# estimatr_glancers: Glance at an estimatr object In graemeblair/DDestimate: Fast Estimators for Design-Based Inference

## Description

Glance at an estimatr object

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## S3 method for class 'lm_robust' glance(x, ...) ## S3 method for class 'lh_robust' glance(x, ...) ## S3 method for class 'iv_robust' glance(x, ...) ## S3 method for class 'difference_in_means' glance(x, ...) ## S3 method for class 'horvitz_thompson' glance(x, ...) ```

## Arguments

 `x` An object returned by one of the estimators `...` extra arguments (not used)

## Value

For `glance.lm_robust`, a data.frame with columns:

 `r.squared` the R^2, R^2 = 1 - Sum(e[i]^2) / Sum((y[i] - y^*)^2), where y^* is the mean of y[i] if there is an intercept and zero otherwise, and e[i] is the ith residual. `adj.r.squared` the R^2 but penalized for having more parameters, `rank` `se_type` the standard error type specified by the user `statistic` the value of the F-statistic `p.value` p-value from the F test `df.residual` residual degrees of freedom `N` the number of observations used

For `glance.lh_robust`, we glance the `lm_robust` component only. You can access the linear hypotheses as a data.frame directy from the `lh` component of the `lh_robust` object

For `glance.iv_robust`, a data.frame with columns:

 `r.squared` The R^2 of the second stage regression `adj.r.squared` The R^2 but penalized for having more parameters, `rank` `df.residual` residual degrees of freedom `N` the number of observations used `se_type` the standard error type specified by the user `statistic` the value of the F-statistic `p.value` p-value from the F test `statistic.weakinst` the value of the first stage F-statistic, useful for the weak instruments test; only reported if there is only one endogenous variable `p.value.weakinst` p-value from the first-stage F test, a test of weak instruments; only reported if there is only one endogenous variable `statistic.endogeneity` the value of the F-statistic for the test of endogeneity; often called the Wu-Hausman statistic, with robust standard errors, we employ the regression based test `p.value.endogeneity` p-value from the F-test for endogeneity `statistic.overid` the value of the chi-squared statistic for the test of instrument correlation with the error term; only reported with overidentification `p.value.overid` p-value from the chi-squared test; only reported with overidentification

For `glance.difference_in_means`, a data.frame with columns:

 `design` the design used, and therefore the estimator used `df` the degrees of freedom `N` the number of observations used `N_blocks` the number of blocks, if used `N_clusters` the number of clusters, if used `condition2` the second, "treatment", condition `condition1` the first, "control", condition

For `glance.horvitz_thompson`, a data.frame with columns:

 `N` the number of observations used `se_type` the type of standard error estimator used `condition2` the second, "treatment", condition `condition1` the first, "control", condition

`generics::glance()`, `estimatr::lm_robust()`, `estimatr::lm_lin()`, `estimatr::iv_robust()`, `estimatr::difference_in_means()`, `estimatr::horvitz_thompson()`