dfddm: Density of Ratcliff Diffusion Decision Model
In fddm: Fast Implementation of the Diffusion Decision Model
dfddm R Documentation
Density of Ratcliff Diffusion Decision Model
Description
Density function for the Ratcliff diffusion decision model (DDM) with
following parameters: v (drift rate), a (threshold
separation), t0 (non-decision time/response time constant), w
(relative starting point), sv (inter-trial variability of drift), and
sigma (diffusion coefficient of underlying Wiener process). If you
are looking to fit the DDM, see ddm().
Usage
dfddm(
rt,
response,
v,
a,
t0,
w = 0.5,
sv = 0,
sigma = 1,
err_tol = 1e-06,
log = FALSE,
switch_mech = "eff_rt",
switch_thresh = 0.8,
n_terms_small = "SWSE",
summation_small = "2017"
)
Arguments
rt
A vector of response times (in seconds). If a response time is
non-positve, then its density will evaluate to 0 if log = FALSE and
-∞ if log = TRUE.
response
Binary response(s) that correspond(s) to either the "lower"
or "upper" threshold. This model parameter can either be a singular value
or a vector. The value(s) in 'response' can be of the following data
types:
integers or doubles (1
→ "lower", 2
→ "upper");
factors (the first level gets mapped to "lower", and the second
level gets mapped to "upper"; any additional levels are ignored).
strings (only the first character is checked, "L"
→ "lower" or "U"
→ "upper", case insensitive);
logicals (FALSE → "lower",
TRUE → "upper");
v
Drift rate. Average slope of the information accumulation process.
The drift gives information about the speed and direction of the
accumulation of information. Large (absolute) values of drift indicate a
good performance. If received information supports the response linked to
the upper threshold, then the sign will be positive; similarly a negative
value indicates that the received information supports the response linked
to the lower threshold. Allowed range: v is a real number. Typical
range: -5 < v < 5.
a
Threshold separation. Amount of information that is considered for
a decision. Large values indicate a conservative decisional style. Allowed
range: 0 < a. Typical range: 0.5 < a < 2.
t0
Non-decision time or response time constant (in seconds). Lower
bound for the duration of all non-decisional processes (encoding and
response execution). If this value is greater than rt, then the
resulting density is returned as if rt \le 0. Allowed range:
0 \le t0. Typical range: 0.1 < t0 < 0.5.
w
Relative starting point. Indicator of an a priori bias in decision
making. When the relative starting point w deviates from 0.5,
the amount of information necessary for a decision differs between
response alternatives. Allowed range: 0 < w < 1.
Default value is 0.5 (i.e., no bias).
sv
Inter-trial-variability of drift rate. Standard deviation of a
normal distribution with mean v describing the distribution of
actual drift rates from specific trials. Values different from 0 can
predict slow errors. Allowed range: 0 \le sv. Typical range:
0 < sv < 2. Default value is 0, which indicates
no drift in the function call. See Details for more information.
sigma
Diffusion coefficient of the underlying Wiener process. Allowed
range: 0 < sigma. Default value is 1. This parameter
simply scales the parameters a, v, and sv as follows.
See Details for more information.
-
a
→ a / sigma
-
v → v /
sigma
-
sv →
sv / sigma
.
err_tol
Allowed error tolerance of the density function. The density
function contains an infinite sum that must be approximated; this
parameter is the upper bound of the total error incurred from this
approximation (in absolute value). See Details for more information.
Default is 1e-6.
log
Logical; if TRUE, probabilities p are given as
log(p). Default is FALSE, which gives the density on the
probability scale.
switch_mech
Which switching mechanism to use in the choice of the
"large-time" or "small-time" density function. Can be one of
{"eff_rt", "terms_large", "terms", "small",
"large"}. Note that the large-time approximation is unstable for
small effective response times ((rt-t0)
/(a*a) < 0.009). See Details for more
information. Default is "eff_rt".
switch_thresh
Threshold for determining whether the effective
response time ((rt-t0)
/(a*a)) is "large" or "small". This
parameter is only considered if switch_mech = "eff_rt" or
switch_mech = "terms_large".
If switch_mech = "eff_rt", an effective response time greater than
switch_thresh is considered "large", and the "large-time" variant
of the density function is used; otherwise, the "small-time" variant of
the density function is used. The default is 0.8.
If switch_mech = "terms_large", this parameter is treated as
ceiling(switch_thresh); the smallest integer that is
not less than switch_thresh. In this case, the default is
ceiling(0.8) = 1. See the switch_mech section of Details for
more information.
Note that if switch_thresh \le 0, then the effective response
time is always treated as "large"; contrarily, if switch_thresh =
∞ then the effective response
time is always treated as "small". However, it is better to simply set
switch_mech = "large" or switch_mech = "small" to always use
the "large-time" or "small-time" variant, respectively.
n_terms_small
Which method to use for calculating the "small-time"
approximation to the density function. Only applicable if
switch_mech = "terms" or switch_mech = "small"; all other
values of switch_mech cause this parameter to be ignored. If
switch_mech = "small", the allowed values are "SWSE",
"Gondan", and "Navarro". The default value is "SWSE".
If switch_mech = "terms", the allowed values are "Gondan"
and "Navarro". Note that if the user inputs
switch_mech = "terms", then the user must also explicitly input
either n_terms_small = "Gondan" or
n_terms_small = "Navarro". See Details for more information.
summation_small
Which style of summation to use for the small-time
approximation to the infinite sum. Can be one of {"2017",
"2014"}. Only applicable if switch_mech is one of
{"eff_rt", "terms_large", "terms", "small"}.
See Details for more information. Default is "2017".
Details
All of the model inputs and parameters (rt, response,
v, a, t0, w, sv, sigma) can be
input as a single value or as a vector of values. If input as a vector of
values, then the standard R recycling rules apply to ensure all
inputs are of the same length.
The default settings of switch_mech = "eff_rt",
switch_thresh = "0.8", n_terms_small = "SWSE",
summation_small = "2017" produce the fastest and most accurate
results, as shown in our associated paper.
sv - Both the "small-time" and "large-time" variants of the density
function have two further variants: one with a constant drift rate v
(i.e., sv = 0), and one with a variable drift rate v
(i.e., sv > 0). The details of the differences between these
two density functions can be found in our associated paper. To use the
density function with a constant drift rate, leave the parameter sv
to its default value of sv = 0, as this will indicate no drift to the
function. To use the density function with a variable drift rate, set the
parameter sv to some non-negative value, i.e., sv > 0.
sigma - The default value of this parameter is 1 because it
only scales the parameters v, a, and sv, as shown
above. However, other formulations of the DDM may set sigma = 0.1
(see Ratcliff (1978), the fourth reference), so care must be taken when
comparing the results of different formulations.
err_tol - The density function is composed of an infinite sum (that
must be approximated) and a multiplicative term outside the infinite sum,
m. The total error of the approximation is the error incurred from
truncating the infinite sum multiplied by m. Thus, to ensure that the
total error is bounded by the user-provided error tolerance, err_tol,
the approximation to the infinite sum uses a modified error tolerance
(err_tol / m). If the error tolerance is small and m is
large, the modified error tolerance can underflow to 0. To protect
against this, we check that the modified error tolerance is at least
1e-300 (near the smallest value that is representable by a floating
point number with double precision). If the modified error tolerance is
smaller than this threshold, then we silently change it to 1e-300.
This case should only be encountered if extreme values of error tolerance
are used (i.e., on the scale of 1e-300).
switch_mech - The density function for the DDM has traditionally been
written in two forms: a "large-time" variant, and a "small-time" variant
(Navarro and Fuss, 2009). These two forms are more
efficient at calculating the density for large and small response times,
respectively. The parameter switch_mech determines how dfddm
decides which of these two variants is used.
switch_mech = "small" uses
only the "small-time" variant, and switch_mech = "large" uses only
the "large-time" variant. The "large-time" variant is unstable for small
effective response times ((rt-t0) /
(a*a) < 0.009) and may produce inaccurate
densities; thus, we do not recommend using the switch_mech = "large"
option if the inputs may contain such small effective response times. To
circumvent this accuracy issue and resolve the differing efficiencies of the
"large-time" and "small-time" variants, there are three switching mechanisms
that can be used to determine which of the two variants is more efficient.
First, switch_mech = "terms" is the traditional approach and
pre-calculates the number of terms required for the "large-time" and
"small-time" sums, and then uses whichever variant requires fewer terms.
This is the mechanism used in the Navarro and Fuss (2009) and Gondan,
Blurton, and Kesselmeier (2014) papers.
Second, switch_mech = "terms_large" pre-calculates the number of
terms only for the "large-time" variant, and compares that to the constant
value of ceil(switch_thresh) (default value of
ceil(0.8) = 1) to determine if the "large-time" variant is
sufficiently efficient.
Third, switch_mech = "eff_rt" determines whether a given effective
response time ((rt-t0)
/(a*a)) is considered "large" or "small" by
comparing it to the value of the parameter switch_thresh (default
value of 0.8); it then uses the corresponding variant.
Both switch_mech = "terms_large" and switch_mech = "eff_rt"
only use the SWSE "small-time" approximation in the case that the
"small-time" variant is more efficient. switch_mech = "eff_rt" is the
most efficient method to determine which variant of the density function
should be used.
switch_thresh - This parameter determines what effective response
times (rt/(a *a)) are "large" and
"small". The paper_analysis folder in the fddm GitHub
repository contains plots showing the relative efficiencies of a range of
values for the switch_thresh parameter when used with
switch_mech = "eff_rt" and also switch_mech = "terms_large".
Note that this parameter changed name and purpose with the release of
fddm version 0.5-0.
n_terms_small - The "small-time" variant has three different methods
for how to truncate the infinite sum in the density function. These
different methods are discussed extensively in our associated paper, but the
key distinction is that n_terms_small = "SWSE" uses a new method of
truncating the infinite sum. The n_terms_small = "SWSE" method is
currently recommended (when possible) because it is the fastest and most
stable algorithm when used with switch_mech = "eff_rt".
summation_small - The "large-time" variant of the density function
does not have any further variants, but the "small-time" variant has more
options with respect to evaluating the infinite sum. There are two
equivalent styles of summation, summation_small = "2017" and
summation_small = "2014", of which the "2017" version
evaluates slightly faster and thus earns our recommendation. These different
styles of summation are discussed in our associated paper.
Value
A vector containing the densities of the DDM with precision
err_tol whose length matches that of the longest input parameter
(usually rt).
References
Navarro, D. J., & Fuss, I. G. (2009). Fast and accurate
calculations for first-passage times in Wiener diffusion models. Journal
of Mathematical Psychology, 53(4), 222-230.
Gondan, M., Blurton, S. P., & Kesselmeier, M. (2014). Even faster and even
more accurate first-passage time densities and distributions for the
Wiener diffusion model. Journal of Mathematical Psychology, 60, 20-22.
Blurton, S. P., Kesselmeier, M., & Gondan, M. (2017). The first-passage
time distribution for the diffusion model with variable drift. Journal of
Mathematical Psychology, 76, 7-12.
Ratcliff, R. (1978). A theory of memory retrieval. Psychological review,
85(2), 59.
See Also
ddm()
Examples
# minimal example
dfddm(rt = 1.2, response = "lower", a = 1, v = -1, t0 = 0.3)
# example with all function parameters set to default or a practical value
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3),
response = c("lower", "upper", "upper", "lower", "upper", "lower"),
a = 1, v = -1, t0 = 0.2, w = 0.5, sv = 0, sigma = 1,
err_tol = 1e-6, log = FALSE, switch_mech = "eff_rt", switch_thresh = 0.8,
n_terms_small = "SWSE", summation_small = "2017")
# example of mismatched input lengths
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3),
response = c("lower", "upper", "upper", "lower", "upper", "lower"),
a = c(1, 3), v = c(-2, 2, 2, -2, 2, -2),
t0 = 0.3, w = c(0.4, 0.5, 0.6), sv = 0.9,
err_tol = 1e-10, log = FALSE, switch_mech = "large",
summation_small = "2017")
# example with Wiener diffusion coefficient (sigma) not equal to 1
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3),
response = c("lower", "upper", "upper", "lower", "upper", "lower"),
a = 1, v = -1, t0 = 0.3, w = 0.5, sv = 0, sigma = 0.1,
err_tol = 1e-10, log = TRUE, switch_mech = "terms_large",
switch_thresh = 1, summation_small = "2017")
### examples of different response inputs
# integer
resp_int <- as.integer(c(1, 2, 2, 1, 2, 1))
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_int,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
# double
resp_dbl <- as.double(c(1, 2, 2, 1, 2, 1))
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_dbl,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
# factor (first level is mapped to "lower")
days <- c("Monday", "Friday", "Friday", "Monday", "Friday", "Monday")
resp_fac <- factor(days, levels = c("Monday", "Friday"))
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_fac,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
# string
resp_str <- c("lower", "upper", "upper", "lower", "upper", "lower")
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_str,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
# logical
resp_log <- c(FALSE, TRUE, TRUE, FALSE, TRUE, FALSE)
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_log,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
fddm documentation built on July 2, 2024, 5:06 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| dfddm | R Documentation |
Density of Ratcliff Diffusion Decision Model
Description
Density function for the Ratcliff diffusion decision model (DDM) with
following parameters: v (drift rate), a (threshold
separation), t0 (non-decision time/response time constant), w
(relative starting point), sv (inter-trial variability of drift), and
sigma (diffusion coefficient of underlying Wiener process). If you
are looking to fit the DDM, see ddm().
Usage
dfddm(
rt,
response,
v,
a,
t0,
w = 0.5,
sv = 0,
sigma = 1,
err_tol = 1e-06,
log = FALSE,
switch_mech = "eff_rt",
switch_thresh = 0.8,
n_terms_small = "SWSE",
summation_small = "2017"
)
Arguments
rt |
A vector of response times (in seconds). If a response time is
non-positve, then its density will evaluate to |
response |
Binary response(s) that correspond(s) to either the "lower" or "upper" threshold. This model parameter can either be a singular value or a vector. The value(s) in 'response' can be of the following data types:
|
v |
Drift rate. Average slope of the information accumulation process.
The drift gives information about the speed and direction of the
accumulation of information. Large (absolute) values of drift indicate a
good performance. If received information supports the response linked to
the upper threshold, then the sign will be positive; similarly a negative
value indicates that the received information supports the response linked
to the lower threshold. Allowed range: |
a |
Threshold separation. Amount of information that is considered for
a decision. Large values indicate a conservative decisional style. Allowed
range: |
t0 |
Non-decision time or response time constant (in seconds). Lower
bound for the duration of all non-decisional processes (encoding and
response execution). If this value is greater than |
w |
Relative starting point. Indicator of an a priori bias in decision
making. When the relative starting point |
sv |
Inter-trial-variability of drift rate. Standard deviation of a
normal distribution with mean |
sigma |
Diffusion coefficient of the underlying Wiener process. Allowed
range:
. |
err_tol |
Allowed error tolerance of the density function. The density
function contains an infinite sum that must be approximated; this
parameter is the upper bound of the total error incurred from this
approximation (in absolute value). See Details for more information.
Default is |
log |
Logical; if |
switch_mech |
Which switching mechanism to use in the choice of the
"large-time" or "small-time" density function. Can be one of
{ |
switch_thresh |
Threshold for determining whether the effective
response time ( |
n_terms_small |
Which method to use for calculating the "small-time"
approximation to the density function. Only applicable if
|
summation_small |
Which style of summation to use for the small-time
approximation to the infinite sum. Can be one of { |
Details
All of the model inputs and parameters (rt, response,
v, a, t0, w, sv, sigma) can be
input as a single value or as a vector of values. If input as a vector of
values, then the standard R recycling rules apply to ensure all
inputs are of the same length.
The default settings of switch_mech = "eff_rt",
switch_thresh = "0.8", n_terms_small = "SWSE",
summation_small = "2017" produce the fastest and most accurate
results, as shown in our associated paper.
sv - Both the "small-time" and "large-time" variants of the density
function have two further variants: one with a constant drift rate v
(i.e., sv = 0), and one with a variable drift rate v
(i.e., sv > 0). The details of the differences between these
two density functions can be found in our associated paper. To use the
density function with a constant drift rate, leave the parameter sv
to its default value of sv = 0, as this will indicate no drift to the
function. To use the density function with a variable drift rate, set the
parameter sv to some non-negative value, i.e., sv > 0.
sigma - The default value of this parameter is 1 because it
only scales the parameters v, a, and sv, as shown
above. However, other formulations of the DDM may set sigma = 0.1
(see Ratcliff (1978), the fourth reference), so care must be taken when
comparing the results of different formulations.
err_tol - The density function is composed of an infinite sum (that
must be approximated) and a multiplicative term outside the infinite sum,
m. The total error of the approximation is the error incurred from
truncating the infinite sum multiplied by m. Thus, to ensure that the
total error is bounded by the user-provided error tolerance, err_tol,
the approximation to the infinite sum uses a modified error tolerance
(err_tol / m). If the error tolerance is small and m is
large, the modified error tolerance can underflow to 0. To protect
against this, we check that the modified error tolerance is at least
1e-300 (near the smallest value that is representable by a floating
point number with double precision). If the modified error tolerance is
smaller than this threshold, then we silently change it to 1e-300.
This case should only be encountered if extreme values of error tolerance
are used (i.e., on the scale of 1e-300).
switch_mech - The density function for the DDM has traditionally been
written in two forms: a "large-time" variant, and a "small-time" variant
(Navarro and Fuss, 2009). These two forms are more
efficient at calculating the density for large and small response times,
respectively. The parameter switch_mech determines how dfddm
decides which of these two variants is used.
switch_mech = "small" uses
only the "small-time" variant, and switch_mech = "large" uses only
the "large-time" variant. The "large-time" variant is unstable for small
effective response times ((rt-t0) /
(a*a) < 0.009) and may produce inaccurate
densities; thus, we do not recommend using the switch_mech = "large"
option if the inputs may contain such small effective response times. To
circumvent this accuracy issue and resolve the differing efficiencies of the
"large-time" and "small-time" variants, there are three switching mechanisms
that can be used to determine which of the two variants is more efficient.
First, switch_mech = "terms" is the traditional approach and
pre-calculates the number of terms required for the "large-time" and
"small-time" sums, and then uses whichever variant requires fewer terms.
This is the mechanism used in the Navarro and Fuss (2009) and Gondan,
Blurton, and Kesselmeier (2014) papers.
Second, switch_mech = "terms_large" pre-calculates the number of
terms only for the "large-time" variant, and compares that to the constant
value of ceil(switch_thresh) (default value of
ceil(0.8) = 1) to determine if the "large-time" variant is
sufficiently efficient.
Third, switch_mech = "eff_rt" determines whether a given effective
response time ((rt-t0)
/(a*a)) is considered "large" or "small" by
comparing it to the value of the parameter switch_thresh (default
value of 0.8); it then uses the corresponding variant.
Both switch_mech = "terms_large" and switch_mech = "eff_rt"
only use the SWSE "small-time" approximation in the case that the
"small-time" variant is more efficient. switch_mech = "eff_rt" is the
most efficient method to determine which variant of the density function
should be used.
switch_thresh - This parameter determines what effective response
times (rt/(a *a)) are "large" and
"small". The paper_analysis folder in the fddm GitHub
repository contains plots showing the relative efficiencies of a range of
values for the switch_thresh parameter when used with
switch_mech = "eff_rt" and also switch_mech = "terms_large".
Note that this parameter changed name and purpose with the release of
fddm version 0.5-0.
n_terms_small - The "small-time" variant has three different methods
for how to truncate the infinite sum in the density function. These
different methods are discussed extensively in our associated paper, but the
key distinction is that n_terms_small = "SWSE" uses a new method of
truncating the infinite sum. The n_terms_small = "SWSE" method is
currently recommended (when possible) because it is the fastest and most
stable algorithm when used with switch_mech = "eff_rt".
summation_small - The "large-time" variant of the density function
does not have any further variants, but the "small-time" variant has more
options with respect to evaluating the infinite sum. There are two
equivalent styles of summation, summation_small = "2017" and
summation_small = "2014", of which the "2017" version
evaluates slightly faster and thus earns our recommendation. These different
styles of summation are discussed in our associated paper.
Value
A vector containing the densities of the DDM with precision
err_tol whose length matches that of the longest input parameter
(usually rt).
References
Navarro, D. J., & Fuss, I. G. (2009). Fast and accurate calculations for first-passage times in Wiener diffusion models. Journal of Mathematical Psychology, 53(4), 222-230.
Gondan, M., Blurton, S. P., & Kesselmeier, M. (2014). Even faster and even more accurate first-passage time densities and distributions for the Wiener diffusion model. Journal of Mathematical Psychology, 60, 20-22.
Blurton, S. P., Kesselmeier, M., & Gondan, M. (2017). The first-passage time distribution for the diffusion model with variable drift. Journal of Mathematical Psychology, 76, 7-12.
Ratcliff, R. (1978). A theory of memory retrieval. Psychological review, 85(2), 59.
See Also
ddm()
Examples
# minimal example
dfddm(rt = 1.2, response = "lower", a = 1, v = -1, t0 = 0.3)
# example with all function parameters set to default or a practical value
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3),
response = c("lower", "upper", "upper", "lower", "upper", "lower"),
a = 1, v = -1, t0 = 0.2, w = 0.5, sv = 0, sigma = 1,
err_tol = 1e-6, log = FALSE, switch_mech = "eff_rt", switch_thresh = 0.8,
n_terms_small = "SWSE", summation_small = "2017")
# example of mismatched input lengths
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3),
response = c("lower", "upper", "upper", "lower", "upper", "lower"),
a = c(1, 3), v = c(-2, 2, 2, -2, 2, -2),
t0 = 0.3, w = c(0.4, 0.5, 0.6), sv = 0.9,
err_tol = 1e-10, log = FALSE, switch_mech = "large",
summation_small = "2017")
# example with Wiener diffusion coefficient (sigma) not equal to 1
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3),
response = c("lower", "upper", "upper", "lower", "upper", "lower"),
a = 1, v = -1, t0 = 0.3, w = 0.5, sv = 0, sigma = 0.1,
err_tol = 1e-10, log = TRUE, switch_mech = "terms_large",
switch_thresh = 1, summation_small = "2017")
### examples of different response inputs
# integer
resp_int <- as.integer(c(1, 2, 2, 1, 2, 1))
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_int,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
# double
resp_dbl <- as.double(c(1, 2, 2, 1, 2, 1))
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_dbl,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
# factor (first level is mapped to "lower")
days <- c("Monday", "Friday", "Friday", "Monday", "Friday", "Monday")
resp_fac <- factor(days, levels = c("Monday", "Friday"))
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_fac,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
# string
resp_str <- c("lower", "upper", "upper", "lower", "upper", "lower")
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_str,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
# logical
resp_log <- c(FALSE, TRUE, TRUE, FALSE, TRUE, FALSE)
dfddm(rt = c(1.2, 0.9, 1.1, 1.4, 0.8, 1.3), response = resp_log,
a = 1, v = -2, t0 = 0.3, w = 0.5, sv = 0.1,
err_tol = 1e-10, log = FALSE, switch_mech = "eff_rt",
summation_small = "2017")
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.