template<bool propto = false, typename T_y , typename T_a , typename T_t0 , typename T_w , typename T_v , typename T_sv , typename T_sw , typename T_st0 >
| auto stan::math::wiener_lcdf_defective |
( |
const T_y & |
y, |
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const T_a & |
a, |
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const T_t0 & |
t0, |
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const T_w & |
w, |
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const T_v & |
v, |
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const T_sv & |
sv, |
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const T_sw & |
sw, |
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const T_st0 & |
st0, |
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const double & |
precision_derivatives = 1e-8 |
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) |
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inline |
Returns the log CDF of the Wiener distribution for a (Wiener) drift diffusion model with up to 7 parameters.
If containers are supplied, returns the log sum of the probabilities. See 'wiener_lpdf' for more comprehensive documentation If the reaction time goes to infinity, the CDF goes to the probability to hit the upper bound (instead of 1, as it is usually the case)
- Template Parameters
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| T_y | type of reaction time |
| T_a | type of boundary separation |
| T_t0 | type of non-decision time |
| T_w | type of relative starting point |
| T_v | type of drift rate |
| T_sv | type of inter-trial variability of drift rate |
| T_sw | type of inter-trial variability of relative starting point |
| T_st0 | type of inter-trial variability of non-decision time |
- Parameters
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| y | The reaction time in seconds |
| a | The boundary separation |
| t0 | The non-decision time |
| w | The relative starting point |
| v | The drift rate |
| sv | The inter-trial variability of the drift rate |
| sw | The inter-trial variability of the relative starting point |
| st0 | The inter-trial variability of the non-decision time |
| precision_derivatives | Level of precision in estimation of partial derivatives |
- Returns
- log probability or log sum of probabilities for upper boundary responses
- Exceptions
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| std::domain_error | if non-decision time t0 is greater than reaction time y. |
| std::domain_error | if 1-sw/2 is smaller than or equal to w. |
| std::domain_error | if sw/2 is larger than or equal to w. |
References**
- 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. https://doi.org/10.1016/j.jmp.2016.11.003
- Foster, K., & Singmann, H. (2021). Another Approximation of the First-Passage Time Densities for the Ratcliff Diffusion Decision Model. arXiv preprint arXiv:2104.01902
- 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. https://doi.org/10.1016/j.jmp.2014.05.002
- Hartmann, R., & Klauer, K. C. (2021). Partial derivatives for the first-passage time distribution in Wiener diffusion models. Journal of Mathematical Psychology, 103, 102550. https://doi.org/10.1016/j.jmp.2021.102550
- Henrich, F., Hartmann, R., Pratz, V., Voss, A., & Klauer, K.C. (2023). The Seven-parameter Diffusion Model: An Implementation in Stan for Bayesian Analyses. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02179-1
- Henrich, F., & Klauer, K. C. (in press). Modeling Truncated and Censored Data With the Diffusion Model in Stan. Behavior Research Methods.
- Linhart, J. M. (2008). Algorithm 885: Computing the Logarithm of the Normal Distribution. ACM Transactions on Mathematical Software. http://doi.acm.org/10.1145/1391989.1391993
- 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. https://doi.org/10.1016/j.jmp.2009.02.003
Definition at line 263 of file wiener_full_lcdf_defective.hpp.
