smdi_diagnose {smdi} | R Documentation |
Computes three group missing data summary diagnostics
Description
This function bundles and calls all three group diagnostics and returns the most important summary metrics. For more information and details, please refer to the individual functions.
Important: don't include variables like ID variables, ZIP codes, dates, etc.
Usage
smdi_diagnose(
data = NULL,
covar = NULL,
median = TRUE,
includeNA = FALSE,
train_test_ratio = c(0.7, 0.3),
set_seed = 42,
ntree = 1000,
n_cores = 1,
model = c("logistic", "linear", "cox"),
form_lhs = NULL,
exponentiated = FALSE
)
Arguments
data |
dataframe or tibble object with partially observed/missing variables |
covar |
character covariate or covariate vector with partially observed variable/column name(s) to investigate. If NULL, the function automatically includes all columns with at least one missing observation and all remaining covariates will be used as predictors |
median |
logical if the median (= TRUE; recommended default) or mean of all absolute standardized mean differences (asmd) should be computed (smdi_asmd()) |
includeNA |
logical, should missingness of other partially observed covariates be explicitly modeled for computation of absolute standardized mean differences (default is FALSE) |
train_test_ratio |
numeric vector to indicate the test/train split ratio for random forest missingness prediction model, e.g. c(.7, .3) is the default |
set_seed |
seed for reproducibility of random forest missingness prediction model, defaults to 42 |
ntree |
integer, number of trees for random forest missingness prediction model (defaults to 1000 trees) |
n_cores |
integer, if >1, computations will be parallelized across amount of cores specified in n_cores (only UNIX systems) |
model |
character describing which outcome model to fit to assess the association between covar missingness indicator and outcome. Currently supported are models of type logistic, linear and cox (see smdi_outcome) |
form_lhs |
string specifying the left-hand side of the outcome formula (see smdi_outcome) |
exponentiated |
logical, should results of outcome regression to assess association between missingness and outcome be exponentiated (default is FALSE) |
Details
Wrapper for individual diagnostics function.
Value
smdi object including a summary table of all three smdi group diagnostics:
Group 1 diagnostic:
-
asmd_mean
orasmd_median
: average/median absolute standardized mean difference (and min, max) of patient characteristics between those without (1) and with (0) observed covariate hotteling_p: p-value of hotelling test. Rejecting the H0 means that Hotelling's test detects a significant difference in the distribution between patients without (1) and with (0) the observed covariate
Group 2 diagnostic:
-
rf_auc
: The area under the receiver operating curve (AUC) as a measure of the ability to predict the missingness of the partially observed covariate
Group 3 diagnostic:
-
estimate_univariate
: univariate association between missingness indicator of covar and outcome -
estimate_adjusted
: association between missingness indicator of covar and outcome conditional on other fully observed covariates and missing indicator variables of other partially observed covariates
References
TBD
See Also
smdi_asmd
smdi_hotelling
smdi_little
smdi_rf
smdi_outcome
Examples
library(smdi)
smdi_diagnose(
data = smdi_data,
covar = "egfr_cat",
model = "cox",
form_lhs = "Surv(eventtime, status)"
)