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comparison partial_least_squares.xml @ 2:caba07f41453 draft default tip

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"planemo upload for repository https://github.com/secimTools/SECIMTools/tree/main/galaxy commit 498abad641099412df56f04ff6e144e4193bbc34-dirty"
author malex
date Thu, 10 Jun 2021 15:41:17 +0000
parents 2e7d47c0b027
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comparison
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1:2e7d47c0b027 2:caba07f41453
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76 For single cross-validation: the same data are used to fit the model and to evaluate the model using three-fold cross validation. 76 For single cross-validation: the same data are used to fit the model and to evaluate the model using three-fold cross validation.
77 77
78 More details can be found in: 78 More details can be found in:
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80 Geladi, Paul, and Bruce R. Kowalski. "Partial least-squares regression: a tutorial." Analytica chimica acta 185 (1986): 1-17. 80 Geladi, Paul and Bruce R. Kowalski. "Partial least-squares regression: a tutorial." Analytica chimica acta 185 (1986): 1-17.
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83 -------------------------------------------------------------------------------- 83 --------------------------------------------------------------------------------
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85 **Note** 85 **Note**
118 - The choice of cross-validation options available for the user. None corresponds to no cross-validation when the user specifies the number of components manually. 118 - The choice of cross-validation options available for the user. None corresponds to no cross-validation when the user specifies the number of components manually.
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120 **Number of Components** 120 **Number of Components**
121 121
122 - The parameter is used only when the "None" cross-validation option is selected. If the field is left blank, the number of components is set to the default value (2). 122 - The parameter is used only when the "None" cross-validation option is selected. If the field is left blank, the number of components is set to the default value (2).
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123 -------------------------------------------------------------------------------- 124 --------------------------------------------------------------------------------
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125 **Output** 126 **Output**
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132 (2) a TSV file containing the weights produced by the model for each feature. 133 (2) a TSV file containing the weights produced by the model for each feature.
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134 (3) a TSV file containing the classification produced by the model for each sample. 135 (3) a TSV file containing the classification produced by the model for each sample.
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136 (4) a TSV file containing the algorithm classification accuracy (in percent). 137 (4) a TSV file containing the algorithm classification accuracy (by percentage).
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138 (5) a PDF file containing the 2D plots for all pairwise comparisons of components between the two treatment groups. 139 (5) a PDF file containing the 2D plots for all pairwise comparisons of components between the two treatment groups.
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140 **NOTE:** Regardless how many components are selected for the algorithm, pairwise 2D plots are produced for the pairs of components. 141 **NOTE:** Regardless how many components are selected for the algorithm, pairwise 2D plots are produced for the pairs of components.
141 Increasing the number of components will increase the number of plots produced. 142 Increasing the number of components will increase the number of plots produced.