AFNI program: 3dDeconvolve
Output of -help
++ 3dDeconvolve: AFNI version=AFNI_2008_07_18_1710 (Nov 24 2009) [32-bit]
++ Authored by: B. Douglas Ward, et al.
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----- DESCRIPTION and PROLEGOMENON -----
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Program to calculate the deconvolution of a measurement 3D+time dataset
with a specified input stimulus time series. This program can also
perform multiple linear regression using multiple input stimulus time
series. Output consists of an AFNI 'bucket' type dataset containing
(for each voxel)
* the least squares estimates of the linear regression coefficients
* t-statistics for significance of the coefficients
* partial F-statistics for significance of individual input stimuli
* the F-statistic for significance of the overall regression model
The program can optionally output extra datasets containing
* the estimated impulse response function
* the fitted model and error (residual) time series
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* Program 3dDeconvolve does Ordinary Least Squares (OLSQ) regression.
* Program 3dREMLfit can be used to do Generalized Least Squares (GLSQ)
regression (AKA 'pre-whitened' least squares) combined with REML
estimation of an ARMA(1,1) temporal correlation structure:
http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dREMLfit.html
* The input to 3dREMLfit is the .xmat.1D matrix file output by
3dDeconvolve, which also writes a 3dREMLfit command line to a file
to make it relatively easy to use the latter program.
* Nonlinear time series model fitting can be done with program 3dNLfim:
http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dNLfim.html
* Preprocessing of the time series input can be done with various AFNI
programs, or with the 'uber-script' afni_proc.py:
http://afni.nimh.nih.gov/pub/dist/doc/program_help/afni_proc.py.html
* The recommended way to use 3dDeconvolve is via afni_proc.py, especially
if you are not familiar with its usage and its peculiarities.
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Consider the time series model Z(t) = K(t)*S(t) + baseline + noise,
where Z(t) = data
K(t) = kernel (e.g., hemodynamic response function or HRF)
S(t) = stimulus time series
baseline = constant, drift, etc. [regressors of no interest]
and * = convolution
Then 3dDeconvolve solves for K(t) given S(t). If you want to process
the reverse problem and solve for S(t) given the kernel K(t), use the
program 3dTfitter with the '-FALTUNG' option. The difference between
the two cases is that K(t) is presumed to be causal and have limited
support, whereas S(t) is a full-length time series. Note that program
3dTfitter does not have all the capabilities of 3dDeconvolve for
calculating output statistics; on the other hand, 3dTfitter can solve
a deconvolution problem (in either direction) with L1 or L2 regression,
and with sign constraints on the computed values (e.g., requiring that
the output S(t) or K(t) be non-negative):
http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTfitter.html
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The 'baseline model' in 3dDeconvolve (and 3dREMLfit) does not mean just
a constant (mean) level of the signal, or even just the slow drifts that
happen in FMRI time series. 'Baseline' here also means the model that
forms the null hypothesis. The Full_Fstat result is the F-statistic
of the full model (all regressors) vs. the baseline model. Thus, it
it common to include irregular time series, such as estimated motion
parameters, in the baseline model via the -stim_file/-stim_base options.
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It is VERY important to realize that statistics (F, t, R^2) computed in
3dDeconvolve are MARGINAL (or partial) statistics. For example, the
t-statistic for a single beta coefficient measures the significance of
that beta value against the regression model where ONLY that one column
of the matrix is removed; that is, the null hypothesis for that
t-statistic is the full regression model minus just that single
regressor. Similarly, the F-statistic for a set of regressors measures
the significance of that set of regressors (eg, a set of TENT functions)
against the full model with just that set of regressors removed. If
this explanation or its consequences are unclear, you need to consult
with a statistician, or with the AFNI message board guru entities.
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Usage Details:
3dDeconvolve command-line-arguments ...
**** Input data and control options:
-input fname fname = filename of 3D+time input dataset
(more than one filename can be given)
(here, and these datasets will be)
(catenated in time; if you do this, )
('-concat' is not needed and is ignored)
** You can input a 1D time series file here,
but the time axis should run along the
ROW direction, not the COLUMN direction as
in the -input1D option. You can automatically
transpose a 1D file on input using the \'
operator at the end of the filename, as in
-input fred.1D\'
* This is the only way to use 3dDeconvolve
with a multi-column 1D time series file.
* The output datasets by default will then
be in 1D format themselves. To have them
formatted as AFNI datasets instead, use
-DAFNI_WRITE_1D_AS_PREFIX=YES
on the command line.
* You should use '-force_TR' to set the TR of
the 1D 'dataset' if you use '-input' rather
than '-input1D' [the default is 1.0 sec].
[-force_TR TR] Use this value of TR instead of the one in
the -input dataset.
(It's better to fix the input using 3drefit.)
[-input1D dname] dname = filename of single (fMRI) .1D time series
where time run downs the column.
[-TR_1D tr1d] tr1d = TR for .1D time series [default 1.0 sec].
This option has no effect without -input1D
[-nodata [NT [TR]] Evaluate experimental design only (no input data)
[-mask mname] mname = filename of 3d mask dataset
[-automask] build a mask automatically from input data
(will be slow for long time series datasets)
** If you don't specify ANY mask, the program will
build one automatically (from each voxel's RMS)
and use this mask solely for the purpose of
reporting truncation-to-short errors (unless
'-float' is used) AND for computing the FDR curves
in the bucket dataset's header (unless '-noFDR'
is used, of course).
* If you don't want the FDR curves to be computed
inside this automatically generated mask, then
use '-noFDR' and later run '3drefit -addFDR' on
the bucket dataset.
* To be precise, the above default masking only
happens when you use '-input' to run the program
with a 3D+time dataset; not with '-input1D'.
[-censor cname] cname = filename of censor .1D time series
[-CENSORTR clist] clist = list of strings that specify time indexes
to be removed from the analysis. Each string is
of one of the following forms:
37 => remove global time index #37
2:37 => remove time index #37 in run #2
37..47 => remove global time indexes #37-47
37-47 => same as above
2:37..47 => remove time indexes #37-47 in run #2
*:0-2 => remove time indexes #0-2 in all runs
+Time indexes within each run start at 0.
+Run indexes start at 1 (just be to confusing).
+Multiple -CENSORTR options may be used, or
multiple -CENSORTR strings can be given at
once, separated by spaces or commas.
+N.B.: 2:37,47 means index #37 in run #2 and
global time index 47; it does NOT mean
index #37 in run #2 AND index #47 in run #2.
[-concat rname] rname = filename for list of concatenated runs
* 'rname' can be in the format
'1D: 0 100 200 300'
which indicates 4 runs, the first of which
starts at time index=0, second at index=100,
and so on.
[-nfirst fnum] fnum = number of first dataset image to use in the
deconvolution procedure. [default = max maxlag]
[-nlast lnum] lnum = number of last dataset image to use in the
deconvolution procedure. [default = last point]
[-polort pnum] pnum = degree of polynomial corresponding to the
null hypothesis [default: pnum = 1]
** If you use 'A' for pnum, the program will
automatically choose a value based on the
time duration of the longest run.
[-legendre] use Legendre polynomials for null hypothesis
(baseline model)
[-nolegendre] use power polynomials for null hypotheses
[default is -legendre]
[-nodmbase] don't de-mean baseline time series
(i.e., polort>0 and -stim_base inputs)
[-dmbase] de-mean baseline time series [default if polort>=0]
[-svd] Use SVD instead of Gaussian elimination [default]
[-nosvd] Use Gaussian elimination instead of SVD
(only use for testing + backwards compatibility)
[-rmsmin r] r = minimum rms error to reject reduced model
(default = 0; don't use this option normally!)
[-nocond] DON'T calculate matrix condition number
** This value is NOT the same as Matlab!
[-singvals] Print out the matrix singular values
(useful for some testing/debugging purposes)
[-GOFORIT [g]] Use this to proceed even if the matrix has
bad problems (e.g., duplicate columns, large
condition number, etc.).
*N.B.: Warnings that you should particularly heed have
the string '!!' somewhere in their text.
*N.B.: Error and Warning messages go to stderr and
also to file 3dDeconvolve.err.
*N.B.: The optional number 'g' that appears is the
number of warnings that can be ignored.
That is, if you use -GOFORIT 7 and 9 '!!'
matrix warnings appear, then the program will
not run. If 'g' is not present, 1 is used.
[-allzero_OK] Don't consider all zero matrix columns to be
the type of error that -GOFORIT is needed to
ignore.
* Please know what you are doing when you use
this option!
[-Dname=val] = Set environment variable 'name' to 'val' for this
run of the program only.
******* Input stimulus options *******
-num_stimts num num = number of input stimulus time series
(0 <= num) [default: num = 0]
*N.B.: '-num_stimts' must come before any of the
following 'stim' options!
*N.B.: Most 'stim' options have as their first argument
an integer 'k', ranging from 1..num, indicating
which stimulus class the argument is defining.
-stim_file k sname sname = filename of kth time series input stimulus
*N.B.: This option directly inserts a column into the
regression matrix; unless you are using the 'old'
method of deconvolution (cf below), you would
only use '-stim_file' to insert baseline model
components such as motion parameters.
[-stim_label k slabel] slabel = label for kth input stimulus
*N.B.: This option is highly recommended, so that
output sub-bricks will be labeled for ease of
recognition when you view them in the AFNI GUI.
[-stim_base k] kth input stimulus is part of the baseline model
*N.B.: 'Baseline model' == Null Hypothesis model
*N.B.: The most common baseline components to add are
the 6 estimated motion parameters from 3dvolreg.
**N.B.: The following 3 options are for the 'old' style of explicit
deconvolution. For most purposes, their usage is no longer
recommended. Instead, you should use the '-stim_times' options
to directly input the stimulus times, rather than code the
stimuli as a sequence of 0s and 1s in this 'old' method!
[-stim_minlag k m] m = minimum time lag for kth input stimulus
[default: m = 0]
[-stim_maxlag k n] n = maximum time lag for kth input stimulus
[default: n = 0]
[-stim_nptr k p] p = number of stimulus function points per TR
Note: This option requires 0 slice offset times
[default: p = 1]
**N.B.: The '-stim_times' options below are the recommended way of
analyzing FMRI time series data now. The options directly
above are only maintained for the sake of backwards
compatibility! For most FMRI users, the 'BLOCK' and 'TENT'
response models will serve their needs.
[-stim_times k tname Rmodel]
Generate the k-th response model from a set of stimulus times
given in file 'tname'. The response model is specified by the
'Rmodel' argument, which can be one of the following:
[In the descriptions, a '1 parameter' model is a model ]
[that has a fixed shape, and only the amplitude varies.]
[Models with more than 1 parameter have multiple basis ]
[functions, and the parameters are their amplitudes. ]
'BLOCK(d,p)' = 1 parameter block stimulus of duration 'd'
** There are 2 variants of BLOCK:
BLOCK4 [the default] and BLOCK5
which have slightly different delays:
HRF(t) = int( g(t-s) , s=0..min(t,d) )
where g(t) = t^q * exp(-t) /(q^q*exp(-q))
and q = 4 or 5. The case q=5 is delayed by
about 1 second from the case q=4.
** Despite the name, you can use 'BLOCK' for event-
related analyses just by setting the duration to
a small value; e.g., 'BLOCK5(1,1)'
** The 'p' parameter is the amplitude of the
basis function, and should usually be set to 1.
If 'p' is omitted, the amplitude will depend on
the duration 'd', which is useful only in
special circumstances!
'TENT(b,c,n)' = n parameter tent function expansion from times
b..c after stimulus time [piecewise linear]
[n must be at least 2; time step is (c-b)/(n-1)]
'CSPLIN(b,c,n)'= n parameter cubic spline function expansion
from times b..c after stimulus time
** TENT and CSPLIN are 'cardinal' interpolation
functions; CSPLIN is a drop-in upgrade of
TENT to a differentiable set of functions.
[n must be at least 4]
'GAM(p,q)' = 1 parameter gamma variate
(t/(p*q))^p * exp(p-t/q)
Defaults: p=8.6 q=0.547 if only 'GAM' is used
'SPMG1' = 1 parameter SPM gamma variate basis function
exp(-t)*(A1*t^P1-A2*t^P2) where
A1 = 0.0083333333 P1 = 5 (main positive lobe)
A2 = 1.274527e-13 P2 = 15 (undershoot part)
This function is NOT normalized to have peak=1!
'SPMG2' = 2 parameter SPM: gamma variate + d/dt derivative
[For backward compatibility: 'SPMG' == 'SPMG2']
'SPMG3' = 3 parameter SPM basis function set
* The SPMGx functions now can take an optional
(duration) argument, specifying that the primal
SPM basis functions should be convolved with
a square wave 'duration' seconds long and then
be normalized to have peak absolute value = 1;
e.g., 'SPMG3(20)' for a 20 second duration with
three basis function. [28 Apr 2009]
* Note that 'SPMG1(0)' will produce the usual
'SPMG1' wavefunction shape, but normalized to
have peak value = 1 (for example).
'POLY(b,c,n)' = n parameter Legendre polynomial expansion
from times b..c after stimulus time
[n can range from 1 (constant) to 20]
'SIN(b,c,n)' = n parameter sine series expansion
from times b..c after stimulus time
[n must be at least 1]
'WAV(d)' = 1 parameter block stimulus of duration 'd'.
* This is the '-WAV' function from program waver!
* If you wish to set the shape parameters of the
WAV function, you can do that by adding extra
arguments, in the order
delay time , rise time , fall time ,
undershoot fraction, undershoot restore time
* The default values are 'WAV(d,2,4,6,0.2,2)'
* Omitted parameters get the default values.
* 'WAV(d,,,,0)' (setting undershoot=0) is
very similar to 'BLOCK5(d,1)', for d > 0.
* Setting duration d to 0 (or just using 'WAV')
gives the pure '-WAV' impulse response function
from waver.
* If d > 0, the WAV(0) function is convolved with
a square wave of duration d to make the HRF,
and the amplitude is scaled back down to 1.
'EXPR(b,c) exp1 ... expn'
= n parameter; arbitrary expressions from times
b..c after stimulus time
* Expressions are separated by spaces, so
each expression must be a contiguous block
of non-whitespace characters
* Expressions use the same format as 3dcalc
* Symbols that can be used in an expression:
t = time in sec since stimulus time
x = time scaled to be x= 0..1 for t=bot..top
z = time scaled to be z=-1..1 for t=bot..top
* Spatially dependent regressors are not allowed!
* Other symbols are set to 0 (silently).
* 3dDeconvolve does LINEAR regression, so the model parameters are
amplitudes of the basis functions; 1 parameter models are 'simple'
regression, where the shape of the impulse response function is
fixed and only the magnitude/amplitude varies. Models with more
free parameters have 'variable' shape impulse response functions.
* If you want NONLINEAR regression, see program 3dNLfim.
* If you want LINEAR regression with allowance for non-white noise,
use program 3dREMLfit.
* For the format of the 'tname' file, see the last part of
http://afni.nimh.nih.gov/pub/dist/doc/misc/Decon/DeconSummer2004.html
and also see the other documents stored in the directory below:
http://afni.nimh.nih.gov/pub/dist/doc/misc/Decon/
and also read the presentation below:
http://afni.nimh.nih.gov/pub/dist/edu/latest/afni_handouts/afni05_regression.pdf
** Note Well:
* The contents of the 'tname' file are NOT just 0s and 1s,
but are the actual times of the stimulus events IN SECONDS.
* You can give the times on the command line by using a string
of the form '1D: 3.2 7.9 | 8.2 16.2 23.7' in place of 'tname',
where the '|' character indicates the start of a new line
(so this example is for a case with 2 catenated runs).
* You cannot use the '1D:' form of input for any of the more
complicated '-stim_times_*' options below!!
* It is a good idea to examine the shape of the response models
if you are unsure of what the different functions will look like.
You can graph columns from the .xmat.1D matrix file with 1dplot;
for example, comparing 'WAV(10)', 'BLOCK4(10,1)', and 'SPMG1(10)':
3dDeconvolve -nodata 200 1.0 -num_stimts 3 -polort -1 \
-local_times -x1D stdout: \
-stim_times 1 '1D: 10 60 110 160' 'WAV(10)' \
-stim_times 2 '1D: 10 60 110 160' 'BLOCK4(10,1)' \
-stim_times 3 '1D: 10 60 110 160' 'SPMG1(10)' \
| 1dplot -one -stdin -xlabel Time -ynames WAV BLOCK4 SPMG1
[-stim_times_AM1 k tname Rmodel]
Similar, but generates an amplitude modulated response model.
The 'tname' file should consist of 'time*amplitude' pairs.
As in '-stim_times', the '*' character can be used as a placeholder
when an imaging run doesn't have any stimulus of a given class.
*N.B.: If no run has a stimulus of a given class, then you must
have at least 1 time that is not '*' for -stim_times_* to
work. You can use a negative time for this purpose, which
will produce a warning message and then be ignored, as in:
-1*37
*
for a 2 run 'tname' file to be used with -stim_times_*.
In such a case, you will also need the -allzero_OK option,
and probably -GOFORIT as well.
[-stim_times_AM2 k tname Rmodel]
Similar, but generates 2 response models: one with the mean
amplitude and one with the differences from the mean.
** NOTE [04 Dec 2008] **
-stim_times_AM1 and -stim_times_AM2 now take files with more
than 1 amplitude attached to each time; for example,
33.7*9,-2,3
indicates a stimulus at time 33.7 seconds with 3 amplitudes
attached (9 and -2 and 3). In this example, -stim_times_AM2 would
generate 4 response models: 1 for the constant response case
and 1 scaled by each of the amplitude sets.
For more information on modulated regression, see
http://afni.nimh.nih.gov/pub/dist/doc/misc/Decon/AMregression.pdf
** NOTE [08 Dec 2008] **
-stim_times_AM1 and -stim_times_AM2 now have 1 extra response model
function available:
dmBLOCK (or dmBLOCK4 or dmBLOCK5)
where 'dm' means 'duration modulated'. If you use this response
model, then the LAST married parameter in the timing file will
be used to modulate the duration of the block stimulus. Any
earlier parameters will be used to modulate the amplitude,
and should be separated from the duration parameter by a ':'
character, as in '30*5,3:12' which means (for dmBLOCK):
a block starting at 30 s,
with amplitude parameters 5 and 3,
and with duration 12 s.
The unmodulated peak response of dmBLOCK is normally set to 1.
If you want the peak response to be a different value, use
dmBLOCK(p)
where p = the desired peak value. As a special case, if you set
p = 0, then the peak response will vary with the duration, as
the simulated BOLD response accumulates. Understand what you
are doing in this case!
*N.B.: The maximum allowed dmBLOCK duration is 999 s.
*N.B.: You can also use dmBLOCK with -stim_times_IM, in which case
each time in the 'tname' file should have just ONE extra
parameter -- the duration -- married to it, as in '30:15',
meaning a block of duration 15 seconds starting at t=30 s.
For some graphs of what dmBLOCK regressors look like, see
http://afni.nimh.nih.gov/pub/dist/doc/misc/Decon/AMregression.pdf
[-stim_times_IM k tname Rmodel]
Similar, but each separate time in 'tname' will get a separate
regressor; 'IM' means 'Individually Modulated' -- that is, each
event will get its own amplitude(s). Presumably you will collect
these many amplitudes afterwards and do some sort of statistics
on them.
*N.B.: Each time in the 'tname' file will get a separate regressor.
If some time is outside the duration of the imaging run(s),
or if the response model for that time happens to hit only
censored-out data values, then the corresponding regressor
will be all zeros. Normally, 3dDeconvolve will not run
if the matrix has any all zero columns. To carry out the
analysis, use the '-allzero_OK' option. Amplitude estimates
for all zero columns will be zero, and should be excluded
from any subsequent analysis. (Probably you should fix the
times in the 'tname' file instead of using '-allzero_OK'.)
[-global_times]
[-local_times]
By default, 3dDeconvolve guesses whether the times in the 'tname'
files for the various '-stim_times' options are global times
(relative to the start of run #1) or local times (relative to
the start of each run). With one of these options, you can force
the times to be considered as global or local for '-stim_times'
options that are AFTER the '-local_times' or '-global_times'.
** Using one of these options (most commonly, '-local_times') is
very highly recommended.
[-stim_times_millisec]
This option scales all the times in any '-stim_times_*' option by
0.001; the purpose is to allow you to input the times in ms instead
of in s. This factor will be applied to ALL '-stim_times' inputs,
before or after this option on the command line. This factor will
be applied before -stim_times_subtract, so the subtraction value
(if present) must be given in seconds, NOT milliseconds!
[-stim_times_subtract SS]
This option means to subtract 'SS' seconds from each time encountered
in any '-stim_times*' option. The purpose of this option is to make
it simple to adjust timing files for the removal of images from the
start of each imaging run. Note that this option will be useful
only if both of the following are true:
(a) each imaging run has exactly the same number of images removed
(b) the times in the 'tname' files were not already adjusted for
these image removal (i.e., the times refer to the image runs
as acquired, not as input to 3dDeconvolve).
In other words, use this option with understanding and care!
** Note that the subtraction of 'SS' applies to ALL '-stim_times'
inputs, before or after this option on the command line!
** And it applies to global times and local times alike!
** Any time (thus subtracted) below 0 will be ignored, as falling
before the start of the imaging run.
** This option, and the previous one, are simply for convenience, to
help you in setting up your '-stim_times*' timing files from
whatever source you get them.
[-basis_normall a]
Normalize all basis functions for '-stim_times' to have
amplitude 'a' (must have a > 0). The peak absolute value
of each basis function will be scaled to be 'a'.
NOTES:
* -basis_normall only affect -stim_times options that
appear LATER on the command line
* The main use for this option is for use with the
'EXPR' basis functions.
******* General linear test (GLT) options *******
-num_glt num num = number of general linear tests (GLTs)
(0 <= num) [default: num = 0]
**N.B.: You only need this option if you have
more than 10 GLTs specified; the program
has built-in space for 10 GLTs, and
this option is used to expand that space.
If you use this option, you should place
it on the command line BEFORE any of the
other GLT options.
[-glt s gltname] Perform s simultaneous linear tests, as specified
by the matrix contained in file 'gltname'
[-glt_label k glabel] glabel = label for kth general linear test
[-gltsym gltname] Read the GLT with symbolic names from the file
'gltname'; see the document below for details:
http://afni.nimh.nih.gov/pub/dist/doc/misc/Decon/DeconSummer2004.html
******* Options to create 3D+time datasets *******
[-iresp k iprefix] iprefix = prefix of 3D+time output dataset which
will contain the kth estimated impulse response
[-tshift] Use cubic spline interpolation to time shift the
estimated impulse response function, in order to
correct for differences in slice acquisition
times. Note that this effects only the 3D+time
output dataset generated by the -iresp option.
**N.B.: This option only applies to the 'old' style of
deconvolution analysis. Do not use this with
-stim_times analyses!
[-sresp k sprefix] sprefix = prefix of 3D+time output dataset which
will contain the standard deviations of the
kth impulse response function parameters
[-fitts fprefix] fprefix = prefix of 3D+time output dataset which
will contain the (full model) time series fit
to the input data
[-errts eprefix] eprefix = prefix of 3D+time output dataset which
will contain the residual error time series
from the full model fit to the input data
[-TR_times dt]
Use 'dt' as the stepsize for output of -iresp and -sresp file
for response models generated by '-stim_times' options.
Default is same as time spacing in the '-input' 3D+time dataset.
The units here are in seconds!
**** Options to control the contents of the output bucket dataset ****
[-fout] Flag to output the F-statistics for each stimulus
** F tests the null hypothesis that each and every
beta coefficient in the stimulus set is zero
** If there is only 1 stimulus class, then its
'-fout' value is redundant with the Full_Fstat
computed for all stimulus coefficients together.
[-rout] Flag to output the R^2 statistics
[-tout] Flag to output the t-statistics
** t tests a single beta coefficient against zero
** If a stimulus class has only one regressor, then
F = t^2 and the F statistic is redundant with t.
[-vout] Flag to output the sample variance (MSE) map
[-nobout] Flag to suppress output of baseline coefficients
(and associated statistics) [** DEFAULT **]
[-bout] Flag to turn on output of baseline coefs and stats.
** Will make the output dataset larger.
[-nocout] Flag to suppress output of regression coefficients
(and associated statistics)
** Useful if you just want GLT results.
[-full_first] Flag to specify that the full model statistics will
be first in the bucket dataset [** DEFAULT **]
[-nofull_first] Flag to specify that full model statistics go last
[-nofullf_atall] Flag to turn off the full model F statistic
** DEFAULT: the full F is always computed, even if
sub-model partial F's are not ordered with -fout.
[-bucket bprefix] Create one AFNI 'bucket' dataset containing various
parameters of interest, such as the estimated IRF
coefficients, and full model fit statistics.
Output 'bucket' dataset is written to bprefix.
[-nobucket] Don't output a bucket dataset. By default, the
program uses '-bucket Decon' if you don't give
either -bucket or -nobucket on the command line.
[-noFDR] Don't compute the statistic-vs-FDR curves for the
bucket dataset.
[same as 'setenv AFNI_AUTOMATIC_FDR NO']
[-xsave] Flag to save X matrix into file bprefix.xsave
(only works if -bucket option is also given)
[-noxsave] Don't save X matrix [this is the default]
[-cbucket cprefix] Save the regression coefficients (no statistics)
into a dataset named 'cprefix'. This dataset
will be used in a -xrestore run instead of the
bucket dataset, if possible.
** Also, the -cbucket and -x1D output can be combined
in 3dSynthesize to produce 3D+time datasets that
are derived from subsets of the regression model
[generalizing the -fitts option, which produces]
[a 3D+time dataset derived from the full model].
[-xrestore f.xsave] Restore the X matrix, etc. from a previous run
that was saved into file 'f.xsave'. You can
then carry out new -glt tests. When -xrestore
is used, most other command line options are
ignored.
[-float] Write output datasets in float format, instead of
as scaled shorts [** recommended **]
[-short] Write output as scaled shorts [default, for now]
***** The following options control miscellanous outputs *****
[-quiet] Flag to suppress most screen output
[-xout] Flag to write X and inv(X'X) matrices to screen
[-xjpeg filename] Write a JPEG file graphing the X matrix
* If filename ends in '.png', a PNG file is output
[-x1D filename] Save X matrix to a .xmat.1D (ASCII) file [default]
* If 'filename' is 'stdout:', the file is written
to standard output, and could be piped into
1dplot (an example is given earlier).
* This can be used for quick checks to see if your
inputs are setting up a 'reasonable' matrix.
[-nox1D] Don't save X matrix [a bad idea]
[-x1D_uncensored ff Save X matrix to a .xmat.1D file, but WITHOUT
ANY CENSORING. Might be useful in 3dSynthesize.
[-x1D_stop] Stop running after writing .xmat.1D files.
* Useful for testing, or if you are going to
run 3dREMLfit instead -- that is, you are just
using 3dDeconvolve to set up the matrix file.
[-progress n] Write statistical results for every nth voxel
* To let you know that something is happening!
[-fdisp fval] Write statistical results to the screen, for those
voxels whose full model F-statistic is > fval
**** Multiple CPU option (local CPUs only, no networking) ****
-jobs J Run the program with 'J' jobs (sub-processes).
On a multi-CPU machine, this can speed the
program up considerably. On a single CPU
machine, using this option would be silly.
* J should be a number from 1 up to the
number of CPUs sharing memory on the system.
* J=1 is normal (single process) operation.
* The maximum allowed value of J is 32.
* For more information on parallelizing, see
http://afni.nimh.nih.gov/afni/doc/misc/afni_parallelize
* Also use -mask or -automask to get more speed; cf. 3dAutomask.
** NOTE **
This version of the program has been compiled to use
double precision arithmetic for most internal calculations.
++ Compile date = Nov 24 2009
This page auto-generated on
Wed Nov 25 05:23:52 EST 2009