tests/testthat/_snaps/ipw.md

In propensity: A Toolkit for Calculating and Working with Propensity Scores

ipw works for binary outcome with a confounder, using logistic ps, logistic outcome

Code
  res
Output
  Inverse Probability Weight Estimator
  Estimand: ATE

  Propensity Score Model:
    Call: glm(formula = z ~ x1 + x2, family = binomial(), data = dat)

  Outcome Model:
    Call: glm(formula = y ~ z, family = quasibinomial(), data = dat, weights = wts)

  Estimates:
          estimate  std.err        z ci.lower ci.upper conf.level   p.value    
  rd       0.19988 0.092425 2.162637   0.0187  0.38103       0.95 0.0305691 *  
  log(rr)  0.56041 0.156172 3.588443   0.2543  0.86651       0.95 0.0003327 ***
  log(or)  0.87831 0.173946 5.049330   0.5374  1.21924       0.95 4.434e-07 ***
  ---
  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ipw works for continuous outcome with a confounder, using logistic ps, linear outcome

Code
  res
Output
  Inverse Probability Weight Estimator
  Estimand: ATE

  Propensity Score Model:
    Call: glm(formula = z ~ x1 + x2, family = binomial(), data = dat)

  Outcome Model:
    Call: lm(formula = y ~ z, data = dat, weights = wts)

  Estimates:
       estimate  std.err        z ci.lower ci.upper conf.level   p.value    
  diff   2.2526  0.17524 12.85419   1.9091    2.596       0.95 < 2.2e-16 ***
  ---
  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ps_mod must be glm, outcome_mod must be glm or lm

Code
  expr
Condition <propensity_class_error>
  Error in `ipw()`:
  ! `ps_mod` must be of class "glm".
  x It has class "lm".

Code
  expr
Condition <propensity_class_error>
  Error:
  ! `"a"` must be of class "character" and length 2.
  x It has length 1.

Code
  expr
Condition <propensity_class_error>
  Error:
  ! `"a"` must be one of class "numeric" and "character" and length 2.
  x It has length 1.

Code
  expr
Condition <propensity_class_error>
  Error in `ipw()`:
  ! `outcome_mod` must be one of class "glm" and "lm".
  x It has class "list".

Code
  expr
Condition <propensity_columns_exist_error>
  Error in `ipw()`:
  ! The data frame `.data` is missing the "z" and "y" columns.

ipw handles various errors correctly

Code
  expr
Condition <propensity_error>
  Error in `check_estimand()`:
  ! Can't determine the estimand from weights.
  i Please specify `estimand`.

Estimand mismatch triggers an error if outcome weights differ from user-specified

Code
  expr
Condition <propensity_error>
  Error in `ipw()`:
  ! Estimand in weights different from `estimand`: "ate" vs. "att"

ipw works for cloglog link in the propensity score model

Code
  expr
Condition <propensity_error>
  Error in `ipw()`:
  ! `exposure` and `outcome` must be the same length.
  x `exposure` is length 400
  x `outcome` is length 100

Code
  expr
Condition <propensity_columns_exist_error>
  Error in `ipw()`:
  ! "z" not found in `model.frame(outcome_mod)`.
  i The outcome model may have transformations in the formula.
  i Please specify `.data`

propensity documentation built on March 3, 2026, 1:06 a.m.