Function reference • fmrireg

hemodynamic response functions

functions related to hemodynamic response functions

hrf(): hemodynamic regressor specification function for model formulas.

hrf_gamma(): Gamma HRF (hemodynamic response function)

hrf_gaussian(): Gaussian HRF (hemodynamic response function)

hrf_spmg1(): hrf_spmg1

hrf_time(): HRF (hemodynamic response function) as a linear function of time

hrf_inv_logit(): hrf_inv_logit

hrf_mexhat(): Mexican Hat HRF (hemodynamic response function)

hrf_sine(): hrf_sine

hrf_half_cosine(): Hemodynamic Response Function with Half-Cosine Basis

hrf_bspline(): B-spline HRF (hemodynamic response function)

gen_hrf(): Construct an HRF Instance

gen_hrf_blocked() hrf_blocked(): Generate a Blocked HRF Function

gen_hrf_lagged() hrf_lagged(): Generate a Lagged HRF Function

hrf_toeplitz(): HRF Toeplitz Matrix

gen_hrf_set(): Generate an HRF Basis Set

gen_empirical_hrf(): Generate an Empirical Hemodynamic Response Function

afni_hrf(): construct an native AFNI hrf specification for '3dDeconvolve' with the 'stim_times' argument.

hrf_smoothing_kernel(): Compute an HRF smoothing kernel

BSpline(): B-spline basis

Ident(): Ident

Poly(): Polynomila basis

functions related to creating, evaluating, and querying regression-related building blocks

regressor(): construct a regressor object

evaluate(): evaluate a function over a sampling grid

onsets(): get event onsets of a variable

nbasis(): return number of basis functions associated with hrf.

durations(): get event durations of a variable

global_onsets(): return the "global" onsets of an object.

global_onsets(<sampling_frame>): Compute global onsets from a sampling_frame

amplitudes(): get amplitude vector

samples(): extract samples

samples(<sampling_frame>): Extract samples from a sampling_frame

blockids(): get the block indices

blocklens(): get block lengths

sampling_frame(): Construct a sampling_frame

trialwise(): trialwise

afni_trialwise(): construct an native AFNI hrf specification for '3dDeconvolve' and individually modulated events using the 'stim_times_IM' argument.

null_regressor(): null_regressor

single_trial_regressor(): single_trial_regressor

convolve(): convolve

convolve(<event_term>): Convolve an event-related design matrix with an HRF.

convolve_design(): Convolve HRF with Design Matrix

convolve_block(): Convolve hemodynamic response with a block duration

shift(): Shift a time series object

event models

functions for building and querying event models for regression analysis

event_model(): Construct an event model

event_model(<list>): event_model.list

event_model(<formula>): event_model.formula

event_table(): event_table

baseline_model(): construct a baseline model to model noise and other non-event-related sources of variance

baseline(): Create a model specification for modeling low-frequency drift in fmri time series.

baseline_terms(): baseline_terms

baseline_term(): baseline_term

block(): a block variable, which is constant over the span of a scanning run

construct(): construct

event_factor(): Create a categorical event sequence from a factor

event_term(): Create an event model term from a named list of variables.

event_basis(): Create an event set from a ParametricBasis object

event_variable(): Create a continuous valued event sequence from a numeric vector

event_matrix(): Create a continuous valued event set from a matrix

event_terms(): event_terms

matrix_term(): matrix_term

design_matrix(): design_matrix

conditions(): Conditions

cells(): The experimental cells of a design

columns(): columns

levels(): levels

elements(): elements

term_matrices(<fmri_model>): Construct term matrices for an fMRI model

term_names(): term_names

parent_terms(): parent_terms

shortnames(): extract short shortnames of variable

longnames() longnames(): extract long names of variable

is_continuous(): is_continuous

is_categorical(): is_categorical

term_indices(): term_indices

term_matrices(): term_matrices

block_term(): block_term

nuisance(): nuisance

covariate(): Construct a Covariate Term

contrasts

functions for creating statistical contrasts

contrast(): Contrast Specification

contrast_set(): Create a Set of Contrasts

contrast_weights(): contrast_weights

contrast_weights(<unit_contrast_spec>): Unit Contrast Weights

contrast_weights(<poly_contrast_spec>): Polynomial Contrast Weights

pair_contrast(): Pair Contrast

pairwise_contrasts(): Pairwise Contrasts

unit_contrast(): Unit Contrast

poly_contrast(): Polynomial Contrast

one_against_all_contrast(): One Against All Contrast

Fcontrasts(): Fcontrasts

Fcontrasts(<event_term>): Compute F-contrasts for Event Term

beta_stats(): Beta Statistics for Linear Model

fit_contrasts(): Fit Contrasts for Linear Model

estcon(): estcon

datasets

functions related to fMRI dataset representations

fmri_dataset(): Create an fMRI Dataset Object from a Set of Scans

latent_dataset(): Create a Latent Dataset Object

fmri_mem_dataset(): Create an fMRI Memory Dataset Object

matrix_dataset(): Create a Matrix Dataset Object

get_data(): get_data

get_mask(): get_mask

data_chunks(): return a set of data chunks

data_chunks(<fmri_file_dataset>): Create Data Chunks for fmri_file_dataset Objects

data_chunks(<fmri_mem_dataset>): Create Data Chunks for fmri_mem_dataset Objects

data_chunks(<matrix_dataset>): Create Data Chunks for matrix_dataset Objects

model execution and stats

functions related to running model functions

fmri_model(): Construct an fMRI regression model

fmri_lm(): Fit a linear regression model for fMRI data analysis

fmri_latent_lm(): Fast fMRI Regression Model Estimation from a Latent Component Dataset

fmri_rlm(): fmri_rlm

afni_lm(): Set up an fMRI linear model for AFNI's 3dDeconvolve

fmri_lm_fit(): Fit an fMRI linear regression model with a specified fitting strategy

estimate_betas(): estimate trialwise beta coefficients for a dataset

estimate_betas(<fmri_dataset>): #' @export estimate_betas.fmri_mem_dataset <- function(x,fixed=NULL, ran, block, method=c("mixed", "pls", "pls_searchlight", "pls_global", "ols"), basemod=NULL, radius=8, niter=8, ncomp=4, lambda=.01,...) estimate_betas.fmri_dataset(x,fixed,ran,block, method, basemod, radius, niter,ncomp, lambda,...) Estimate betas for an fMRI dataset

estimate_betas(<matrix_dataset>): Estimate betas for a matrix dataset

estimate_hrf(): Estimate hemodynamic response function (HRF) using Generalized Additive Models (GAMs)

gen_afni_lm(): generate an AFNI linear model command from a configuration file

p_values(): p_values

standard_error(): standard_error

standard_error(<fmri_latent_lm>): Calculate the standard error for an fmri_latent_lm object

run(<afni_lm_spec>): Run an afni_lm_spec object

chunkwise_lm(): estimate a linear model sequentially for each "chunk" (a matrix of time-series) of data

stats(): stats

miscellaneous

miscellaneous and utlity functions

split_by_block(): split_by_block

split_onsets(): split an onset vector into a list

split_onsets(<event_term>): Split onsets of an event_term object

plot(<regressor>): plot a regressor object

design_plot(): Design Plot for fMRI Model

read_fmri_config(): read a basic fMRI configuration file

fmrireg: fmrireg: regresssion tools for fMRI data

despike(): Despike Time Series Data

soft_threshold(): Soft-threshold function

print(<fmri_betas>): Pretty print method for fmri_betas objects

plot(<event_model>): Plot an event_model object

evaluate(<hrfspec>): evaluate.hrfspec

evaluate(<HRF>): evaluate.HRF

multiresponse_bootstrap_lm(): Multiresponse Bootstrap Linear Model

run(): run