# likelihood_norm: Calculate log likelihoods In ggdmc: Cognitive Models

## Description

These function calculate log likelihoods. `likelihood_rd` implements the equations in Voss, Rothermund, and Voss (2004). These equations calculate diffusion decision model (Ratcliff & Mckoon, 2008). Specifically, this function implements Voss, Rothermund, and Voss's (2004) equations A1 to A4 (page 1217) in C++.

## Usage

 ```1 2 3 4 5 6 7``` ```likelihood_norm(x, data, min_lik = 1e-10) likelihood_norm_pda(x, data, min_lik = 1e-10) likelihood_rd(x, data, min_lik = 1e-10) likelihood_cnorm(x, data, min_lik = 1e-10) ```

## Arguments

 `x` a parameter vector `data` data model instance `min_lik` minimal likelihood.

a vector

## References

Voss, A., Rothermund, K., & Voss, J. (2004). Interpreting the parameters of the diffusion model: An empirical validation. Memory & Cognition, 32(7), 1206-1220.

Ratcliff, R. (1978). A theory of memory retrival. Psychological Review, 85, 238-255.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28``` ```model <- BuildModel( p.map = list(A = "1", B = "1", t0 = "1", mean_v = "M", sd_v = "1", st0 = "1"), match.map = list(M = list(s1 = 1, s2 = 2)), factors = list(S = c("s1", "s2")), constants = c(st0 = 0, sd_v = 1), responses = c("r1", "r2"), type = "norm") p.vector <- c(A = .25, B = .35, t0 = .2, mean_v.true = 1, mean_v.false = .25) dat <- simulate(model, 1e3, ps = p.vector) dmi <- BuildDMI(dat, model) den <- likelihood_norm(p.vector, dmi) ## den <- likelihood_norm_pda(p.vector, dmi) ## This takes more than 1 s, so commented out model <- BuildModel( p.map = list(a = "1", v = "1", z = "1", d = "1", t0 = "1", sv = "1", sz = "1", st0 = "1"), constants = c(st0 = 0, d = 0), match.map = list(M = list(s1 = "r1", s2 = "r2")), factors = list(S = c("s1", "s2")), responses = c("r1", "r2"), type = "rd") p.vector <- c(a = 1, v = 1, z = 0.5, sz = 0.25, sv = 0.2, t0 = .15) dat <- simulate(model, 1e2, ps = p.vector) dmi <- BuildDMI(dat, model) den <- likelihood_rd(p.vector, dmi) ```

ggdmc documentation built on Sept. 2, 2018, 1:03 a.m.