Obtain Treatment from Design

When passing a lm object to lmitt(), extract and use the treatment variable specified in the Design.

Usage

assigned(design = NULL, data = NULL, dichotomy = NULL)

adopters(design = NULL, data = NULL, dichotomy = NULL)

a.(design = NULL, data = NULL, dichotomy = NULL)

z.(design = NULL, data = NULL, dichotomy = NULL)

Arguments

design

Optional Design. If the Design can't be identified in the model (usually because neither weights (ate() or ett()) nor a covariate adjustment model (cov_adj()) are found), the Design can be passed diretly.

data

Optional data set. By default assigned() will attempt to identify the appropriate data, if this fails (or you want to overwrite it), you can pass the data here.

dichotomy

optional; a formula defining the dichotomy of the treatment variable if it isn't already 0/1. See details for more information. If ett() or ate() is called within a lmitt() call that specifies a dichotomy argument, that dichotomy will be used if the argument here has not been specified.

Value

The treatment variable to be placed in the regression formula.

Details

When passing a lm object to lmitt(), the treatment variable in the formula passed to lm() needs to be identifiable. Rather than placing the treatment variable directly in the formula, use one of these functions, to allow lmitt() to identify the treatment variable.

To keep the formula in the lm() call concise, instead of passing design and data arguments to these functions, one can pass a WeightedDesign object to the weights argument of the lm() call or a SandwichLayer object to the offset argument.

Alternatively, you can pass the design and data arguments.

While assigned() can be used in any situation, it is most useful for scenarios where the treatment variable is non-binary and the Design contains a Dichotomy. For example, say q is a 3-level ordinal treatment variable, and the binary comparison of interest is captured in dichotomy = q == 3 ~ q < 3. If you were to fit a model including q as a predictor, e.g. lm(y ~ q, ...), lm would treat q as the full ordinal variable. On the other hand, by calling lm(y ~ assigned(), weights = ate(des), ...), assigned() will generate the appropriate binary variable to allow estimation of treatment effects.

If called outside of a model call and without a data argument, this will extract the treatment from the design. If this is the goal, the treatment() function is better suited for this purpose.

Examples

data(simdata)
des <- obs_design(z ~ uoa(uoa1, uoa2), data = simdata)
mod <- lm(y ~ assigned(), data = simdata, weights = ate(des))
lmittmod <- lmitt(mod)
summary(lmittmod)
#> 
#> Call:
#> lmitt(mod)
#> 
#>  Coefficients :
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)  -0.0090     0.1734  -0.052    0.959
#> assigned()    0.1205     0.2301   0.524    0.603
#> Std. Error calculated via type "CR0"
#>