bal.tab.time.list {cobalt} | R Documentation |
Balance Statistics for Longitudinal Datasets
Description
Generates balance statistics for data coming from a longitudinal treatment scenario. The primary input is in the form of a list of formulas or data.frame
s contain the covariates at each time point. bal.tab()
automatically classifies this list as either a data.frame.list
or formula.list
, respectively.
Usage
## S3 method for class 'formula.list'
bal.tab(
x,
stats,
int = FALSE,
poly = 1,
distance = NULL,
addl = NULL,
data = NULL,
continuous,
binary,
s.d.denom,
thresholds = NULL,
weights = NULL,
cluster = NULL,
imp = NULL,
pairwise = TRUE,
s.weights = NULL,
abs = FALSE,
subset = NULL,
quick = TRUE,
...
)
## S3 method for class 'data.frame.list'
bal.tab(
x,
treat.list,
stats,
int = FALSE,
poly = 1,
distance = NULL,
addl = NULL,
data = NULL,
continuous,
binary,
s.d.denom,
thresholds = NULL,
weights = NULL,
cluster = NULL,
imp = NULL,
pairwise = TRUE,
s.weights = NULL,
abs = FALSE,
subset = NULL,
quick = TRUE,
...
)
Arguments
x |
either a list of data frames containing all the covariates to be assessed at each time point or a list of formulas with the treatment for each time period on the left and the covariates for which balance is to be displayed on the right. Covariates to be assessed at multiple points must be included in the entries for each time point. Data must be in the "wide" format, with one row per unit. If a formula list is supplied, an argument to |
stats |
|
int |
|
poly |
|
distance |
an optional formula or data frame containing distance values (e.g., propensity scores) or a character vector containing their names. If a formula or variable names are specified, |
addl |
an optional formula or data frame containing additional covariates for which to present balance or a character vector containing their names. If a formula or variable names are specified, |
data |
an optional data frame containing variables named in other arguments. For some input object types, this is required. |
continuous |
whether mean differences for continuous variables should be standardized ( |
binary |
whether mean differences for binary variables (i.e., difference in proportion) should be standardized ( |
s.d.denom |
|
thresholds |
a named vector of balance thresholds, where the name corresponds to the statistic (i.e., in |
weights |
a vector, list, or |
cluster |
either a vector containing cluster membership for each unit or a string containing the name of the cluster membership variable in |
imp |
either a vector containing imputation indices for each unit or a string containing the name of the imputation index variable in |
pairwise |
whether balance should be computed for pairs of treatments or for each treatment against all groups combined. See |
s.weights |
Optional; either a vector containing sampling weights for each unit or a string containing the name of the sampling weight variable in |
abs |
|
subset |
a |
quick |
|
... |
for some input types, other arguments that are required or allowed. Otherwise, further arguments to control display of output. See display options for details. |
treat.list |
treatment status for each unit at each time point. This can be specified as a list or data frame of vectors, each of which contains the treatment status of each individual at each time point, or a list or vector of the names of variables in |
Details
bal.tab.formula.list()
and bal.tab.data.frame.list()
generate a list of balance summaries for each time point based on the treatments and covariates provided. All data must be in the "wide" format, with exactly one row per unit and columns representing variables at different time points. See the WeightIt::weightitMSM()
documentation for an example of how to transform long data into wide data using reshape()
.
Multiple sets of weights can be supplied simultaneously by including entering a data frame or a character vector containing the names of weight variables found in data
or a list thereof. When only one set of weights is supplied, the output for the adjusted group will simply be called "Adj"
, but otherwise will be named after each corresponding set of weights. Specifying multiple sets of weights will also add components to other outputs of bal.tab()
.
Value
An object of class bal.tab.msm
containing balance summaries at each time point. Each balance summary is its own bal.tab
object. See class-bal.tab.msm
for more details.
See bal.tab() base methods()
for more detailed information on the value of the bal.tab
objects produced for each time point.
See Also
-
bal.tab()
for details of calculations. -
class-bal.tab.msm
for output and related options. -
class-bal.tab.cluster
for more information on clustered data. -
class-bal.tab.imp
for more information on multiply imputed data. -
bal.tab.multi()
for more information on multi-category treatments.
Examples
data("msmdata", package = "WeightIt")
## Estimating longitudinal propensity scores and weights
ps1 <- glm(A_1 ~ X1_0 + X2_0,
data = msmdata,
family = "binomial")$fitted.values
w1 <- ifelse(msmdata$A_1 == 1, 1 / ps1, 1 / (1 - ps1))
ps2 <- glm(A_2 ~ X1_1 + X2_1 +
A_1 + X1_0 + X2_0,
data = msmdata,
family = "binomial")$fitted.values
w2 <- ifelse(msmdata$A_2 == 1, 1 / ps2, 1 / (1 - ps2))
ps3 <- glm(A_3 ~ X1_2 + X2_2 +
A_2 + X1_1 + X2_1 +
A_1 + X1_0 + X2_0,
data = msmdata,
family = "binomial")$fitted.values
w3 <- ifelse(msmdata$A_3 == 1, 1 / ps3, 1 / (1 - ps3))
w <- w1 * w2 * w3
# Formula interface plus addl:
bal.tab(list(A_1 ~ X1_0 + X2_0,
A_2 ~ X1_1 + X2_1 +
A_1 + X1_0 + X2_0,
A_3 ~ X1_2 + X2_2 +
A_2 + X1_1 + X2_1 +
A_1 + X1_0 + X2_0),
data = msmdata,
weights = w,
distance = list(~ps1, ~ps2, ~ps3),
addl = ~X1_0 * X2_0,
un = TRUE)
# data frame interface:
bal.tab(list(msmdata[c("X1_0", "X2_0")],
msmdata[c("X1_1", "X2_1", "A_1", "X1_0", "X2_0")],
msmdata[c("X1_2", "X2_2", "A_2", "X1_1", "X2_1",
"A_1", "X1_0", "X2_0")]),
treat.list = msmdata[c("A_1", "A_2", "A_3")],
weights = w,
distance = list(~ps1, ~ps2, ~ps3),
un = TRUE)