Dynamic Data Definition

odds <- function (p)  p/(1 - p) # TODO temporary remove when added to package
plotcolors <- c("#B84226", "#1B8445", "#1C5974")

cbbPalette <- c("#B84226","#B88F26", "#A5B435", "#1B8446",
                "#B87326","#B8A526", "#6CA723", "#1C5974") 

ggtheme <- function(panelback = "white") {

    panel.background = element_rect(fill = panelback),
    panel.grid = element_blank(),
    axis.ticks =  element_line(colour = "black"),
    panel.spacing =unit(0.25, "lines"),  # requires package grid
    panel.border = element_rect(fill = NA, colour="gray90"), 
    plot.title = element_text(size = 8,vjust=.5,hjust=0),
    axis.text = element_text(size=8),
    axis.title = element_text(size = 8)


Often, we'd like to explore data generation and modeling under different scenarios. For example, we might want to understand the operating characteristics of a model given different variance or other parametric assumptions. There is functionality built into simstudy to facilitate this type of dynamic exploration. First, there are functions updateDef and updateDefAdd that essentially allow one to edit lines of existing data definition tables. Second, there is a built in mechanism - called double-dot reference - to access external variables that do not exist in a defined data set or data definition.

Updating existing definition tables

The updateDef function updates a row in a definition table created by functions defData or defRead. Analogously, updateDefAdd function updates a row in a definition table created by functions defDataAdd or defReadAdd.

The original data set definition includes three variables x, y, and z, all normally distributed:

defs <- defData(varname = "x", formula = 0, variance = 3, dist = "normal")
defs <- defData(defs, varname = "y", formula = "2 + 3*x", variance = 1, dist = "normal")
defs <- defData(defs, varname = "z", formula = "4 + 3*x - 2*y", variance = 1, dist = "normal")


In the first case, we are changing the relationship of y with x as well as the variance:

defs <- updateDef(dtDefs = defs, changevar = "y", newformula = "x + 5", newvariance = 2)

In this second case, we are changing the distribution of z to Poisson and updating the link function to log:

defs <- updateDef(dtDefs = defs, changevar = "z", newdist = "poisson", newlink = "log")

And in the last case, we remove a variable from a data set definition. Note in the case of a definition created by defData that it is not possible to remove a variable that is a predictor of a subsequent variable, such as x or y in this case.

defs <- updateDef(dtDefs = defs, changevar = "z", remove = TRUE)

Double-dot external variable reference

For a truly dynamic data definition process, simstudy (as of version 0.2.0) allows users to reference variables that exist outside of data generation. These can be thought of as a type of hyperparameter of the data generation process. The reference is made directly in the formula itself, using a double-dot ("..") notation before the variable name. Here is a simple example:

def <- defData(varname = "x", formula = 0, 
  variance = 5, dist = "normal")
def <- defData(def, varname = "y", formula = "..B0 + ..B1 * x", 
  variance = "..sigma2", dist = "normal")

B0 <- 4;
B1 <- 2;
sigma2 <- 9

dd <- genData(100, def)

fit <- summary(lm(y ~ x, data = dd))


It is easy to create a new data set on the fly with a difference variance assumption without having to go to the trouble of updating the data definitions.

sigma2 <- 16

dd <- genData(100, def)
fit <- summary(lm(y ~ x, data = dd))


The double-dot notation can be flexibly applied using lapply (or the parallel version mclapply) to create a range of data sets under different assumptions:

sigma2s <- c(1, 2, 6, 9)

gen_data <- function(sigma2, d) {
  dd <- genData(200, d)
  dd$sigma2 <- sigma2

dd_4 <- lapply(sigma2s, function(s) gen_data(s, def))
dd_4 <- rbindlist(dd_4)

ggplot(data = dd_4, aes(x = x, y = y)) +
  geom_point(size = .5, color = "grey30") +
  facet_wrap(sigma2 ~ .) +
  theme(panel.grid = element_blank())

The double-dot notation is also array-friendly. For example if we want to create a mixture distribution from a vector of values (which we can also do using a categorical distribution), we can define the mixture formula in terms of the vector. In this case we are generating permuted block sizes of 2 and 4:

defblk <- defData(varname = "blksize", 
   formula = "..sizes[1] | .5 + ..sizes[2] | .5", dist = "mixture")

sizes <- c(2, 4)
genData(1000, defblk)

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simstudy documentation built on Oct. 23, 2020, 6:55 p.m.