classification_results: Example of output from designSampleSizeClassification...
In Vitek-Lab/MSstatsSampleSize: Simulation tool for optimal design of high-dimensional MS-based proteomics experiment
Description
Usage
Format
Details
Examples
Description
It is the output of designSampleSizeClassification function
with a list of simulated_datasets
generated under same protein number and sample size.
The list should include the required elements as below.
Usage
1
Format
A list with six elements
Details
num_proteins : the number of simulated proteins
num_samples : a vector with the number of simulated samples in each condition
mean_predictive_accuracy : the mean predictive accuracy over all the simulated datasets.
mean_feature_importance : the mean protein importance vector over all the simulated datasets,
the length of which is ‘num_proteins’.
predictive_accuracy : a vector of predictive accuracy on each simulated dataset.
feature_importance : a matrix of feature importance,
where rows are proteins and columns are simulated datasets.
the length of which is ‘num_proteins’.
Examples
1
2
3
4
classification_results$num_proteins
classification_results$num_samples
classification_results$mean_predictive_accuracy
head(classification_results$mean_feature_importance)
Vitek-Lab/MSstatsSampleSize documentation built on Aug. 28, 2020, 10:39 a.m.
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Description Usage Format Details Examples
Description
It is the output of designSampleSizeClassification function
with a list of simulated_datasets
generated under same protein number and sample size.
The list should include the required elements as below.
Usage
1 |
Format
A list with six elements
Details
num_proteins : the number of simulated proteins
num_samples : a vector with the number of simulated samples in each condition
mean_predictive_accuracy : the mean predictive accuracy over all the simulated datasets.
mean_feature_importance : the mean protein importance vector over all the simulated datasets, the length of which is ‘num_proteins’.
predictive_accuracy : a vector of predictive accuracy on each simulated dataset.
feature_importance : a matrix of feature importance, where rows are proteins and columns are simulated datasets. the length of which is ‘num_proteins’.
Examples
1 2 3 4 | classification_results$num_proteins
classification_results$num_samples
classification_results$mean_predictive_accuracy
head(classification_results$mean_feature_importance)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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