Maps a parameter vector that corresponds to sampled parameters
of the cognitive model back to the experimental design. The parameter vector
can be created using sampled_p_vector(). The returned matrix shows whether/how parameters
differ across the experimental factors.
Usage
mapped_par(
p_vector,
design,
model = NULL,
digits = 3,
remove_subjects = TRUE,
covariates = NULL,
...
)Arguments
- p_vector
A parameter vector. Must be in the form of
sampled_p_vector(design)- design
A design list. Created by
design- model
Optional model type (if not already specified in
design)- digits
Integer. Will round the output parameter values to this many decimals
- remove_subjects
Boolean. Whether to include subjects as a factor in the design
- covariates
Covariates specified in the design can be included here.
- ...
optional arguments
Value
Matrix with a column for each factor in the design and for each model parameter type (p_type).
Examples
# First define a design:
design_DDMaE <- design(data = forstmann,model=DDM,
formula =list(v~0+S,a~E, t0~1, s~1, Z~1, sv~1, SZ~1),
constants=c(s=log(1)))
#> Parameter(s) st0 not specified in formula and assumed constant.
#>
#> Sampled Parameters:
#> [1] "v_Sleft" "v_Sright" "a" "a_Eneutral" "a_Eaccuracy"
#> [6] "t0" "Z" "sv" "SZ"
#>
#> Design Matrices:
#> $v
#> S v_Sleft v_Sright
#> left 1 0
#> right 0 1
#>
#> $a
#> E a a_Eneutral a_Eaccuracy
#> speed 1 0 0
#> neutral 1 1 0
#> accuracy 1 0 1
#>
#> $t0
#> t0
#> 1
#>
#> $s
#> s
#> 1
#>
#> $Z
#> Z
#> 1
#>
#> $sv
#> sv
#> 1
#>
#> $SZ
#> SZ
#> 1
#>
#> $st0
#> st0
#> 1
#>
# Then create a p_vector:
p_vector=c(v_Sleft=-2,v_Sright=2,a=log(1),a_Eneutral=log(1.5),a_Eaccuracy=log(2),
t0=log(.2),Z=qnorm(.5),sv=log(.5),SZ=qnorm(.5))
# This will map the parameters of the p_vector back to the design
mapped_par(p_vector,design_DDMaE)
#> E S v a sv t0 st0 s Z SZ z sz
#> 1 speed left -2 1.0 0.5 0.2 0 1 0.5 0.5 0.50 0.50
#> 2 neutral left -2 1.5 0.5 0.2 0 1 0.5 0.5 0.75 0.75
#> 3 accuracy left -2 2.0 0.5 0.2 0 1 0.5 0.5 1.00 1.00
#> 4 speed right 2 1.0 0.5 0.2 0 1 0.5 0.5 0.50 0.50
#> 5 neutral right 2 1.5 0.5 0.2 0 1 0.5 0.5 0.75 0.75
#> 6 accuracy right 2 2.0 0.5 0.2 0 1 0.5 0.5 1.00 1.00