Download full-text PDF A vortex population viability analysis model for the Chacoan peccary (Catagonus wagneri) Article (PDF Available) September 2016 with 162 Reads. Download full-text PDF. Characterization of a DNA-Binding Protein Implicated in Transcription in Wheat Mitochondria. These data suggest a role for p63 in transcription in wheat mitochondria.
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NOTE: Windows Vista CAUTION If you are installing either of these old versions onto Windows Vista, the installer option to install the Control Panel applet must be DISABLED to avoid having it break the Control Panel on your Vista system. ![]() Final release: v.1.5.6 (End of Series)
Modify the appearance and behavior of the confusion matrix chart by changing property values. Add column and row summaries and a title.
A column-normalized column summary displays the number of correctly and incorrectly classified observations for each predicted class as percentages of the number of observations of the corresponding predicted class. A row-normalized row summary displays the number of correctly and incorrectly classified observations for each true class as percentages of the number of observations of the corresponding true class. OptionDescription'off'Do not display a column summary.' Absolute'Display the total number of correctly and incorrectly classifiedobservations for each predicted class.' Column-normalized'Display the number of correctly and incorrectly classifiedobservations for each predicted class as percentages of the numberof observations of the corresponding predicted class. Thepercentages of correctly classified observations can be thought ofas class-wise precisions (or positive predictive values).'
Total-normalized'Display the number of correctly and incorrectly classifiedobservations for each predicted class as percentages of the totalnumber of observations.Example: cm = confusionchart(,'ColumnSummary','column-normalized')Example: cm.ColumnSummary = 'column-normalized'. OptionDescription'off'Do not display a row summary.' Absolute'Display the total number of correctly and incorrectly classifiedobservations for each true class.' Row-normalized'Display the number of correctly and incorrectly classifiedobservations for each true class as percentages of the number ofobservations of the corresponding true class. The percentages ofcorrectly classified observations can be thought of as class-wiserecalls (or true positive rates).' Total-normalized'Display the number of correctly and incorrectly classifiedobservations for each true class as percentages of the total numberof observations.Example: cm = confusionchart(,'RowSummary','row-normalized')Example: cm.RowSummary = 'row-normalized'.
OptionDescription'absolute'Display the total number of observations in each cell.' Column-normalized'Normalize each cell value by the number of observations that hasthe same predicted class.' Row-normalized'Normalize each cell value by the number of observations that hasthe same true class.' Total-normalized'Normalize each cell value by the total number ofobservations.Modifying the normalization of cell values also affects the colors ofthe cells.Example: cm = confusionchart(,'Normalization','total-normalized')Example: cm.Normalization = 'total-normalized'.
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