simpar.Rd
Create Parameters for Simulation with Uncertainty
simpar(
nsim,
theta,
covar,
omega,
sigma,
odf = NULL,
sdf = NULL,
digits = 4,
min = -Inf,
max = Inf
)
scalar numeric specifying the number of sets to attempt
vector of point estimates of fixed effect parameters
variance-covariance matrix for fixed effect parameters
list of variance-covariance matrices for first level random effects
list of variance-covariance matrices for second level random effects
vector of omega degrees of freedom, one per matrix
vector of sigma degrees of freedom, one per matrix
number of significant digits to include in output
lower limit for parameter estimates
upper limit for parameter estimates
matrix, with column names indicating parameters, and row names indicating set number before filtering by min and max.
If min or max are non-default (see below), you may want to set nsim marginally higher to allow for dropped sets. covar is coerced to matrix using as.matrix.
If omega and sigma are not lists, they are coerced using list. Then each element is coerced using as.matrix.
By default, each element in odf and sdf will be the length (number of elements) in the corresponding matrix.
min
and max
may be given as scalar values, in which case they apply to
all parameters (as do the defaults). Alternatively, the first n limits may
be specified as a vector, in which case the remaining (if any) will be the
default. If any simulated parameter does not fall between its limits, inclusive,
the entire parameter set (row) is dropped from the result, with warning.
set.seed(100)
simpar(
nsim=10,
theta=c(13,75,1),
covar=matrix(c(10,7,2,7,30,1,2,1,0.5),ncol=3,nrow=3),
omega=list(
0.1,
matrix(c(0.04,0.02,0.02,0.04),ncol=2,nrow=2)
),
odf=c(50,20),
sigma=list(0.04,1),
sdf=c(99,99),
min=rep(0,3),
max=rep(90,3)
)
#> TH.1 TH.2 TH.3 OM1.1 OM2.2 OM3.2 OM3.3 SG1.1 SG2.2
#> 1 14.13 77.63 1.0640 0.10390 0.04442 0.007829 0.03910 0.03569 1.0690
#> 2 12.98 74.21 1.2430 0.09322 0.05781 0.048300 0.07054 0.04070 1.0910
#> 3 12.59 75.60 0.9816 0.11000 0.06001 0.009597 0.03225 0.04675 1.0330
#> 4 13.38 69.56 1.4090 0.11790 0.10790 0.061510 0.06000 0.03660 0.9531
#> 5 13.18 74.26 0.8007 0.11190 0.08363 0.046380 0.07052 0.04134 1.2040
#> 6 12.40 73.30 0.7659 0.09395 0.03944 0.017340 0.07900 0.03551 0.9038
#> 7 13.02 78.48 0.7257 0.08317 0.05386 0.027630 0.04757 0.04314 0.8023
#> 8 13.11 70.69 1.1650 0.08464 0.04884 0.041840 0.08535 0.04101 0.8885
#> 9 12.07 80.26 0.3608 0.10420 0.03331 -0.006786 0.06227 0.03728 0.8611
#> 10 19.73 74.93 2.5130 0.06927 0.06935 0.050570 0.05766 0.03791 0.9706
simpar(
nsim=1,
theta=c(13,75,1),
covar=matrix(c(10,7,2,7,30,1,2,1,0.5),ncol=3,nrow=3),
omega=list(
0.1,
matrix(c(0.04,0.02,0.02,0.04),ncol=2,nrow=2)
),
odf=c(50,20),
sigma=list(0.04,1),
sdf=c(99,99),
min=rep(0,3),
max=rep(90,3)
)
#> TH.1 TH.2 TH.3 OM1.1 OM2.2 OM3.2 OM3.3 SG1.1 SG2.2
#> 1 15.18 75.47 1.29 0.09543 0.03465 0.01318 0.07248 0.0442 1.26
simpar(
nsim=1,
theta=c(13,75,1),
covar=matrix(c(10,7,2,7,30,1,2,1,0.5),ncol=3,nrow=3),
omega=list(
0.1,
matrix(c(0.04,0.02,0.02,0.04),ncol=2,nrow=2)
),
odf=c(50,20),
sigma=list(0.04,1),
sdf=c(99,99),
min=Inf,
max=-1
)
#> Warning: 1 of 1 rows dropped
#> TH.1 TH.2 TH.3 OM1.1 OM2.2 OM3.2 OM3.3 SG1.1 SG2.2