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- checking for file ‘ream/DESCRIPTION’ ... OK
- this is package ‘ream’ version ‘1.0-1’
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- checking whether package ‘ream’ can be installed ... [5s/7s] OK
See the install log for details.
- used C compiler: ‘Apple clang version 14.0.0 (clang-1400.0.29.202)’
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- checking installed files from ‘inst/doc’ ... OK
- checking files in ‘vignettes’ ... OK
- checking examples ... [0s/0s] ERROR
Running examples in ‘ream-Ex.R’ failed
The error most likely occurred in:
> ### Name: CDSTP
> ### Title: Continuous Dual-Stage Two-Phase Model of Selective Attention
> ### Aliases: CDSTP dCDSTP pCDSTP rCDSTP
>
> ### ** Examples
>
> # Probability density function
> dCDSTP(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
+ phi = c(0.3, 0.5, 0.5, -0.5, -1.0, -0.5, 8.0, 4.0, 1.0, 2.0, 1.3, 1.3, 0.0, 0.0, 1.0))
- checking for unstated dependencies in vignettes ... OK
- checking package vignettes in ‘inst/doc’ ... OK
- checking running R code from vignettes ... [0s/0s] ERROR
Errors in running code in vignettes:
when running code in ‘guidline.Rmd’
...
# Random sampling
rDMC(n = 100, phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
> (samp <- rDMC(n = 10, phi = c(0.3, 0.5, -1, 0.2, 0.05,
+ 2.5, 3, 1, 0.5, 0, 0, 1), dt = 1e-04))
... incomplete output. Crash?
‘guidline.Rmd’ using ‘UTF-8’... failed to complete the test
- checking re-building of vignette outputs ... [6s/8s] NOTE
Error(s) in re-building vignettes:
--- re-building ‘guidline.Rmd’ using rmarkdown
DMC package:ream R Documentation
_ D_ i_ f_ f_ u_ s_ i_ o_ n _ M_ o_ d_ e_ l _ f_ o_ r _ C_ o_ n_ f_ l_ i_ c_ t _ T_ a_ s_ k_ s
_ D_ e_ s_ c_ r_ i_ p_ t_ i_ o_ n:
The DMC is a two-process evidence accumulation model for the study
of conflict tasks. It sums together a controlled and an automatic
process to generate a single accumulator for generating the
likelihood function. This accumulator has the same parameters as
the SDDM with the exception of the drift rate, given by
v(x,t) = s*A*exp(-t/tau)*[e*t/(tau*(alpha-1))]^{alpha-1}*[(alpha-1)/t - 1/tau] + mu_c.
_ U_ s_ a_ g_ e:
dDMC(rt, resp, phi, x_res = "default", t_res = "default")
pDMC(rt, resp, phi, x_res = "default", t_res = "default")
rDMC(n, phi, dt = 1e-05)
_ A_ r_ g_ u_ m_ e_ n_ t_ s:
rt: vector of response times
resp: vector of responses ("upper" and "lower")
phi: parameter vector in the following order:
1. Non-decision time (t_{nd}). Time for non-decision
processes such as stimulus encoding and response
execution. Total decision time t is the sum of the
decision and non-decision times.
2. Relative start (w). Sets the start point of accumulation
as a ratio of the two decision thresholds. Related to the
absolute start z point via equation z = b_l + w*(b_u -
b_l).
3. Coherence parameter (s). Sets stimulus coherence. If s =
1, coherent condition; if s = 0, neutral condition; if s
= -1, incoherent condition.
4. Automatic process amplitude (A). Max value of automatic
process.
5. Scale parameter (tau). Contributes to time automatic
process. Time to max t_{max} = (alpha – 1)*tau.
6. Shape parameter (alpha). Indicates the shape of the
automatic process. Must have value more than 1 (alpha >
1).
7. Drift rate of the controlled process (mu_c).
8. Noise scale (sigma). Model noise scale parameter.
9. Decision thresholds (b). Sets the location of each
decision threshold. The upper threshold b_u is above 0
and the lower threshold b_l is below 0 such that b_u =
-b_l = b. The threshold separation a = 2b.
10. Contamination (g). Sets the strength of the contamination
process. Contamination process is a uniform distribution
f_c(t) where f_c(t) = 1/(g_u-g_l) if g_l <= t <= g_u and
f_c(t) = 0 if t < g_l or t > g_u. It is combined with PDF
f_i(t) to give the final combined distribution f_{i,c}(t)
= g*f_c(t) + (1-g)*f_i(t), which is then output by the
program. If g = 0, it just outputs f_i(t).
11. Lower bound of contamination distribution (g_l). See
parameter g.
12. Upper bound of contamination distribution (g_u). See
parameter g.
x_res: spatial/evidence resolution
t_res: time resolution
n: number of samples
dt: step size of time. We recommend 0.00001 (1e-5)
_ V_ a_ l_ u_ e:
For the density a list of PDF values, log-PDF values, and the sum
of the log-PDFs, for the distribution function a list of of CDF
values, log-CDF values, and the sum of the log-CDFs, and for the
random sampler a list of response times (rt) and response
thresholds (resp).
_ A_ u_ t_ h_ o_ r(_ s):
Raphael Hartmann & Matthew Murrow
_ R_ e_ f_ e_ r_ e_ n_ c_ e_ s:
Ulrich, R., Schröter, H., Leuthold, H., & Birngruber, T. (2015).
Automatic and controlled stimulus processing in conflict tasks:
Superimposed diffusion processes and delta functions. _Cognitive
psychology, 78_, 148-174.
_ E_ x_ a_ m_ p_ l_ e_ s:
# Probability density function
dDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Cumulative distribution function
pDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Random sampling
rDMC(n = 100, phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
- checking PDF version of manual ... [5s/5s] OK
- DONE
Status: 2 ERRORs, 1 NOTE
- using check arguments '--no-clean-on-error '