| "FCSsummary-class" {rflowcyt} | R Documentation |
The data summary statistics along with metadata output help summarize a "FCS-class" object using the "summary" method.
Objects can be created by calls of the form new("FCSsummary", ...).
num.cells:"numeric" the number
of cells or rows from the datanum.param:"numeric" the number
of parameters or columns from the dataunivariate.stat:"matrix"
five-number summary including the standard deviation of all the
column variables metadata.info:"list" with
the following slots: "Description", "ColumnParametersSummary", and "fcsinfoNames". signature(x = "FCSsummary"): prints the output
of the summary statistics of the data and the metadatasignature(object = "FCSsummary"): same as "print"A.J. Rossini, J.Y. Wan, and Zoe Moodie
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics : New York, 2001. pp.279-283.
Jerome H. Friedman and Nicholas I. Fisher. Bump Hunting in High-Dimensional Data. Tech Report. October 28, 1998.
J. Paul Robinson, et al. Current Protocols in Cytometry. John Wiley & Sons, Inc : 2001.
Mario Roederer and Richard R. Hardy. Frequency Difference Gating: A Multivariate Method for Identifying Subsets that Differe between Samples. Cytometry, 45:56-64, 2001.
Mario Roederer and Adam Treister and Wayne Moore and Leonore A. Herzenberg. Probability Binning Comparison: A Metric for Quantitating Univariate Distribution Differences. Cytometry, 45:37-46, 2001.
Keith A. Baggerly. Probability Binning and Testing Agreement between Multivariate Immunofluorescence Histograms: Extending the Chi-Squared Test. Cytometry, 45:141-150, 2001.
"FCS-class",
"show-methods",
"print-methods"
default.sum<-new("FCSsummary")
## show, print
default.sum