`weightit`

object manually`as.weightit.Rd`

This function allows users to get the benefits of a `weightit`

object when using weights not estimated with `weightit()`

or `weightitMSM()`

. These benefits include diagnostics, plots, and direct compatibility with cobalt for assessing balance.

```
as.weightit(...)
# S3 method for default
as.weightit(weights,
treat,
covs = NULL,
estimand = NULL,
s.weights = NULL,
ps = NULL,
...)
as.weightitMSM(...)
# S3 method for default
as.weightitMSM(weights,
treat.list,
covs.list = NULL,
estimand = NULL,
s.weights = NULL,
ps.list = NULL,
...)
```

- weights
required; a

`numeric`

vector of weights, one for each unit.- treat
required; a vector of treatment statuses, one for each unit.

- covs
an optional

`data.frame`

of covariates. For using WeightIt functions, this is not necessary, but for use with cobalt it is.- estimand
an optional

`character`

of length 1 giving the estimand. The text is not checked.- s.weights
an optional

`numeric`

vector of sampling weights, one for each unit.- ps
an optional

`numeric`

vector of propensity scores, one for each unit.- treat.list
a list of treatment statuses at each time point..

- covs.list
an optional list of

`data.frame`

s of covariates of covariates at each time point. For using WeightIt functions, this is not necessary, but for use with cobalt it is.- ps.list
an optional list of

`numeric`

vectors of propensity scores at each time point.- ...
additional arguments. These must be named. They will be included in the output object.

An object of class `weightit`

(for `as.weightit()`

) or `weightitMSM`

(for `as.weightitMSM()`

).

```
treat <- rbinom(500, 1, .3)
weights <- rchisq(500, df = 2)
W <- as.weightit(weights= weights, treat = treat,
estimand = "ATE")
summary(W)
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 0.0345 |---------------------------| 12.9095
#> control 0.0020 |---------------------------| 12.8696
#>
#> - Units with 5 most extreme weights by group:
#>
#> 3 85 197 193 1
#> treated 8.2941 8.9772 10.2539 10.3918 12.9095
#> 226 352 485 131 330
#> control 10.2435 10.3664 10.592 10.7309 12.8696
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 1.131 0.785 0.486 0
#> control 1.016 0.716 0.418 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 344. 156.
#> Weighted 169.6 68.68
```