# simDat

From MargCond v1.0.0
by James Proudfoot

##### Function to simulate multivariate longitudinal data

A function that simulates correlated multivariate data based on a set of fixed and random effects.

- Keywords
- datagen

##### Usage

```
simDat(n, fixed_effects, rand_effects, error_var = c(2, 2),
error_structure = "normal", rho = 0, times = 1:5, X = NULL, Z = NULL)
```

##### Arguments

- n
total sample size (number of clusters)

- fixed_effects
list of fixed effect vectors for each outcome

- rand_effects
list of random effect vectors for each outcome

- error_var
vector of error variances for each outcome

- error_structure
structure for the random error term, either

`"normal"`

for multivariate normal or`"50:50 normal"`

for a mixture of two normal distributions- rho
correlation between outcomes

- times
times for each repeated measure

- X
fixed effect design matrix

- Z
random effect design matrix

##### Value

A dataframe included simulated outcomes and the design matrices

##### Examples

```
# NOT RUN {
set.seed(2112)
NN = 80
n_times = 1:3
## Simulating some data
simdat <- simDat(n = NN,
fixed_effects = list(c(1, 1, 2), c(1.5, 1, 3)),
rand_effects = list(1, 1),
error_var = c(4, 4),
error_structure = 'normal',
rho = .35,
times = n_times,
X = cbind(rep(1, NN * length(n_times)),
rnorm(NN * length(n_times), 0, 2),
rbinom(NN * length(n_times), 1, .5)),
Z = cbind(rep(1, NN * length(n_times))))
## Adding random missing values
aa <- sample(1:nrow(simdat), 10, replace = TRUE)
bb <- sample(1:7, 10, replace = TRUE)
for (i in 1:length(aa)) {
simdat[aa[i], bb[i]] <- NA
}
# }
```

*Documentation reproduced from package MargCond, version 1.0.0, License: GPL-2*

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