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Covariance adjustment of teeMod model estimates

Source: R/cov_adj.R
cov_adj.Rd

cov_adj() takes a fitted covariance model and returns the information necessary for adjusting direct adjustment model estimates and associated standard errors for covariates. Standard errors will reflect adjustments made to the outcomes as well as contributions to sampling variability arising from the estimates of the covariance adjustment model coefficients.

Usage

cov_adj(model, newdata = NULL, specification = NULL, by = NULL)

Arguments

model

any model that inherits from a glm, lm, or robustbase::lmrob object

newdata

a dataframe of new data. Default is NULL, in which case a dataframe is sought from higher up the call stack.

specification

a StudySpecification object. Default is NULL, in which case a StudySpecification object is sought from higher up the call stack.

by

optional; a string or named vector of unique identifier columns in the data used to create specification and the data used to fit the covariance adjustment model. Default is NULL, in which case unit of assignment columns are used for identification (even if they do not uniquely identify units of observation). If a named vector is provided, names should represent variables in the data used to create specification, while values should represent variables in the covariance adjustment data.

Value

A SandwichLayer if specification is not NULL or a StudySpecification object is found in the call stack, otherwise a PreSandwichLayer object

Details

Prior to generating adjustments, cov_adj() identifies the treatment variable specified in the StudySpecification object passed to specification and replaces all values with a reference level. If the treatment has logical type, this reference level is FALSE, and if it has numeric type, this is the smallest non-negative value (which means 0 for 0/1 binary). Factor treatments are not currently supported for StudySpecification objects.

The values of the output vector represent adjustments for the outcomes in newdata if newdata is provided; adjustments for the outcomes in the data used to fit a teeMod model if cov_adj() is called within the offset argument of the model fit; or they are the fitted values from model if no relevant dataframe can be extracted from the call stack. The length of the output of cov_adj() will match the number of rows of the dataframe used.

Examples

data(STARdata)
STARdata$treatment <- STARdata$stark == "small"
STARdata$treatment[is.na(STARdata$treatment)] <- FALSE
STARdata$studentid <- as.character(seq_len(nrow(STARdata)))

covariance_y0_read <- lm(readk ~ gender + ethnicity + lunchk +
                                 ladderk + experiencek + tethnicityk,
                         data = STARdata, subset = !treatment)

STARdata_spec <- rct_spec(treatment ~ cluster(studentid), data = STARdata)
STARdata_ate    <- ate(STARdata_spec, data = STARdata)
STARdata_ett    <- ett(STARdata_spec, data = STARdata)

ett_read <- lm(readk ~ treatment,
               offset = cov_adj(covariance_y0_read, newdata = STARdata),
               data = STARdata,
               weights = STARdata_ett)
#> Warning: Some covariance adjustments are NA; be careful of dropping these observations when fitting the ITT effect model
coef(ett_read)
#>   (Intercept) treatmentTRUE 
#> -1.079602e-13  5.554179e+00 

ate_read <- lm(readk ~ treatment,
               offset = cov_adj(covariance_y0_read, newdata = STARdata),
               data = STARdata,
               weights = STARdata_ate)
#> Warning: Some covariance adjustments are NA; be careful of dropping these observations when fitting the ITT effect model
coef(ate_read)
#>   (Intercept) treatmentTRUE 
#> -1.022791e-13  5.554179e+00 

ate_read_eth <- lm(readk ~ treatment * ethnicity,
                   offset = cov_adj(covariance_y0_read, newdata = STARdata),
                   data = STARdata,
                   weights = STARdata_ate)
#> Warning: Some covariance adjustments are NA; be careful of dropping these observations when fitting the ITT effect model
coef(ate_read_eth)
#>                     (Intercept)                   treatmentTRUE 
#>                   -9.603676e-14                    5.901494e+00 
#>                   ethnicityafam                  ethnicityasian 
#>                   -1.612015e-14                    1.965411e-14 
#>               ethnicityhispanic               ethnicityamindian 
#>                    6.700549e-13                    1.565857e-15 
#>                  ethnicityother     treatmentTRUE:ethnicityafam 
#>                   -6.431281e-14                   -1.289929e+00 
#>    treatmentTRUE:ethnicityasian treatmentTRUE:ethnicityhispanic 
#>                   -2.494972e+01                    3.214437e+01 
#> treatmentTRUE:ethnicityamindian    treatmentTRUE:ethnicityother 
#>                    1.391799e+01                    3.202748e+00