2e7bc1bb2dbe partialR_square.xml
--- a/partialR_square.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/partialR_square.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,69 +1,73 @@
-
-
-
-
- statistic_tools_macros.xml
-
-
- partialR_square.py
- $input1
- $response_col
- $predictor_cols
- $out_file1
- 1>/dev/null
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. class:: infomark
-
-**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
-
------
-
-.. class:: infomark
-
-**What it does**
-
-This tool computes the Partial R squared for all possible variable subsets using the following formula:
-
-**Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, which denotes the case where the 'i'th predictor is dropped.
-
-
-
-In general, **Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, where,
-
-- SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the full set of predictors SSE(X1, X2 … Xp)
-- SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the set of predictors excluding; for example, if we omit the first predictor, it will be SSE(X2 … Xp).
-
-
-The 4 columns in the output are described below:
-
-- Column 1 (Model): denotes the variables present in the model
-- Column 2 (R-sq): denotes the R-squared value corresponding to the model in Column 1
-- Column 3 (Partial R squared_Terms): denotes the variable/s for which Partial R squared is computed. These are the variables that are absent in the reduced model in Column 1. A '-' in this column indicates that the model in Column 1 is the Full model.
-- Column 4 (Partial R squared): denotes the Partial R squared value corresponding to the variable/s in Column 3. A '-' in this column indicates that the model in Column 1 is the Full model.
-
-*R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.*
-
-
-
+
+
+
+
+ statistic_tools_macros.xml
+
+
+/dev/null
+]]>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Convert characters*
+
+-----
+
+.. class:: infomark
+
+**What it does**
+
+This tool computes the Partial R squared for all possible variable subsets using the following formula:
+
+**Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, which denotes the case where the 'i'th predictor is dropped.
+
+
+
+In general, **Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, where,
+
+- SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the full set of predictors SSE(X1, X2 … Xp)
+- SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the set of predictors excluding; for example, if we omit the first predictor, it will be SSE(X2 … Xp).
+
+
+The 4 columns in the output are described below:
+
+- Column 1 (Model): denotes the variables present in the model
+- Column 2 (R-sq): denotes the R-squared value corresponding to the model in Column 1
+- Column 3 (Partial R squared_Terms): denotes the variable/s for which Partial R squared is computed. These are the variables that are absent in the reduced model in Column 1. A '-' in this column indicates that the model in Column 1 is the Full model.
+- Column 4 (Partial R squared): denotes the Partial R squared value corresponding to the variable/s in Column 3. A '-' in this column indicates that the model in Column 1 is the Full model.
+
+*R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.*
+
+]]>
+
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe pca.xml
--- a/pca.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/pca.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,101 +1,105 @@
-
-
-
-
- statistic_tools_macros.xml
-
-
- pca.py
- $input1
- $var_cols
- $methodChoice.method
- $out_file1
- $out_file2
- #if $methodChoice.method == "svd":
- $methodChoice.scale
- #end if
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. class:: infomark
-
-**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
-
------
-
-.. class:: infomark
-
-**What it does**
-
-This tool performs Principal Component Analysis on the given numeric input data using functions from R statistical package - 'princomp' function (for Eigenvector based solution) and 'prcomp' function (for Singular value decomposition based solution). It outputs two files, one containing the summary statistics of PCA, and the other containing biplots of the observations and principal components.
-
-*R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.*
-
------
-
-.. class:: warningmark
-
-**Note**
-
-- This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
-
-- The summary statistics in the output are described below:
-
- - Std. deviation: Standard deviations of the principal components
- - Loadings: a list of eigen-vectors/variable loadings
- - Scores: Scores of the input data on the principal components
-
-
-
+
+
+
+
+ statistic_tools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Convert characters*
+
+-----
+
+.. class:: infomark
+
+**What it does**
+
+This tool performs Principal Component Analysis on the given numeric input data using functions from R statistical package - 'princomp' function (for Eigenvector based solution) and 'prcomp' function (for Singular value decomposition based solution). It outputs two files, one containing the summary statistics of PCA, and the other containing biplots of the observations and principal components.
+
+*R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.*
+
+-----
+
+.. class:: warningmark
+
+**Note**
+
+- This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
+
+- The summary statistics in the output are described below:
+
+ - Std. deviation: Standard deviations of the principal components
+ - Loadings: a list of eigen-vectors/variable loadings
+ - Scores: Scores of the input data on the principal components
+
+]]>
+
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe readme.rst
--- a/readme.rst Tue Jul 29 06:30:45 2014 -0400
+++ b/readme.rst Fri Jan 09 12:56:07 2015 -0500
@@ -2,11 +2,12 @@
===============================
These wrappers are based on RPy1 versions included in Galaxy-Main and
-ported by John Chilton to RPy2. Please see the following Mail from
-galaxy-dev:
+ported by John Chilton and Björn Grüning to RPy2 and Galaxy tool dependencies.
+Please see the following Mail from galaxy-dev:
http://lists.bx.psu.edu/pipermail/galaxy-dev/2013-May/014694.html
+
Missing ports to RPy2:
- rgenetics/rgQC.py
@@ -17,27 +18,17 @@
ToDo:
-- add tool_dependencies.xml to RPy2 and R-3.0
- testing
- porting of missing tools
-============
-Installation
-============
-
-
-
=======
History
=======
-
- v0.1: no release yet
-
-
Wrapper Licence (MIT/BSD style)
===============================
diff -r ffcdde989859 -r 2e7bc1bb2dbe scatterplot.xml
--- a/scatterplot.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/scatterplot.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,70 +1,75 @@
-
- of two numeric columns
-
-
- statistic_tools_macros.xml
-
- scatterplot.py $input $out_file1 $col1 $col2 "$title" "$xlab" "$ylab"
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. class:: infomark
-
-**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert*
-
------
-
-**Syntax**
-
-This tool creates a simple scatter plot between two variables containing numeric values of a selected dataset.
-
-- All invalid, blank and comment lines in the dataset are skipped. The number of skipped lines is displayed in the resulting history item.
-
-- **Plot title** The scatterplot title
-- **Label for x axis** and **Label for y axis** The labels for x and y axis of the scatterplot.
-
------
-
-**Example**
-
-- Input file::
-
- 1 68 4.1
- 2 71 4.6
- 3 62 3.8
- 4 75 4.4
- 5 58 3.2
- 6 60 3.1
- 7 67 3.8
- 8 68 4.1
- 9 71 4.3
- 10 69 3.7
-
-- Create a simple scatterplot between the variables in column 2 and column 3 of the above dataset.
-
-.. image:: $PATH_TO_IMAGES/images/scatterplot.png
-
-
-
+
+ of two numeric columns
+
+
+ statistic_tools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Convert*
+
+-----
+
+**Syntax**
+
+This tool creates a simple scatter plot between two variables containing numeric values of a selected dataset.
+
+- All invalid, blank and comment lines in the dataset are skipped. The number of skipped lines is displayed in the resulting history item.
+
+- **Plot title** The scatterplot title
+- **Label for x axis** and **Label for y axis** The labels for x and y axis of the scatterplot.
+
+-----
+
+**Example**
+
+- Input file::
+
+ 1 68 4.1
+ 2 71 4.6
+ 3 62 3.8
+ 4 75 4.4
+ 5 58 3.2
+ 6 60 3.1
+ 7 67 3.8
+ 8 68 4.1
+ 9 71 4.3
+ 10 69 3.7
+
+- Create a simple scatterplot between the variables in column 2 and column 3 of the above dataset.
+
+.. image:: $PATH_TO_IMAGES/images/scatterplot.png
+
+]]>
+
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe short_reads_figure_score.xml
--- a/short_reads_figure_score.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/short_reads_figure_score.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,31 +1,37 @@
-
-
+
+ statistic_tools_macros.xml
-short_reads_figure_score.py $input1 $output1
-
-
-
-
-
-
+
+
+
+
+
+
+
+
-
-
-
-
-
-
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-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+seq1
+ 23 33 34 25 28 28 28 32 23 34 27 4 28 28 31 21 28
Illumina (Solexa) data::
- -40 -40 40 -40 -40 -40 -40 40
-
+ -40 -40 40 -40 -40 -40 -40 40
+
-----
**Output example**
@@ -62,26 +68,25 @@
- For Roche (454) X-axis (shown above) indicates **relative** position (in %) within reads as this technology produces reads of different lengths;
- For Illumina (Solexa) and ABI SOLiD X-axis shows **absolute** position in nucleotides within reads.
-
+
Every box on the plot shows the following values::
- o <---- Outliers
+ o <---- Outliers
o
- -+- <---- Upper Extreme Value that is no more
- | than box length away from the box
+ -+- <---- Upper Extreme Value that is no more
+ | than box length away from the box
|
- +--+--+ <---- Upper Quartile
+ +--+--+ <---- Upper Quartile
| |
- +-----+ <---- Median
+ +-----+ <---- Median
| |
- +--+--+ <---- Lower Quartile
+ +--+--+ <---- Lower Quartile
|
|
- -+- <---- Lower Extreme Value that is no more
+ -+- <---- Lower Extreme Value that is no more
than box length away from the box
- o <---- Outlier
-
-
-
-
+ o <---- Outlier
+
+]]>
+
diff -r ffcdde989859 -r 2e7bc1bb2dbe statistic_tools_macros.xml
--- a/statistic_tools_macros.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/statistic_tools_macros.xml Fri Jan 09 12:56:07 2015 -0500
@@ -1,7 +1,7 @@
- R_3_0_1
+ Rrpy2numpyncurses
diff -r ffcdde989859 -r 2e7bc1bb2dbe tool_dependencies.xml
--- a/tool_dependencies.xml Tue Jul 29 06:30:45 2014 -0400
+++ b/tool_dependencies.xml Fri Jan 09 12:56:07 2015 -0500
@@ -4,13 +4,13 @@
-
+
-
-
+
+
-
+
@@ -19,8 +19,8 @@
-
-
+
+ https://github.com/bgruening/download_store/raw/master/r_statistic_tools/kernlab_0.9-19.tar.gzhttps://github.com/bgruening/download_store/raw/master/r_statistic_tools/yacca_1.1.tar.gz
diff -r ffcdde989859 -r 2e7bc1bb2dbe tool_test_output.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_test_output.html Fri Jan 09 12:56:07 2015 -0500
@@ -0,0 +1,356 @@
+
+
+
+
+
+
+ Tool Test Results (powered by Planemo)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+