---
title: "simdata: A package for creating simulated datasets"
author: "Michael Kammer"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
fig_width: 8
fig_height: 6
toc: true
toc_depth: 2
number_sections: true
vignette: >
%\VignetteIndexEntry{simdata: A package for creating simulated datasets}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
bibliography: "Demo_references.bib"
---
```{r message=FALSE, warning=FALSE, include=FALSE}
library(simdata)
library(ggplot2)
library(reshape2)
library(GGally)
library(knitr)
library(doParallel)
library(doRNG)
```
# Introduction
Simulation studies in statistics or other methodological research fields are
controlled experiments used to assess the properties of algorithms,
gauge the performance of statistical or machine learning models, and gain
insights into complex phenomena which are not readily understood analytically.
While simulation studies by nature are simplifying the "true" underlying
mechanisms of interest, they are useful because they allow the complete
specification of the "ground truth" to which comparisons can be made and the
experimental conditions are fully under control of the experimenter. This is
in contrast to data obtained from observational studies, in which many
factors may influence the resulting measurements.
Simulation studies typically consist of three major components:
- A data generating mechanism
- The methods or models to be evaluated
- Evaluation criteria to assess the methods after application to the generated data
All three together make up what we will call the *simulation study design*.
The data generating mechanism usually simulates (i.e. creates) data
according to pre-defined parameters, e.g. the number of variables (i.e. columns
of the data matrix) to be generated, their correlation structure or the number
of observations (i.e. rows of the data matrix).
Usually, the data generation involves some uncertainty or noise when creating
the data, to mimic the uncertainty when data is obtained through measurements
in the real world or sampled from a population.
Therefore, each simulation scenario can be conducted repeatedly to remove the
effect of sampling variability.
The methods or models to be evaluated in the study are then applied to the
datasets generated by the data generating mechanism and the evaluation criteria
are computed, e.g. some measure of deviation from the ground truth.
## Data generation with this package
The goal of the `simdata` package is to provide a simple yet flexible
framework which supports the first step of a simulation study,
namely the data generating mechanism.
The way data is created in this package involves the following procedure:
1. Draw an initial dataset `Z` from some probability distribution. An example
might be to draw a number of variables with given correlation structure
from a multivariate Normal distribution.
2. The initial data is then transformed via user specified transformation
functions into the final dataset `X`. Examples for transformations could be
to derive binary variables from Normal random variables or to change location
and scale of the initial random variables.
3. The final dataset is optionally post-processed. Examples are to truncate
the dataset to prevent problematic outliers or to standardize the data.
After these steps, the final dataset `X` can be used in the further steps
of the simulation study.
## Workflow with this package
In order to implement the outlined data generating mechanism, the user
first has to define a simulation design. This is done by extending the S3 class
`simdesign`, or using one of the pre-defined simulation designs,
which is used to as an interface for further package functions.
It stores all necessary information to generate data following the given
specification. The actual data generation then happens in the `simulate_data`
function. Please see the numerous examples below to quickly familiarize
yourself with the package functionality.
## Packages used
This vignette makes use of the [`tidyverse`](https://www.tidyverse.org/)
environment of packages and specifically requires the `dplyr`, `purrr`,
`ggplot2`, `forcats` and `GGally` packages to be available, as well as the
`reshape2` package for data transformation. Furthermore, for the
[parallelization example](#parallel) the `doParallel` and `doRNG` package
are loaded.
Please refer to [the R environment used to create this vignette](#rsession) for
detailed information. In this vignette we will prefix all relevant
function calls by `::` to show the package which implements the function - this
is not necessary but only done for demonstration purposes.
# Example 1: Independent data, no transformation {#ex1}
We demonstrate a very basic example here:
- Setup a design to sample from 5 independent normal variables
- Define a diagonal 5 x 5 correlation matrix (`correlation_matrix`)
- Ensure that the data is returned as a data.frame by passing the `data.frame`
constructor to `transform_initial`. (Otherwise the data would be returned
as a matrix.)
- Pass a prefix to the function to construct variable names
- Simulate from the design to obtain 100 observations (`n_obs`) with a given
random seed (`seed`).
- Inspect the simulated data.
```{r message=FALSE, warning=FALSE}
correlation_matrix = diag(1, nrow = 5)
sim_design = simdata::simdesign_mvtnorm(relations = correlation_matrix,
transform_initial = data.frame,
prefix_final = "variable")
sim_data = simdata::simulate_data(sim_design, n_obs = 100, seed = 25897165)
knitr::kable(head(sim_data))
p = GGally::ggpairs(sim_data,
upper = list(continuous = "points"),
progress = FALSE) +
theme_bw()
print(p)
```
# Example 2: Independent data, with transformation {#ex2}
In this example we transform the initial data to obtain a more
interesting dataset. The basic principle follows [Example 1](#ex1).
- Create a transformation function `transformation` using `simdata::function_list`
- Pass functions that transform the columns of the simulated, initial
dataset as named arguments
- The number of columns in the final dataset is given by the number of
transformations passed here
- To let the names of the arguments determine the final columnnames,
set `prefix_final` to NULL in the `simdesign` constructor
- Pass the created transformation function as `transform_initial` argument
to the `simdesign` constructor.
```{r message=TRUE, warning=TRUE}
correlation_matrix = diag(1, nrow = 5)
transformation = simdata::function_list(
"v1" = function(x) x[, 1],
"v2*2" = function(x) x[, 2] * 2,
"v3^2" = function(x) x[, 3]^2,
"v4+v5" = function(x) x[, 4] + x[, 5],
check.names = FALSE # pass columnnames exactly as specified here
)
sim_design = simdata::simdesign_mvtnorm(
relations = correlation_matrix,
transform_initial = transformation,
prefix_final = NULL
)
sim_data = simdata::simulate_data(sim_design, n_obs = 100, seed = 25897165)
knitr::kable(head(sim_data))
p = GGally::ggpairs(sim_data, upper = list(continuous = "points"),
progress = FALSE) +
theme_bw()
print(p)
```
# Example 3: Realistic, complex data {#ex3}
In this example we implement a design following @BinderSetup. The simulated
data resembles data obtained from a biomedical study with a complex correlation
pattern and different variable distributions.
- Create a complex initial correlation matrix comfortably using
`simdata::cor_from_upper`
- This defines the initial distribution as a multivariate Normal
distribution `Z` with 15 dimensions
- Use a variety of transformations to create numeric, binary and ordinal variables
- Using the transformation the initial distribution `Z` is transformed to
the final dataset `X` with 17 columns
```{r}
correlation_matrix = simdata::cor_from_upper(
15,
rbind(c(1,2,0.8), c(1,9,0.3),
c(3,5,0.3), c(3,9,-0.5),
c(4,6,-0.5), c(4,7,-0.3),
c(5,6,-0.3), c(5,12,0.5),
c(6,7,0.5), c(6,11,0.5), c(6,14,0.3),
c(7,11,0.3), c(7,14,0.3),
c(8,9,-0.3), c(8,11,0.3),
c(11,14,0.5)))
transformation = simdata::function_list(
v1 = function(z) floor(10 * z[,1] + 55),
v2 = function(z) z[,2] < 0.6,
v3 = function(z) exp(0.4 * z[,3] + 3),
v4 = function(z) z[,4] >= -1.2,
v5 = function(z) z[,4] >= 0.75,
v6 = function(z) exp(0.5 * z[,5] + 1.5),
v7 = function(z) floor(pmax(0, 100 * exp(z[,6]) - 20)),
v8 = function(z) floor(pmax(0, 80 * exp(z[,7]) - 20)),
v9 = function(z) z[,8] < -0.35,
v10 = function(z) (z[,9] >= 0.5) & (z[,9] < 1.5),
v11 = function(z) z[,9] >= 1.5,
v12 = function(z) 0.01*floor(100 * (z[,10] + 4)^2),
v13 = function(z) floor(10 * z[,11] + 55),
v14 = function(z) floor(10 * z[,12] + 55),
v15 = function(z) floor(10 * z[,13] + 55),
v16 = function(z) z[,14] < 0,
v17 = function(z) z[,15] < 0
)
sim_design = simdata::simdesign_mvtnorm(
relations = correlation_matrix,
transform_initial = transformation,
prefix_final = NULL
)
```
## Visualisation of initial correlation matrix
We can display the correlation matrix of the initial underlying distribution
using `ggplot2`. Or we can use the graph plotting tools from this package to
display a basic correlation network.
```{r echo=FALSE}
plotdf = melt(sim_design$cor_initial)
ggplot(plotdf, aes(x = factor(Var1), y = factor(Var2), fill = value)) +
geom_tile() +
geom_text(aes(label = value)) +
scale_x_discrete(position = "top") +
scale_y_discrete(limits = rev) +
xlab(expr(paste("Initial variable ", z[i]))) +
ylab(expr(paste("Initial variable ", z[i]))) +
coord_equal() +
scale_fill_gradient2(
"Correlation",
limits = c(-1, 1),
low = "#377eb8", mid = "white", high = "#e41a1c") +
theme_bw()
```
Or we can use the simple graph visualisation provided by this package. To keep
this graph simple, correlations below a certain threshold are removed. The
`simdata::plot_cor_network` function provides several options to improve display of
the graph, some of which are explained in more detail [below](#plotestcor).
Here we just point out that the layout of the network is based on the
Fruchterman-Reingold algorithm as implemented in the `igraph` package and
therefore comprises random parts. Therefore, using a seed for the random number
generation is advised.
```{r}
simdata::plot_cor_network(sim_design, seed = 1)
```
## Simulation and visualisation of final data {#simfinal}
We simulate from the design and visualise numeric variables via violinplots and
discrete variables via barplots using `ggplot2`.
```{r}
sim_data = simdata::simulate_data(sim_design, n_obs = 100, seed = 25897165)
```
```{r echo=FALSE, message=FALSE, warning=FALSE}
numeric_vars = names(sim_design$types_final)[sim_design$types_final == "numeric"]
plotdf = melt(sim_data[, numeric_vars], measure.vars = numeric_vars)
p = ggplot(plotdf, aes(x = "", y = value)) +
geom_violin() +
geom_dotplot(binaxis = "y", stackdir = "center") +
facet_wrap(~ variable, scales = "free", nrow = 3) +
theme_bw() +
ylab("Value") +
theme(axis.title.x = element_blank())
print(p)
discrete_vars = names(sim_design$types_final)[sim_design$types_final != "numeric"]
plotdf = melt(sim_data[, discrete_vars], measure.vars = discrete_vars)
p = ggplot(plotdf, aes(x = variable, fill = value, color = value)) +
geom_bar(alpha = 0.5, size = 1) +
scale_fill_brewer(palette = "Set1") +
scale_color_brewer(palette = "Set1") +
ylab("Proportion of total samplesize (%)") +
theme_bw() +
theme(axis.title.x = element_blank())
print(p)
```
We can also plot the correlation structure of the single, final dataset via the
correlation matrix and visualise it using `ggplot2` or as a network using the
functions provided by this package.
```{r echo=FALSE, message=FALSE, warning=FALSE}
plotdf = cor(sim_data, method = "s", use = "p")
plotdf = melt(plotdf)
ggplot(plotdf, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
geom_text(aes(label = round(value, 2)), size = 2) +
scale_fill_gradient2(
"Correlation",
limits = c(-1, 1),
low = "#377eb8", mid = "white", high = "#e41a1c") +
coord_equal() +
scale_x_discrete(position = "top") +
scale_y_discrete(limits = rev) +
xlab(expr(paste("Final variable ", v[i]))) +
ylab(expr(paste("Final variable ", v[i]))) +
theme_bw() +
theme(text = element_text(size = 10))
```
## Visualisation of estimated correlation network {#plotestcor}
Note that the above correlation matrix is based on a single simulated dataset
of a fixed size. To provide a robust and stable estimate of the correlation
after transforming the initial dataset, one can employ
approximation by simulation, as the initial correlation structure is affected
by the data transformation and can not be analytically expressed in all cases.
This can conveniently be achieved using `estimate_final_correlation`. The
resulting correlation network can be visualized via `plot_cor_network`
or by `plot_estimated_cor_network` which combines
the estimation of the correlation matrix and the network plotting into one
wrapper function.
Note that the layout algorithm of `igraph` computes a similar layout for
initial and final correlation structure, given some manual tweaking of the random
seed.
This probably does not hold for all setups, but if the initial and final
layout are somewhat similar (i.e. after proper pruning via cutting of low
correlations), the resulting graphs may reflect that.
We demonstrate some basic plotting functionality (the arguments are identical
for `plot_cor_network`):
- Draw all edges in the graph by setting `cor_cutoff` to NULL
- Turn off edge labeling by setting `edge_label_function` to NULL
- Controling edge width using `edge_width_function`
- Using edge weights (i.e. correlation between two variables) to influence the
graph layout via `use_edge_weights` - higher edge weight brings vertices
closer together
```{r message=FALSE, warning=FALSE}
# draw full network
simdata::plot_estimated_cor_network(sim_design,
cor_cutoff = NULL,
edge_label_function = NULL,
edge_width_function = function(x) x*25,
use_edge_weights = TRUE,
edge.color = "clipped-ramp",
seed = 2321673)
```
The following graphs all use the default correlation cutoff of 0.1.
- Switching correlation type to Spearman using `cor_type`
- Note that when edge weights are used, then the networks for different
correlation types can not be compared directly, as the vertex layout may
change
- Setting various other parameters
- Note the usage of `mar` to set the image margins to make the axes visisble
- Note that distinction between categorical and numeric data is turned off
(`show_categorical`)
```{r message=FALSE, warning=FALSE}
# simplify by using cor_cutoff
simdata::plot_estimated_cor_network(sim_design,
seed = 2)
# set correlation type
simdata::plot_estimated_cor_network(sim_design,
cor_type = "spearman",
seed = 2321673)
# set various parameters
simdata::plot_estimated_cor_network(sim_design, seed = 2321673,
edge.color = "red-blue",
axes = TRUE, cor_type = "s",
edge_width_function = function(x) var(x)*200,
show_categorical = FALSE,
mar = c(2, 2, 0, 0))
```
In all of these plots it is evident that the dataset contains a tight cluster
of highly correlated variables (especially `v4, v5, v7, v8, v13, v16`) but also
quite independent variables (`v12, v15, v17`). Some are also only connected to
the network via one other variable (`v2, v14`). Thanks to the network layout of
the plot, which is often more intelligible than a plain correlation matrix,
this quite clearly shows the rich variety of correlation patterns
within the simulated data.
## Post-processing
Post-processing can be used to apply a number of functions to the
dataset before it is returned by `simulate_data`. This is useful to e.g.
standardize and truncate data, as shown below.
To add post-processing functions to the `simdesign` object, simply pass a
list to the constructor or add it directly to the object. Each entry has
a name (name of a function) and is a list with named arguments passed to the
function (if default parameters should be used simply pass an empty list).
See the `do_processing` help for further details.
The simulation of the data does not need to be changed in any way.
### Truncation
- Add truncation to the post-processing via `process_truncate_by_iqr`
- Truncation is only added for specific numeric variables by passing a
named vector
```{r}
sim_design$process_final = list(
process_truncate_by_iqr = list(
truncate_multipliers = c(v6 = 2, v7 = 2, v8 = 2)
)
)
sim_data = simdata::simulate_data(sim_design, n_obs = 100, seed = 25897165)
```
Compare the results with the [results before](#simfinal) and note the
truncation in the specified variables (`v6, v7, v8`).
```{r echo=FALSE, message=FALSE, warning=FALSE}
numeric_vars = names(sim_design$types_final)[sim_design$types_final == "numeric"]
plotdf = melt(sim_data[, numeric_vars])
p = ggplot(plotdf, aes(x = "", y = value)) +
geom_violin() +
geom_dotplot(binaxis = "y", stackdir = "center") +
facet_wrap(~ variable, scales = "free", nrow = 3) +
theme_bw() +
ylab("Value") +
theme(axis.title.x = element_blank())
print(p)
```
Note that the above truncation is based on statistics derived from each
individual simulated dataset. To implement truncation based on statistics
derived from the data generating mechanism itself, one can 1) simulate a
reasonably large, untruncated dataset, 2) derive the desired upper and lower
truncation thresholds, and 3) add `process_truncate_by_threshold` with the
derived truncation thresholds to the simulator object to truncate by
fixed tresholds in every simulation run. Truncation can be done independently
for lower and upper thresholds (see example below).
```{r}
sim_design$process_final = list(
process_truncate_by_threshold = list(
truncate_upper = c(v8 = 200, v7 = 300),
truncate_lower = c(v6 = 2)
)
)
sim_data = simdata::simulate_data(sim_design, n_obs = 100, seed = 25897165)
```
Compare the results with the [results before](#simfinal) and note the
truncation in the specified variables (`v6, v7, v8`).
```{r echo=FALSE, message=FALSE, warning=FALSE}
numeric_vars = names(sim_design$types_final)[sim_design$types_final == "numeric"]
plotdf = melt(sim_data[, numeric_vars])
ggplot(plotdf, aes(x = "", y = value)) +
geom_violin() +
geom_dotplot(binaxis = "y", stackdir = "center") +
facet_wrap(~ variable, scales = "free", nrow = 3) +
theme_bw() +
ylab("Value") +
theme(axis.title.x = element_blank())
```
### Standardization
Similarly, standardization can be applied using the standard `scale` function,
changing the scale on the y-axis of the following plots.
Note that the order of functions passed via `process_final` is important.
Since `base::scale()` returns a `matrix`, we also want to transform the
result back to a `data.frame` in the end.
```{r}
sim_design$process_final = list(
process_truncate_by_iqr = list(
truncate_multipliers = c(v6 = 2, v7 = 2, v8 = 2)
),
scale = list(),
data.frame = list()
)
sim_data = simdata::simulate_data(sim_design, n_obs = 100, seed = 25897165)
```
```{r echo=FALSE, message=FALSE, warning=FALSE}
numeric_vars = names(sim_design$types_final)[sim_design$types_final == "numeric"]
plotdf = melt(sim_data[, numeric_vars])
ggplot(plotdf, aes(x = "", y = value)) +
geom_violin() +
geom_dotplot(binaxis = "y", stackdir = "center") +
facet_wrap(~ variable, scales = "free", nrow = 3) +
theme_bw() +
ylab("Value") +
theme(axis.title.x = element_blank())
```
# Advanced simulation setup
In the following we briefly present some more advanced usage aspects of
the package.
## Rejection sampling
A very simple form of rejection sampling is implemented in the
`simulate_data_conditional` function, which only accepts a final dataset if
it fulfills some specified conditions. This can be useful to prevent issues
during simulation runs when the simulated data is e.g. likely to produce
collinear matrices due to high dependencies between the variables or if some
of them produce low variance variables.
Note that such an approach can lead to serious bias in the simulations if the
rejection rate is very high. Thus, it might be necessary to revise such a
setup or at least record the number of rejections for reporting purposes
(as facilitated by the `return_tries` option of the function).
Usually rejections should occur very rarely. In this case the
`simulate_data_conditional` function can be used to let the simulation run
smoothly or let the calling function decide on how to handle rejections.
To show an example, we set up a very simple simulation with two variables and
make them collinear on purpose via a transformation.
- The `is_collinear` function from this package is used to check for collinearity
- After at most three runs with rejected datasets the function returns
(`reject_max_iter`)
- The number of tries is returned in a list (`return_tries`)
```{r}
correlation_matrix = diag(1, nrow = 2)
transformation = simdata::function_list("v1" = function(x) x[, 1],
"v1*2" = function(x) x[, 1] * 2,
check.names = FALSE)
sim_design = simdata::simdesign_mvtnorm(
relations = correlation_matrix,
transform_initial = transformation,
prefix_final = NULL
)
# ignoring the collinearity
sim_data = simdata::simulate_data(sim_design, n_obs = 100, seed = 2)
knitr::kable(cor(sim_data))
# rejecting collinear matrices
sim_data = simdata::simulate_data_conditional(sim_design,
n_obs = 100, seed = 2,
reject = is_collinear,
reject_max_iter = 3,
return_tries = TRUE)
sim_data
```
Note that multiple conditions can be checked by passing a `function_list` as
rejection function. All of them must be fulfilled or the matrix is rejected.
In our example below we randomly transform the columns to be collinear or
constant. However, in the end we obtain a result which passes both checks.
```{r}
correlation_matrix = diag(1, nrow = 3)
transformation = simdata::function_list(
"v1" = function(x) x[, 1],
"might_be_collinear" = function(x) {
if (rbinom(1, 1, 0.5)) {
return(x[, 1] * 2)
} else return(x[, 2])
},
"might_be_constant" = function(x) {
if (rbinom(1, 1, 0.5)) {
return(0)
} else return(x[, 3])
},
check.names = FALSE)
sim_design = simdata::simdesign_mvtnorm(
relations = correlation_matrix,
transform_initial = transformation,
prefix_final = NULL
)
sim_data = simdata::simulate_data_conditional(
sim_design,
n_obs = 100, seed = 3,
reject = simdata::function_list(is_collinear,
contains_constant),
reject_max_iter = 3,
return_tries = TRUE)
sprintf("Number of tries: %d", sim_data[[2]])
knitr::kable(head(sim_data[[1]]))
```
## User defined `simdesign` classes
### Other initial distributions {#other}
In this package, simulating initial data from a multivariate Normal distribution
is already implemented in the `simdesign_mvtnorm` S3 class. However, it is easy
to implement other distributions and extend the underlying interface of the
`simdesign` class. To do this, all that needs to be implemented is the
`generator` object of the new simulation class. Below we show how to code
a toy wrapper to simulate binary data using the `stats::rbinom` function.
- A new class must implement a `generator` function which takes one argument
(number of observations to simulate) and outputs a two-dimensional array
(matrix or data.frame)
- A new class should always use `...` to pass further arguments to `simdesign`
- A new class should extend the class attribute to facilitate writing
S3 methods which recognize the newly implemented class
```{r}
binomial_simdesign <- function(size = 1, prob = 0.5, ...) {
# define generator function
# make sure it returns a two-dimensional array
generator = function(n) matrix(rbinom(n, size = size, prob = prob), ncol = 1)
# setup simdesign object
# make sure to pass generator function and ...
# all other information passed is optional
dsgn = simdata::simdesign(
generator = generator,
size = size,
prob = prob,
...
)
# extend the class attribute
class(dsgn) = c("binomial_simdesign", class(dsgn))
# return the object
dsgn
}
```
Finally, we can use the newly created class as in the examples before.
```{r}
sim_design = binomial_simdesign(size = 1, prob = 0.7)
sim_data = simdata::simulate_data(sim_design, 100, seed = 1)
knitr::kable(table(sim_data))
```
### Using real data
The package can also be easily extended to be used as wrapper for resampling
real datasets. Similar to the [example above](#other), this is easily
accomplished by extending the `simdesign` class as shown below.
Here we implement a simple bootstrap procedure by sampling randomly with
replacement from the dataset. Many other resampling techniques could be
created similarly.
```{r}
realdata_simdesign <- function(dataset, ...) {
# define generator function
# make sure it returns a two-dimensional array
generator = function(n) dataset[sample(1:nrow(dataset), n, replace = TRUE), ,
drop = FALSE]
# setup simdesign object
# make sure to pass generator function and ...
# all other information passed is optional
dsgn = simdata::simdesign(
generator = generator,
dataset = dataset,
...
)
# extend the class attribute
class(dsgn) = c("realdata_simdesign", class(dsgn))
# return the object
dsgn
}
```
Note that this works because the dataset is saved to the environment of
the generator function and therefore always accessible to the generator
function. It can be retrieved via
`get("dataset", envir = environment(sim_design$generator))`.
Finally, we can use the newly created class as in the examples before.
```{r}
data(iris)
sim_design = realdata_simdesign(iris, prefix_final = NULL)
sim_data = simdata::simulate_data(sim_design, 100, seed = 1)
knitr::kable(head(sim_data))
```
## Aspects of high-dimensional setups
In high-dimensional simulation studies the number of simulated variables is large.
We briefly demonstrate how to simulate 100 variables from a multivariate
Normal distribution with block correlation matrix. This shows how to use
the package functionality programmatically.
- Create a block diagonal matrix easily using `base::expand.grid` and
`cor_from_upper`
- Note that for 1000s of variables drawing from a single multivariate Normal
distribution using `mvtnorm::rmvnorm` is highly inefficient and could be
replaced by drawing from individual blocks of multivariate Normal
distributions of smaller dimension
- Transform the variables blockwise by creating a `function_list` from a list
of functions via `as_function_list`
- Note the use of `base::substitute` to construct functions programmatically
(`substitute` actually substitutes the passed value for `i` instead of the
symbol `i` in the function definition)
- Note that the final transformation is applied to a whole submatrix at once
(this is possible as long as the resulting outputs have equal number of
rows)
```{r}
correlation_matrix = simdata::cor_from_upper(
100,
entries = rbind(
expand.grid(1:30, 1:30, 0.5),
expand.grid(31:50, 31:50, 0.2))
)
# create list of transformation functions programmatically
# For the first 60 variables:
# odd varibles will be translated
# even variables will be scaled
transformation = list()
for (i in 1:60) {
if (i %% 2) {
transformation[[i]] = substitute(function(x) x[, i] * 5, list(i = i))
} else transformation[[i]] = substitute(function(x) x[, i] - 10, list(i = i))
}
# the remaining are returned as they are
transformation[[61]] = function(x) x[, 61:100]
# construct single transformation function from the list
transformation = simdata::as_function_list(transformation)
# create simulation design
sim_design = simdata::simdesign_mvtnorm(
relations = correlation_matrix,
transform_initial = transformation
)
```
We can visualise the correlation matrix as before using `ggplot2`.
```{r echo=FALSE}
plotdf = melt(sim_design$cor_initial)
ggplot(plotdf, aes(x = factor(Var1), y = factor(Var2), fill = value)) +
geom_tile() +
scale_x_discrete(position = "top") +
scale_y_discrete(limits = rev) +
xlab(expr(paste("Initial variable ", z[i]))) +
ylab(expr(paste("Initial variable ", z[i]))) +
coord_equal() +
scale_fill_gradient2(
"Correlation",
limits = c(-1, 1),
low = "#377eb8", mid = "white", high = "#e41a1c") +
theme_bw() +
theme(axis.text = element_blank())
```
A correlation network can often be used to clearly display such large correlation
structures. To keep the network simple, we remove vertex and edge labels and
adjust vertex and edge sizes.
```{r}
simdata::plot_cor_network(sim_design, seed = 2,
vertex_labels = NA,
edge_label_function = NULL,
edge_width_function = function(x) 0.01,
edge_weight_function = function(x) 0.25 * x,
use_edge_weights = TRUE,
edge.color = "clipped-ramp",
vertex.size = 3)
```
The simulated data is subsequently obtained by applying the specified
transformations to the initial multivariate Normal data. It is visualised
below by means of density curves for each variable (for brevity the legend
is not displayed and variables are indicated by different colors).
```{r}
sim_data = simdata::simulate_data(sim_design, n_obs = 50, seed = 5)
```
```{r echo=FALSE, message=FALSE, warning=FALSE}
ggplot(melt(sim_data), aes(x = value, color = variable)) +
geom_density() +
scale_color_viridis_d(option = "plasma") +
guides(color = "none") +
xlab("Value") + ylab("Density") +
theme_bw()
```
## Parallelization {#parallel}
This example briefly shows how to use the package together with parallelization
via clusters to speed up simulations. On Unix-based systems, forking provides
an alternative method to parallelize code, which results in even simpler code
than the one below. We use the same setup as in [Example 2](#ex2).
The following code is not run to ensure that the vignette builds on any system,
but can be used as is on any system that has the `simdata` package installed.
- We set up a local cluster using 2 CPU threads via the `parallel` and
`doParallel` packages
- Alternative parallel implementations such as `future` can be used.
- Note that the random seed is now set via the options of `doRNG` in the
head of the `foreach` loop (and NOT within the call of `simulate_data`).
- Note that the `simdata` package needs to be explicitly passed to the
`foreach` loop such that every thread has access to the library. This is
Windows specific, on Unix-based systems this is not necessary as forking
automatically makes the library available to all child processes.
- Note that the global variable `sim_design` is passed to the threads
automatically, but this can be forced via the `.export` argument in the
`foreach` loop
- The results are stored in a list.
```{r eval=FALSE, include=TRUE}
correlation_matrix = diag(1, nrow = 5)
transformation = simdata::function_list(
"v1" = function(x) x[, 1],
"v2*2" = function(x) x[, 2] * 2,
"v3^2" = function(x) x[, 3]^2,
"v4+v5" = function(x) x[, 4] + x[, 5],
check.names = FALSE # pass columnnames exactly as specified here
)
sim_design = simdata::simdesign_mvtnorm(
relations = correlation_matrix,
transform_initial = transformation,
prefix_final = NULL
)
# parallelisation
cl = parallel::makeCluster(1)
doParallel::registerDoParallel(cl)
res = foreach(
i = 1:10,
.packages = c("simdata"),
.options.RNG = 1 # note that the seed is passed here
) %dorng% {
simulate_data(sim_design, n_obs = 100)
# do some task with the simulated data
}
parallel::stopCluster(cl)
knitr::kable(purrr::map(res[1:2], summary))
```
## Partial functions
The `simdata` packages makes heavy use of function objects to define simulation
designs (e.g. also NORTA based simulation). Sometimes it may be necessary to
fix parameters of such functions, or define them via code. In [Example 2](#ex2)
we created a variable `v2*2` by multiplying the initial data times 2.
What if we wanted to parametrize this? If we simply use a global variable
this will have undesirable consequences, as R may change the value of the
global value at any time (or remove the value, which leads to a failure of
the sampling procedure).
```{r}
# parameter
mult = 2
transformation = simdata::function_list(
"v1" = function(x) x[, 1] * mult # dangerous, depends on global variable!
)
sim_design = simdata::simdesign_mvtnorm(
relations = diag(1),
transform_initial = transformation
)
sample1 = simdata::simulate_data(sim_design, n_obs = 5, seed = 25897165)
# change value of global variable
mult = 4
sample2 = simdata::simulate_data(sim_design, n_obs = 5, seed = 25897165)
# note the different values in both columns
knitr::kable(cbind(sample1, sample2))
```
This can be avoided by the use of `simdata::partial`. This requires to
specify the transformation as function in two parameters, of which one is fixed.
```{r}
# parameter
mult = 2
transformation = simdata::function_list(
# specify function as partial
"v1" = simdata::partial(function(x, mult) x[, 1] * mult, mult = mult)
)
sim_design = simdata::simdesign_mvtnorm(
relations = diag(1),
transform_initial = transformation
)
sample1 = simdata::simulate_data(sim_design, n_obs = 5, seed = 25897165)
# change value of global variable
mult = 4
sample2 = simdata::simulate_data(sim_design, n_obs = 5, seed = 25897165)
# note both columns are equal now, as expected
knitr::kable(cbind(sample1, sample2))
```
`simdata::partial` can be used to fix an arbitrary number of parameters, and
may also help if a transformation is defined inside another function (as the
parameter set inside a function may not be available outside).
# R session information {#rsession}
```{r echo=FALSE}
sessionInfo()
```
# References