Default value is automatically estimated. Default value is FALSEĪlternative numeric value indicating the number of CPU cores to be used. Not used for the "cubic" smoothing spline basis as it used the inner design points.Īlternative logical value if you want to keep the model output. Otherwise calculated as proposed by Ruppert 2002. 2005.Īlternatively an integer, the number of knots used for the "p-spline" or "cubic p-spline" basis calculation. 1999, the "p-spline" is the truncated p-spline basis as defined by Durban et al. The "cubic" basis ( default) is the cubic smoothing spline as defined by Verbyla et al. What type of basis to use, matching one of "cubic", "p-spline" or "cubic p-spline". If TRUE returns the predicted derivative information on the observed time points.By default set to FALSE.Ĭharacter string. By default set to the original time points observed in the experiment. Numeric vector containing the time points to be predicted. Numeric vector containing the sample time point information.Ĭharacter, numeric or factor vector containing information about the unique identity of each sample LmmSpline ( data, time, sampleID, timePredict, deri, basis, knots, keepModels, numCores )ĭata.frame or matrix containing the samples as rows and features as columns summary.noise: Summary of a 'noise' Object.summary.lmmspline: Summary of a 'lmmspline' Object.summary.lmmsde: Summary of a 'lmmsde' Object.predict.lmmspline: Predicts fitted values of an 'lmmspline' Object.plot.noise: Plot of 'associations' objects.plot.lmmspline: Plot of 'lmmspline' object.
lmmsDE-methods: Differential expression analysis using linear mixed effect.lmmsde-class: 'lmmsde' class a S4 class that extends 'lmms' class.lmms-class: 'lmms' class a S4 superclass to extend 'lmmspline' and.kidneySimTimeGroup: Kidney Simulation Data.investNoise-methods: Quality control for time course profiles.filterNoise-methods: Filter non-informative trajectories.deriv.lmmspline: Derivative information for 'lmmspline' objects.
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