rdata.frame: Generate a random data.frame with tunable characteristics

In mi: Missing Data Imputation and Model Checking

Generate a random data.frame with tunable characteristics

Description

This function generates a random data.frame with a missingness mechanism that is used to impose a missingness pattern. The primary purpose of this function is for use in simulations

Usage

rdata.frame(N = 1000, 
            restrictions = c("none", "MARish", "triangular", "stratified", "MCAR"),
            last_CPC = NA_real_, strong = FALSE, pr_miss = .25, Sigma = NULL, 
            alpha = NULL, experiment = FALSE, 
            treatment_cor = c(rep(0, n_full - 1), rep(NA, 2 * n_partial)),
            n_full = 1, n_partial = 1, n_cat = NULL,
            eta = 1, df = Inf, types = "continuous", estimate_CPCs = TRUE)

Arguments

N	integer indicating the number of observations
restrictions	character string indicating what restrictions to impose on the the missing data mechansim, see the Details section
last_CPC	a numeric scalar between -1 and 1 exclusive or NA_real_ (the default). If not NA_real_, then this value will be used to construct the correlation matrix from which the data are drawn. This option is useful if restrictions is "triangular" or "stratified", in which case the degree to which last_CPC is not zero causes a violation of the Missing-At-Random assumption that is confined to the last of the partially observed variables
strong	Integer among 0, 1, and 2 indicating how strong to make the instruments with multiple partially observed variables, in which case the missingness indicators for each partially observed variable can be used as instruments when predicting missingness on other partially observed variables. Only applies when restrictions = "triangular"
pr_miss	numeric scalar on the (0,1) interval or vector of length n_partial indicating the proportion of observations that are missing on partially observed variables
Sigma	Either NULL (the default) or a correlation matrix of appropriate order for the variables (including the missingness indicators). By default, such a matrix is generated at random.
alpha	Either NULL, NA, or a numeric vector of appropriate length that governs the skew of a multivariate skewed normal distribution; see rmsn. The appropriate length is n_full - 1 + 2 * n_partial iff none of the variable types is nominal. If some of the variable types are nominal, then the appropriate length is n_full - 1 + 2 * n_partial + sum(n_cat) - length(n_cat). If NULL, alpha is taken to be zero, in which case the data-generating process has no skew. If NA, alpha is drawn from rt with df degrees of freedom
experiment	logical indicating whether to simulate a randomized experiment
treatment_cor	Numeric vector of appropriate length indicating the correlations between the treatment variable and the other variables, which is only relevant if experiment = TRUE. The appropriate length is n_full - 1 + 2 * n_partial iff none of the variable types is nominal. If some of the variable types are nominal, then the appropriate length is n_full - 1 + 2 * n_partial + sum(n_cat) - length(n_cat). If treatment_cor is of length one and is zero, then it will be recylced to the appropriate length. The treatment variable should be uncorrelated with intended covariates and uncorrelated with missingness on intended covariates. If any elements of treatment_cor are NA, then those elements will be replaced with random draws. Note that the order of the random variables is: all fully observed variables,all partially observed but not nominal variables, all partially observed nominal variables, all missingness indicators for partially observed variables.
n_full	integer indicating the number of fully observed variables
n_partial	integer indicating the number of partially observed variables
n_cat	Either NULL or an integer vector (possibly of length one) indicating the number of categories in each partially observed nominal or ordinal variable; see the Details section
eta	Positive numeric scalar which serves as a hyperparameter in the data-generating process. The default value of 1 implies that the correlation matrix among the variables is jointly uniformally distributed, using essentially the same logic as in the clusterGeneration package
df	positive numeric scalar indicating the degress of freedom for the (possibly skewed) multivariate t distribution, which defaults to Inf implying a (possibly skewed) multivariate normal distribution
types	a character vector (possibly of length one, in which case it is recycled) indicating the type for each fully observed and partially observed variable, which currently can be among "continuous", "count", "binary", "treatment" (which is binary), "ordinal", "nominal", "proportion", "positive". See the Details section. Unique abbreviations are acceptable.
estimate_CPCs	A logical indicating whether the canonical partial correlations between the partially observed variables and the latent missingnesses should be estimated. The default is TRUE but considerable wall time can be saved by switching it to FALSE when there are many partially observed variables.

Details

By default, the correlation matrix among the variables and missingness indicators is intended to be close to uniform, although it is often not possible to achieve exactly. If restrictions = "none", the data will be Not Missing At Random (NMAR). If restrictions = "MARish", the departure from Missing At Random (MAR) will be minimized via a call to optim, but generally will not fully achieve MAR. If restrictions = "triangular", the MAR assumption will hold but the missingness of each partially observed variable will only depend on the fully observed variables and the other latent missingness indicators. If restrictions = "stratified", the MAR assumption will hold but the missingness of each partially observed variable will only depend on the fully observed variables. If restrictions = "MCAR", the Missing Completely At Random (MCAR) assumption holds, which is much more restrictive than MAR.

There are some rules to follow, particularly when specifying types. First, if experiment = TRUE, there must be exactly one treatment variable (taken to be binary) and it must come first to ensure that the elements of treatment_cor are handled properly. Second, if there are any partially observed nominal variables, they must come last; this is to ensure that they are conditionally uncorrelated with each other. Third, fully observed nominal variables are not supported, but they can be made into ordinal variables and then converted to nominal after the fact. Fourth, including both ordinal and nominal partially observed variables is not supported yet, Finally, if any variable is specified as a count, it will not be exactly consistent with the data-generating process. Essentially, a count variable is constructed from a continuous variable by evaluating pt on it and passing that to qpois with an intensity parameter of 5. The other non-continuous variables are constructed via some transformation or discretization of a continuous variable.

If some partially observed variables are either ordinal or nominal (but not both), then the n_cat argument governs how many categories there are. If n_cat is NULL, then the number of categories defaults to three. If n_cat has length one, then that number of categories will be used for all categorical variables but must be greater than two. Otherwise, the length of n_cat must match the number of partially observed categorical variables and the number of categories for the ith such variable will be the ith element of n_cat.

Value

A list with the following elements:

1. true a data.frame containing no NA values

2. obs a data.frame derived from the previous with some NA values that represents a dataset that could be observed

3. empirical_CPCs a numeric vector of empirical Canonical Partial Correlations, which should differ only randomly from zero iff MAR = TRUE and the data-generating process is multivariate normal

4. L a Cholesky factor of the correlation matrix used to generate the true data

In addition, if alpha is not NULL, then the following elements are also included:

1. alpha the alpha vector utilized

2. sn_skewness the skewness of the multivariate skewed normal distribution in the population; note that this value is only an approximation of the skewness when df < Inf

3. sn_kurtosis the kurtosis of the multivariate skewed normal distribution in the population; note that this value is only an approximation of the kurtosis when df < Inf

Author(s)

Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.

See Also

data.frame, missing_data.frame

Examples

rdf <- rdata.frame(n_partial = 2, df = 5, alpha = rnorm(5))
print(rdf$empirical_CPCs) # not zero
rdf <- rdata.frame(n_partial = 2, restrictions = "triangular", alpha = NA)
print(rdf$empirical_CPCs) # only randomly different from zero
print(rdf$L == 0) # some are exactly zero by construction
mdf <- missing_data.frame(rdf$obs)
show(mdf)
hist(mdf)
image(mdf)
# a randomized experiment
rdf <- rdata.frame(n_full = 2, n_partial = 2, 
                   restrictions = "triangular", experiment = TRUE,
                   types = c("t", "ord", "con", "pos"),
                   treatment_cor = c(0, 0, NA, 0, NA))
Sigma <- tcrossprod(rdf$L)
rownames(Sigma) <- colnames(Sigma) <- c("treatment", "X_2", "y_1", "Y_2",
                                        "missing_y_1", "missing_Y_2")
print(round(Sigma, 3))

mi documentation built on June 7, 2022, 1:04 a.m.