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Clustered variance-covariance matrices and T statistic d.o.f.

Description

Variance-covariance matrices with robust clustered standard errors anddegrees-of-freedom for T statistics, for tests and examples specifyingvcov(d.o.f. defined as g - 1, where g is the number of clusters). Generatedwithmargex data in this package.

Usage

cvcov

Format

A list of three lists, from an OLS model, logit model, and OLS with a polynomialinteraction with missing data, each containing

clust

3-by-3 variance-covariance matrix

dof

integer, degrees of freedom for the T statistic

Details

See data-raw/make_cluster_vcov.R for details.

Source

http://cameron.econ.ucdavis.edu/research/Cameron_Miller_JHR_2015_February.pdf

Estimating predictive margins on a model

Description

This function estimates the predictive effects and levels for variables withina model using the delta method.

Usage

marg(  mod,  var_interest,  data = NULL,  weights = NULL,  vcov_mat = NULL,  dof = NULL,  type = "levels",  base_rn = 1,  at_var_interest = NULL,  at = NULL,  cofint = 0.95,  ...)

Arguments

Details

The variable for the predictive margin is specified byvar_interest. Ifmargins are only needed at particular values ofvar_interest,at_var_interest should be used. If margins ofvar_interest areneeded at across the levels of adifferent variable in the model,at should be used.

If higher-order polynomial terms (e.g.y ~ x + x^2) are addedusing the R functionpoly, theraw = TRUEargument should be used to include the basic polynomial termsinstead of orthogonal polynomial terms. If orthogonal polynomials are used,marg will fail when the user specifiesat for a small setof values for the variable in question (e.g.at = list(x = 10)),sincepoly needs more data to calculate orthogonal polynomials(e.g.poly(10, 2) fails, butpoly(c(10, 8, 3), 2) will run).

P values are calculated with T tests for gaussian families, and Z testsotherwise. If a new variance-covariance matrix is provided (e.g. forclustering standard errors), the degrees of freedom for the T test / p-valuecalculation may need to be specified usingdof.

This function currently only supportsglm andivreg objects. If you would like to uselmobjects, consider running aglm with familygaussian.

When calculating predicted levels and effects for models built using weights,marg returns weighted averages for levels and effects by default.Users can remove this option by settingweights = NULL.

Value

list of dataframes with predicted margins/effects, standard errors, p-values,and confidence interval bounds

Predicted Margins for 'glm' objects

Description

Obtains predicted margins and standard errorsof those predictions from a fitted generalized linear model object.

Usage

## S3 method for class 'glm'marg(  mod,  var_interest,  data = mod$data[names(mod$prior.weights), ],  weights = mod$prior.weights,  ...)

Arguments

Examples

data(mtcars)mod <- glm(vs ~ as.factor(gear) + mpg, data = mtcars, family = 'binomial')# Get the level of the outcome variable at different values of `gear`marg(mod, var_interest = 'gear', type = 'levels')# Get the effect of `gear` on the outcome value, holding values of `mpg`# constantmarg(mod, var_interest = 'gear', type = 'effects',     at = list(mpg = c(15, 21)))data(margex)mod <- glm(outcome ~ as.factor(treatment) + distance,       data = margex, family = 'binomial')# Get the level of the outcome variable at different values of `treatment`marg(mod, var_interest = 'treatment', type = 'levels', at = NULL)# Get the effect of `treatment` on the outcome variablemarg(mod, var_interest = 'treatment', type = 'effects', at = NULL)# Get the level of the outcome variable at different values of `distance`marg(mod, var_interest = 'distance', type = 'levels',          at = NULL, at_var_interest = c(10, 20, 30))# Using a custom variance-covariance matrix for clustered standard errors# (also requires custom degrees of freedom for T statistic with OLS model),# clustering on the "arm" variabledata(margex)data(cvcov)# ?cvcovv <- cvcov$ols$clustd <- cvcov$ols$stata_dofmod <- glm(outcome ~ treatment + distance,           data = margex, family = 'binomial')marg(mod, var_interest = 'treatment', type = 'levels',          vcov_mat = v, dof = d)# Using weightsdata(margex)mm <- glm(y ~ as.factor(treatment) + age, data = margex, family = 'gaussian',          weights = distance)z1 <- marg(mod = mm, var_interest = 'treatment', type = 'levels')[[1]]z2 <- marg(mod = mm, var_interest = 'treatment', type = 'effects')[[1]]

Predicted Margins for 'ivreg' objects from theAER package

Description

Obtains predicted margins and standard errorsof those predictions from a fittedivreg model object.

Usage

## S3 method for class 'ivreg'marg(mod, var_interest, data, weights = NULL, ...)

Arguments

Examples

# From ?AER::ivreg# datadata("CigarettesSW", package = "AER")CigarettesSW$rprice <- with(CigarettesSW, price/cpi)CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)# modelfm <- AER::ivreg(log(packs) ~ log(rprice) + log(rincome) |                   log(rincome) + tdiff + I(tax/cpi),                 data = CigarettesSW, subset = year == "1995")# Get margins for different levels of price/cpirprice_levs <- round(quantile(CigarettesSW$rprice))marg(fm, data = subset(CigarettesSW, year == "1995"),     var_interest = 'rprice', at_var_interest = rprice_levs)

Artificial data for margins

Description

A fictitious dataset outcome, treatment, and demographic variables for 3000observations.

Usage

margex

Format

A data frame with 3000 rows and 11 variables:

y

numeric

outcome

integer, 0 or 1

sex

character: "female" or "male"

group

integer

age

integer

distance

numeric

ycn

numeric

yc

numeric, 0 or 1

treatment

integer

agegroup

character: "20-29", "30-39", or "40+"

arm

integer

Source

https://www.stata-press.com/data/r14/margex.dta

Main wrapper function to calculate margins and standard errors

Description

For one set of transformed covariates (not including the variable ofinterest), calculate the predicted level and standard error for thevariable of interest.

Usage

pred_se(  df_levels,  model,  type,  base_rn,  vcov_mat,  weights,  deriv_func,  link_func)

Arguments