Package 'ggdmc'

Description

Binding a data set with a model object. The function also checks whether they are compatible and adds attributes on a data model instance.

Usage

BuildDMI(x, model)

Arguments

Value

a data model instance

Description

A model object consists of arraies with model attributes.

Usage

BuildModel(p.map, responses, factors = list(A = "1"), match.map = NULL,
  constants = numeric(0), type = "norm", posdrift = TRUE,
  verbose = TRUE)

## S3 method for class 'model'
print(x, p.vector = NULL, ...)

## S3 method for class 'dmi'
print(x, ...)

Arguments

Examples

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, sz = 0, sv = 0),
        match.map = list(M = list(s1 = "r1", s2 = "r2")),
        factors   = list(S = c("s1", "s2")),
        responses = c("r1", "r2"),
        type      = "rd")

Description

BuildPrior sets up parameter prior distributions for each model parameter. p1 and p2 refer to the first and second parameters a prior distribution.

Usage

BuildPrior(p1, p2, lower = rep(NA, length(p1)), upper = rep(NA,
  length(p1)), dists = rep("tnorm", length(p1)),
  untrans = rep("identity", length(p1)), types = c("tnorm", "beta",
  "gamma", "lnorm", "unif", "constant", "tnorm2", NA))

Arguments

Details

Four distribution types are implemented:

1. Normal and truncated normal, where: p1 = mean, p2 = sd. It specifies a normal distribution when bounds are set -Inf and Inf,

2. Beta, where: p1 = shape1 and p2 = shape2 (see pbeta). Note the uniform distribution is a special case of the beta with p1 and p2 = 1),

3. Gamma, where p1 = shape and p2 = scale (see pgamma). Note p2 is scale, not rate,

4. Lognormal, where p1 = meanlog and p2 = sdlog (see plnorm).

Value

a list of list

Description

Check a parameter vector

Usage

check_pvec(p.vector, model)

Arguments

Description

Convert MCMC chains to a data frame for plotting functions

Usage

ConvertChains(x, start = 1, end = NA, pll = TRUE)

Arguments

Description

A modified dbeta function

Usage

dbeta_lu(x, p1, p2, lower, upper, lg = FALSE)

Arguments

Description

A modified dcauchy functions

Usage

dcauchy_l(x, p1, p2, lg = FALSE)

Arguments

Description

Used with constant prior

Usage

dconstant(x, p1, p2, lower, upper, lg = FALSE)

Arguments

Description

Calculate deviance for a model object for which a log-likelihood value can be obtained, according to the formula -2*log-likelihood.

Usage

## S3 method for class 'model'
deviance(object, ...)

Arguments

Description

A modified dgamma function

Usage

dgamma_l(x, p1, p2, lower, upper, lg = FALSE)

Arguments

Description

Calculate DIC and BPIC.

Usage

DIC(object, ...)

BPIC(object, ...)

Arguments

Description

A modified dlnorm functions

Usage

dlnorm_l(x, p1, p2, lower, upper, lg = FALSE)

Arguments

Description

Random number generation, probability density and cumulative density functions for truncated normal distribution.

Usage

dtnorm(x, p1, p2, lower, upper, lg = FALSE)

rtnorm(n, p1, p2, lower, upper)

ptnorm(q, p1, p2, lower, upper, lt = TRUE, lg = FALSE)

Arguments

Value

a column vector.

Examples

## rtn example
dat1 <- rtnorm(1e5, 0, 1, 0, Inf)
hist(dat1, breaks = "fd", freq = FALSE, xlab = "",
     main = "Truncated normal distributions")

## dtn example
x <- seq(-5, 5, length.out = 1e3)
dat1 <- dtnorm(x, 0, 1, -2, 2, 0)
plot(x, dat1, type = "l", lwd = 2, xlab = "", ylab= "Density",
     main = "Truncated normal distributions")

## ptn example
x <- seq(-10, 10, length.out = 1e2)
mean <- 0
sd <- 1
lower <- 0
upper <- 5
dat1 <- ptnorm(x, 0, 1, 0, 5, lg = TRUE)

Description

effectiveSize calls effectiveSize in coda package to calculate sample sizes.

Usage

effectiveSize_hyper(x, start, end, digits, verbose)

effectiveSize_many(x, start, end, verbose)

effectiveSize_one(x, start, end, digits, verbose)

effectiveSize(x, hyper = FALSE, start = 1, end = NA, digits = 0,
  verbose = FALSE)

Arguments

Examples

#################################40
## effectiveSize example
#################################40
## Not run: 
es1 <- effectiveSize_one(hsam[[1]], 1, 100, 2, TRUE)
es2 <- effectiveSize_one(hsam[[1]], 1, 100, 2, FALSE)
es3 <- effectiveSize_many(hsam, 1, 100, TRUE)
es4 <- effectiveSize_many(hsam, 1, 100, FALSE)
es5 <- effectiveSize_hyper(hsam, 1, 100, 2, TRUE)
es6 <- effectiveSize(hsam, TRUE, 1, 100, 2, TRUE)
es7 <- effectiveSize(hsam, TRUE, 1, 100, 2, FALSE)
es8 <- effectiveSize(hsam, FALSE, 1, 100, 2, TRUE)
es9 <- effectiveSize(hsam, FALSE, 1, 100, 2, FALSE)
es10 <- effectiveSize(hsam[[1]], FALSE, 1, 100, 2, TRUE)

## End(Not run)

Description

gelman function calls the function, gelman.diag in the coda package to calculates PSRF.

Usage

gelman(x, hyper = FALSE, start = 1, end = NA, confidence = 0.95,
  transform = TRUE, autoburnin = FALSE, multivariate = TRUE,
  split = TRUE, subchain = FALSE, nsubchain = 3, digits = 2,
  verbose = FALSE, ...)

hgelman(x, start = 1, end = NA, confidence = 0.95,
  transform = TRUE, autoburnin = FALSE, split = TRUE,
  subchain = FALSE, nsubchain = 3, digits = 2, verbose = FALSE,
  ...)

Arguments

Examples

## Not run: 
rhat1 <- hgelman(hsam); rhat1
rhat2 <- hgelman(hsam, end = 51); rhat2
rhat3 <- hgelman(hsam, confidence = .90); rhat3
rhat4 <- hgelman(hsam, transform = FALSE); rhat4
rhat5 <- hgelman(hsam, autoburnin = TRUE); rhat5
rhat6 <- hgelman(hsam, split = FALSE); rhat6
rhat7 <- hgelman(hsam, subchain = TRUE); rhat7
rhat8 <- hgelman(hsam, subchain = TRUE, nsubchain = 4);
rhat9 <- hgelman(hsam, subchain = TRUE, nsubchain = 4,
digits = 1, verbose = TRUE);

hat1 <- gelman(hsam[[1]], multivariate = FALSE); hat1
hat2 <- gelman(hsam[[1]], hyper = TRUE, verbose = TRUE); hat2
hat3 <- gelman(hsam, hyper = TRUE, verbose = TRUE); hat3
hat4 <- gelman(hsam, multivariate = TRUE, verbose = FALSE);
hat5 <- gelman(hsam, multivariate = FALSE, verbose = FALSE);
hat6 <- gelman(hsam, multivariate = FALSE, verbose = TRUE);
hat7 <- gelman(hsam, multivariate = T, verbose = TRUE);

## End(Not run)

Description

A wrapper function to extract system information from Sys.info and .Platform

Usage

get_os()

Examples

get_os()
## sysname
## "linux"

Description

Constructs a matrix, showing how many responses to in each cell. The function checks whether the format of n and ns conform.

Usage

GetNsim(model, n, ns)

Arguments

Details

n can be:

1. an integer for a balanced design,

2. a matrix for an unbalanced design, where rows are subjects and columns are cells. If the matrix is a row vector, all subjects have the same n in each cell. If it is a column vector, all cells have the same n. Otherwise each entry specifies the n for a particular subject x cell combination. See below for concrete examples.

Examples

model <- BuildModel(
  p.map     = list(A = "1", B = "R", t0 = "1", mean_v = "M", sd_v = "M",
                  st0 = "1"),
  match.map = list(M = list(s1 = 1, s2 = 2)),
  constants = c(sd_v.false = 1, st0 = 0),
  factors   = list(S = c("s1","s2")),
  responses = c("r1", "r2"),
  type      = "norm")

#######################30
## Example 1
#######################30
GetNsim(model, ns = 2, n = 1)
#      [,1] [,2]
# [1,]    1    1
# [2,]    1    1

#######################30
## Example 2
#######################30
n <- matrix(c(1:2), ncol = 1)
#      [,1]
# [1,]    1  ## subject 1 has 1 response for each cell
# [2,]    2  ## subject 2 has 2 responses for each cell

GetNsim(model, ns = 2, n = n)
#      [,1] [,2]
# [1,]    1    1
# [2,]    2    2

#######################30
## Example 3
#######################30
n <- matrix(c(1:2), nrow = 1)
#      [,1] [,2]
# [1,]    1    2
GetNsim(model, ns = 2, n = n)
#     [,1] [,2]
# [1,]   1    2 ## subject 1 has 1 response for cell 1 and 2 responses for cell 2
# [2,]   1    2 ## subject 2 has 1 response for cell 1 and 2 responses for cell 2

#######################30
## Example 4
#######################30
n <- matrix(c(1:4), nrow=2)
#      [,1] [,2]
# [1,]    1    3
# [2,]    2    4
ggdmc::GetNsim(model, ns = 2, n = n)
#      [,1] [,2]
# [1,]    1    3 ## subject 1 has 1 response for cell 1 and 3 responses for cell 2
# [2,]    2    4 ## subject 2 has 2 responses for cell 1 and 4 responses for cell 2

Description

The matrix is used to simulate data. Each row represents one set of parameters for a participant.

Usage

GetParameterMatrix(x, nsub, prior = NA, ps = NA, seed = NULL)

Arguments

Details

One must enter either a vector or a matrix as true parameters to the argument, ps, when presuming to simulate data based on a fixed-effect model. When the assumption is to simulate data based on a random-effect model, one must enter a prior object to the argument, prior to first randomly generate a true parameter matrix.

Value

a ns x npar matrix

Examples

model <- BuildModel(
p.map     = list(a ="1", v = "1",z = "1", d = "1", sz = "1", sv = "1",
            t0 = "1", st0 = "1"),
match.map = list(M = list(s1 = "r1", s2 ="r2")),
factors   = list(S = c("s1", "s2")),
constants = c(st0 = 0, d = 0),
responses = c("r1", "r2"),
type      = "rd")

p.prior <- BuildPrior(
  dists = c("tnorm", "tnorm", "beta", "beta", "tnorm", "beta"),
  p1    = c(a = 1, v = 0, z = 1, sz = 1, sv = 1, t0 = 1),
  p2    = c(a = 1, v = 2, z = 1, sz = 1, sv = 1, t0 = 1),
  lower = c(0, -5, NA, NA, 0, NA),
  upper = c(2,  5, NA, NA, 2, NA))

## Example 1: Randomly generate 2 sets of true parameters from
## parameter priors (p.prior)
GetParameterMatrix(model, 2, p.prior)
##            a         v         z        sz       sv        t0
## [1,] 1.963067  1.472940 0.9509158 0.5145047 1.344705 0.0850591
## [2,] 1.512276 -1.995631 0.6981290 0.2626882 1.867853 0.1552828

## Example 2: Use a user-selected true parameters
true.vector  <- c(a=1, v=1, z=0.5, sz=0.2, sv=1, t0=.15)
GetParameterMatrix(model, 2, NA, true.vector)
##      a v   z  sz sv   t0
## [1,] 1 1 0.5 0.2  1 0.15
## [2,] 1 1 0.5 0.2  1 0.15
GetParameterMatrix(model, 2, ps = true.vector)

## Example 3: When a user enter arbritary sequence of parameters.
## Note sv is before sz. It should be sz before sv
## See correct sequence, by entering "attr(model, 'p.vector')"
## GetParameterMatrix will rearrange the sequence.
true.vector  <- c(a=1, v=1, z=0.5, sv=1, sz = .2, t0=.15)
GetParameterMatrix(model, 2, NA, true.vector)
##      a v   z  sz sv   t0
## [1,] 1 1 0.5 0.2  1 0.15
## [2,] 1 1 0.5 0.2  1 0.15

Description

Extract parameter names from a model object

Usage

GetPNames(x)

Arguments

Description

ggdmc uses the population-based Markov chain Monte Carlo to conduct Bayesian computation on cognitive models.

Author(s)

Yi-Shin Lin <[email protected]>
Andrew Heathcote <[email protected]>

References

Heathcote, A., Lin, Y.-S., Reynolds, A., Strickland, L., Gretton, M. & Matzke, D., (2018). Dynamic model of choice. Behavior Research Methods. https://doi.org/10.3758/s13428-018-1067-y.

Turner, B. M., & Sederberg P. B. (2012). Approximate Bayesian computation with differential evolution, Journal of Mathematical Psychology, 56, 375–385.

Ter Braak (2006). A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces. Statistics and Computing, 16, 239-249.

Description

The function tests whether we have drawn enough samples.

Usage

iseffective(x, minN, nfun, verbose = FALSE)

Arguments

Description

The function tests whether Markov chains converge prematurelly:

Usage

isflat(x, p1 = 1/3, p2 = 1/3, cut_location = 0.25, cut_scale = Inf,
  verbose = FALSE)

Arguments

Description

The function tests whether Markov chains are mixed well.

Usage

ismixed(x, cut = 1.01, split = TRUE, verbose = FALSE)

Arguments

See Also

gelman)

Description

The function tests whether Markov chains encounter a parameter region that is difficult to search. CheckConverged is a wrapper function running the four checking functions, isstuck, isflat, ismixed and iseffective.

Usage

isstuck(x, hyper = FALSE, cut = 10, start = 1, end = NA,
  verbose = FALSE)

CheckConverged(x)

Arguments

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

likelihood(pvector, data, min_lik = 1e-10)

Arguments

Value

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

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(p.vector, dmi)

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 (p.vector, dmi)

Description

Create a MCMC list

Usage

mcmc_list.model(x, start = 1, end = NA, pll = TRUE)

Arguments

Description

Calculate each chain separately for the mean (across many MCMC iterations) of posterior log-likelihood. If the difference of the means and the median (across chains) of the mean of posterior is greater than the cut, chains are considered stuck. The default value for cut is 10. unstick manually removes stuck chains from posterior samples.

Usage

PickStuck(x, hyper = FALSE, cut = 10, start = 1, end = NA,
  verbose = FALSE, digits = 2)

Arguments

Value

PickStuck gives an index vector; unstick gives a DMC sample.

Examples

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 = .75, B = .25, t0 = .2, mean_v.true = 2.5, mean_v.false = 1.5)

p.prior <- BuildPrior(
  dists = c("tnorm", "tnorm", "beta", "tnorm", "tnorm"),
  p1    = c(A = .3, B = .3, t0 = 1, mean_v.true = 1, mean_v.false = 0),
  p2    = c(1, 1,   1, 3, 3),
  lower = c(0,  0,  0, NA, NA),
  upper = c(NA,NA,  1, NA, NA))

## Not run: 
dat <- simulate(model, 30, ps = p.vector)
dmi <- BuildDMI(dat, model)
sam <- run(StartNewsamples(dmi, p.prior))
bad <- PickStuck(sam)

## End(Not run)

Description

plot_prior plots one member in a prior object. plot.prior plots all members in a prior object.

Usage

plot_prior(i, prior, xlim = NA, natural = TRUE, npoint = 100,
  trans = NA, save = FALSE, ...)

## S3 method for class 'prior'
plot(x, save = FALSE, ps = NULL, ...)

Arguments

Examples

p.prior <- BuildPrior(
           dists = rep("tnorm", 7),
           p1    = c(a = 2,   v.f1 = 4,  v.f2 = 3,  z = 0.5, sv = 1,
                     sz = 0.3, t0 = 0.3),
           p2    = c(a = 0.5, v.f1 = .5, v.f2 = .5, z = 0.1, sv = .3,
                     sz = 0.1, t0 = 0.05),
           lower = c(0,-5, -5, 0, 0, 0, 0),
           upper = c(5, 7,  7, 1, 2, 1, 1))

plot_prior("a", p.prior)
plot_prior(2, p.prior)
plot(p.prior)

Description

a convenient function to rearrange p.prior or an element in a pp.prior as a data frame for inspection.

Usage

## S3 method for class 'prior'
print(x, ...)

Arguments

Value

a data frame listing prior distributions and their settings

Examples

pop.mean  <- c(a=1,  v.f1=1,  v.f2=.2, z=.5, sz=.3,  sv.f1=.25, sv.f2=.23,
               t0=.3)
pop.scale <- c(a=.2, v.f1=.2, v.f2=.2, z=.1, sz=.05, sv.f1=.05, sv.f2=.05,
               t0=.05)

p.prior <- BuildPrior(
  dists = rep("tnorm", 8),
  p1    = pop.mean,
  p2    = pop.scale,
  lower = c(0, -5, -5, 0, 0, 0, 0, 0),
  upper = c(2,  5,  5, 1, 2, 2, 1, 1))

print(p.prior)

Description

A wrapper function for generating random numbers of either the model type, rd, or norm.

Usage

random(type, pmat, n, seed = NULL)

Arguments

Description

rlba_norm, only slightly faster than maker, calls C++ function directly.

Usage

rlba_norm(n, A, b, mean_v, sd_v, t0, st0, posdrift)

Arguments

Value

a n x 2 matrix of RTs (first column) and responses (second column).

Description

Probability density functions and random generation for parameter prior distributions.

Usage

rprior(prior, n = 1)

Arguments

Examples

p.prior <- BuildPrior(
 dists = c("tnorm", "tnorm", "beta", "tnorm", "beta", "beta"),
 p1    = c(a = 1, v = 0, z = 1, sz = 1, sv = 1, t0 = 1),
 p2    = c(a = 1, v = 2, z = 1, sz = 1, sv = 1, t0 = 1),
 lower = c(0,-5, NA, NA, 0, NA),
 upper = c(2, 5, NA, NA, 2, NA))

rprior(p.prior, 9)
##               a           v         z         sz        sv         t0
## [1,] 0.97413686  0.78446178 0.9975199 -0.5264946 0.5364492 0.55415052
## [2,] 0.72870190  0.97151662 0.8516604  1.6008591 0.3399731 0.96520848
## [3,] 1.63153685  1.96586939 0.9260939  0.7041254 0.4138329 0.78367440
## [4,] 1.55866180  1.43657110 0.6152371  0.1290078 0.2957604 0.23027759
## [5,] 1.32520281 -0.07328408 0.2051155  2.4040387 0.9663111 0.06127237
## [6,] 0.49628528 -0.19374770 0.5142829  2.1452972 0.4335482 0.38410626
## [7,] 0.03655549  0.77223432 0.1739831  1.4431507 0.6257398 0.63228368
## [8,] 0.71197612 -1.15798082 0.8265523  0.3813370 0.4465184 0.23955415
## [9,] 0.38049166  3.32132034 0.9888108  0.9684292 0.8437480 0.13502154

pvec <- c(a=1, v=1, z=0.5, sz=0.25, sv=0.2,t0=.15)
p.prior  <- BuildPrior(
  dists = rep("tnorm", 6),
  p1    = c(a=2,   v=2.5, z=0.5, sz=0.3, sv=1,  t0=0.3),
  p2    = c(a=0.5, v=.5,  z=0.1, sz=0.1, sv=.3, t0=0.05) * 5,
  lower = c(0,-5, 0, 0, 0, 0),
  upper = c(5, 7, 2, 2, 2, 2))

Description

Simulate response time data either for one subject or multiple subjects. The simulation is based on a model object. For one subject, one must supply a true parameter vector to the ps argument.

Usage

## S3 method for class 'model'
simulate(object, nsim = NA, seed = NULL, nsub = NA,
  prior = NA, ps = NA, ...)

Arguments

Details

For multiple subjects, one can enter a matrix (or a row vector) as true parameters. Each row is to generate data separately for a subject. This is the fixed-effect model. To generate data based on a random-effect model, one must supply a prior object. In this case, ps argument is unused. Note in some cases, a random-effect model may fail to draw data from the model, because true parameters are randomly drawn from a prior object. This would happen sometimes in diffusion model, because certain parameter combinations are considered invalid.

ps can be a row vector, in which case each subject has identical parameters. It can also be a matrix with one row per subject, in which case it must have ns rows. The true values will be saved as parameters attribute in the output object.

Value

a data frame

Description

Fit a hierarchical or a fixed-effect model, using Bayeisan optimisation. We use a specific type of pMCMC algorithm, the DE-MCMC. This particular sampling method includes crossover and two different migration operators. The migration operators are similar to random-walk algorithm. They wouold be less efficient to find the target parameter space, if been used alone.

Usage

StartNewsamples(data, prior = NULL, nmc = 200, thin = 1,
  nchain = NULL, report = 100, rp = 0.001, gammamult = 2.38,
  pm0 = 0.05, pm1 = 0.05, block = TRUE, ncore = 1)

run(samples, nmc = 500, thin = 1, report = 100, rp = 0.001,
  gammamult = 2.38, pm0 = 0, pm1 = 0, block = TRUE, ncore = 1,
  add = FALSE)

Arguments

Description

Calculate summary statistics for posterior samples

Usage

summary_mcmc_list(object, prob = c(0.025, 0.25, 0.5, 0.75, 0.975), ...)

Arguments

Description

This calls seven different variants of summary function to summarise posterior samples

Usage

## S3 method for class 'model'
summary(object, hyper = FALSE, start = 1, end = NA,
  hmeans = FALSE, hci = FALSE, prob = c(0.025, 0.25, 0.5, 0.75,
  0.975), recovery = FALSE, ps = NA, type = 1, verbose = FALSE,
  digits = 2, ...)

Arguments

Examples

## Not run: 
est1 <- summary(hsam[[1]], FALSE)
est2 <- summary(hsam[[1]], FALSE, 1, 100)

est3 <- summary(hsam)
est4 <- summary(hsam, verbose = TRUE)
est5 <- summary(hsam, verbose = FALSE)

hest1 <- summary(hsam, TRUE)

## End(Not run)

Description

TableParameters arranges the values in a parameter vector and creates a response x parameter matrix. The matrix is used by the likelihood function, assigning a trial to a cell for calculating probability densities.

Usage

TableParameters(p.vector, cell, model, n1order)

Arguments

Value

each row corresponding to the model parameter for a response. When n1.order is FALSE, TableParameters returns a martix without rearranging into node 1 order. For example, this is used in the simulate function. By default n1.order is TRUE.

Examples

m1 <- BuildModel(
  p.map     = list(a = "1", v = "F", z = "1", d = "1", sz = "1", sv = "F",
                   t0 = "1", st0 = "1"),
  match.map = list(M = list(s1 = "r1", s2 = "r2")),
  factors   = list(S = c("s1", "s2"), F = c("f1","f2")),
  constants = c(st0 = 0, d = 0),
  responses = c("r1","r2"),
  type      = "rd")

m2 <- BuildModel(
  p.map = list(A = "1", B = "1", mean_v = "M", sd_v = "1",
    t0 = "1", st0 = "1"),
  constants = c(st0 = 0, sd_v = 1),
  match.map = list(M = list(s1 = 1, s2 = 2)),
  factors   = list(S = c("s1", "s2")),
  responses = c("r1", "r2"),
  type      = "norm")

pvec1 <- c(a = 1.15, v.f1 = -0.10, v.f2 = 3, z = 0.74, sz = 1.23,
           sv.f1 = 0.11, sv.f2 = 0.21, t0 = 0.87)
pvec2 <- c(A = .75, B = .25, mean_v.true = 2.5, mean_v.false = 1.5,
           t0 = .2)

print(m1, pvec1)
print(m2, pvec2)

accMat1 <- TableParameters(pvec1, "s1.f1.r1", m1, FALSE)
accMat2 <- TableParameters(pvec2, "s1.r1",    m2, FALSE)

##    a    v   t0    z d   sz   sv st0
## 1.15 -0.1 0.87 0.26 0 1.23 0.11   0
## 1.15 -0.1 0.87 0.26 0 1.23 0.11   0

##    A b  t0 mean_v sd_v st0
## 0.75 1 0.2    2.5    1   0
## 0.75 1 0.2    1.5    1   0

Description

Extracts the parameter array (ie theta) from posterior samples of a partiipant and convert it to a coda mcmc.list.

Usage

theta2mcmclist(x, start = 1, end = NA, split = FALSE,
  subchain = FALSE, nsubchain = 3, thin = NA)

phi2mcmclist(x, start = 1, end = NA, split = FALSE,
  subchain = FALSE, nsubchain = 3)

Arguments

Details

phi2mcmclist extracts the phi parameter array, which stores the location and scale parameters at the hyper level.

Examples

## Not run: 
model <- BuildModel(
p.map     = list(a = "RACE", v = c("S", "RACE"), z = "RACE", d = "1",
            sz = "1", sv = "1", t0 = c("S", "RACE"), st0 = "1"),
match.map = list(M = list(gun = "shoot", non = "not")),
factors   = list(S = c("gun", "non"), RACE = c("black", "white")),
constants = c(st0 = 0, d = 0, sz = 0, sv = 0),
responses = c("shoot", "not"),
type      = "rd")

pnames <- GetPNames(model)
npar   <- length(pnames)
pop.mean  <- c(1, 1, 2.5, 2.5, 2.5, 2.5, .50, .50, .4, .4, .4, .4)
pop.scale <- c(.15, .15, 1, 1, 1, 1, .05, .05, .05, .05, .05, .05)
names(pop.mean)  <- pnames
names(pop.scale) <- pnames
pop.prior <- BuildPrior(
  dists = rep("tnorm", npar),
  p1    = pop.mean,
  p2    = pop.scale,
  lower = c(rep(0, 2), rep(-5, 4), rep(0, 6)),
  upper = c(rep(5, 2), rep(7, 4), rep(2, 6)))
p.prior <- BuildPrior(
  dists = rep("tnorm", npar),
  p1    = pop.mean,
  p2    = pop.scale*10,
  lower = c(rep(0, 2), rep(-5, 4), rep(0, 6)),
  upper = c(rep(10, 2), rep(NA, 4), rep(5, 6)))
mu.prior <- BuildPrior(
  dists = rep("tnorm", npar),
  p1    = pop.mean,
  p2    = pop.scale*10,
  lower = c(rep(0,  2), rep(-5, 4), rep(0, 6)),
  upper = c(rep(10, 2), rep(NA, 4), rep(5, 6)))
sigma.prior <- BuildPrior(
  dists = rep("beta", npar),
  p1    = rep(1, npar),
  p2    = rep(1, npar),
  upper = rep(2, npar))
names(sigma.prior) <- GetPNames(model)
priors <- list(pprior=p.prior, location=mu.prior, scale=sigma.prior)
dat    <- simulate(model, nsim = 10, nsub = 10, prior = pop.prior)
dmi    <- BuildDMI(dat, model)
ps     <- attr(dat, "parameters")

fit0 <- StartNewsamples(dmi, priors)
fit  <- run(fit0)

tmp1 <- theta2mcmclist(fit[[1]])
tmp2 <- theta2mcmclist(fit[[2]], start = 10, end = 90)
tmp3 <- theta2mcmclist(fit[[3]], split = TRUE)
tmp4 <- theta2mcmclist(fit[[4]], subchain = TRUE)
tmp5 <- theta2mcmclist(fit[[5]], subchain = TRUE, nsubchain = 4)
tmp6 <- theta2mcmclist(fit[[6]], thin = 2)

## End(Not run)

Description

Unstick posterios samples (One subject)

Usage

unstick_one(x, bad)

Arguments