tmsRegression: Generate regression parameters from TMS data

In fernandoPalluzzi/tmsClassifier: Random Forest Classifiers for Transcranial Magnetic Stimulation Data

Description

Generate regression parameters from TMS data, needed for subject classification. For each subject, TMS indicators (SICI-ICF, SAI, LICI) are modeled as a polynomial functions of time, in the form y ~ poly(t). This function estimates two parameters for SICI (SICI = bs0 + bs*t), two parameters for ICF (ICF = bi0 + bi*t), three parameters for SAI (SAI = b0 + b1*t + b2*t^2), and two parameters for LICI (LICI = a0 + a1*t).

Usage

tmsRegression(tms, sici.icf = 1:7, sai = 8:11, lici = 12:14,
  adjust = NULL)

Arguments

tms	A data.frame containing subjects as rows and TMS values as columns. Optionally, the user may add covariate columns (e.g., sex, age, center) to adjust TMS regression estimates.
sici.icf	Numeric vector determining the position of temporally-ordered SICI-ICF columns (SICI: short-interval intracortical inhibition; ICF: intracortical facilitation). By default, they should be the first 7 measures (sici.icf = 1:7; 4 for SICI and 3 for ICF), taken at times (interstimulus intervals): 1, 2, 3, 5, 7, 10, 15 ms. Set sici.icf to NULL to exclude these values from classification.
sai	Numeric vector determining the position of temporally-ordered SAI (short-latency afferent inhibition) columns. By default, they should be the 4 columns following sici.icf (sai = 8:11), taken at time steps (interstimulus intervals): -4, 0, 4, 8 ms. Set sai to NULL to exclude these values from classification.
lici	Numeric vector determining the position of temporally-ordered LICI (long-interval intracortical inhibition) columns. By default, they should be the 3 columns following sai (lici = 12:14), taken at time steps (interstimulus intervals): 50, 100, 150 ms. Set lici to NULL to exclude these values from classification.
adjust	Numeric vector determining the position of covariates to adjust for. By default, adjust = NULL (no covariate adjustment is done).

Value

A data.frame of estimated regression parameters.

fernandoPalluzzi/tmsClassifier documentation built on Feb. 3, 2021, 12:31 p.m.