FitTwoStateSPR: fit the sensorgrame data based on the two state model
In ffeng23/SPRATS: An R package to do Two state modelling of SPR data

Description Usage Arguments Details Value

estimate the mean equilibrium dissociation constant based on the two state model

FitTwoStateSPR(x, mode = 1, type = 1, steadyStateStart = -1,
  steadyStateEnd = -1, windowSize = 10, init.association = NULL,
  init.dissociation = NULL, control = list(maxiter = 500, tol = 0.01,
  minFactor = 1/1e+10), trace = F, fix.ligand = TRUE, Rligand = NULL)

`x`	SensorgramData containing the data to be fitted
`mode`	Integer to selection which type of fitting to do 1 to do nonlinear regression 2 to do linear regression. nonlinear regression is better
`type`	integer to indicate which type of analysis in dissociation phase to be used. 1. for multi-state approximation 2. for two state do fitting and then regression.
`steadyStateStart`	numeric the starting time for steady state optional and if provided, will overwrite the one in sensorgramdata
`steadyStateEnd`	numeric the ending time for steady state optional and if provided, will overwrite the ones in sensorgramdata
`windowSize`	numeric the time period used to do approximation to estimate the mean dissociaiton rate constant
`init.association`	list of initial values of parameter to do the non-linear regression. It has the following members list(Rmax=200, KD=1E-3)
`init.dissociation`	list of initial values of parameter to do the non-linear regression. It has the following members for type 1 analysis list(R1=100, r1=0.1) and for type 2 analysis list(R1=20, R2=200,r1=-0.005,r2=-0.001)
`control`	list of control elements for run non-linear regression
`trace`	boolean to control whether show the trace of nonlinear regression.
`fix.ligand`	a boolean indicating which ligand immoblization model is using. TRUE, the fixed ligand immobilzation model. Rmax is used FALSE, the variable ligand immbolization model. Rligand and efficiency are used. See also `LangmuirModel-class`
`Rligand`	the input for the variable immobilization levels of ligands on the chip. This is used by the variable ligand immbolization model.See also `LangmuirModel-class`

It can also estimate the mean dissociation rate constants at the steady state. It assumes the two state model, but can be easily generilized to more than "two-conformation" cases. In the multi-conformation cases, the mean dissociation rate constant can only be approximated empirically using the data of a small window at the beginning of the dissociation phase. Check detail of the implementation here http:// Note: this one is a S4 method. it is probably not necessary. We will change it(??).

a list of parameters estimated

ffeng23/SPRATS documentation built on May 16, 2019, 12:50 p.m.