Merge pull request #186 from DeclareDesign/ls/coefs

estimate -> coefficients in model object

DESCRIPTION

@@ -28,6 +28,8 @@ Suggests:
     RcppEigen,
     fabricatr (>= 0.3),
     randomizr (>= 0.9),
+    margins,
+    prediction,
     AER
 Enhances:
     broom,

NAMESPACE

@@ -1,10 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(coef,difference_in_means)
-S3method(coef,horvitz_thompson)
-S3method(coef,iv_robust)
-S3method(coef,lm_lin)
-S3method(coef,lm_robust)
 S3method(confint,difference_in_means)
 S3method(confint,horvitz_thompson)
 S3method(confint,iv_robust)

R/S3_print.R

@@ -30,7 +30,7 @@ print_summary_lm_like <- function(x,
     cat("\nCoefficients:\n")
   }
 
-  print(x$coefficients, digits = digits)
+  print(coef(x), digits = digits)
 
   if (!is.null(x$fstatistic)) {
     cat(

R/S3_summary.R

@@ -2,16 +2,16 @@
 summary.lm_robust <- function(object,
                               ...) {
 
-  if (is.matrix(object$estimate)) {
-    ny <- ncol(object$estimate)
+  if (is.matrix(coef(object))) {
+    ny <- ncol(coef(object))
 
     ret <- setNames(
       vector("list", ny),
       paste("Response", object$outcome)
     )
 
     mat_objs <- c(
-      "estimate",
+      "coefficients",
       "std.error",
       "df",
       "ci.lower",

R/S3_tidy.R

@@ -86,7 +86,7 @@ tidy_data_frame <- function(object, digits = NULL) {
 
   vec_cols <-
     c(
-      "estimate",
+      "coefficients",
       "std.error",
       "p.value",
       "ci.lower",
@@ -95,6 +95,7 @@ tidy_data_frame <- function(object, digits = NULL) {
     )
 
   tidy_mat <- do.call("cbind", lapply(vec_cols, function(x) {as.vector(object[[x]])}))
+  vec_cols[which(vec_cols == "coefficients")] <- "estimate"
   colnames(tidy_mat) <- vec_cols
   return_frame <- data.frame(
     term = object[["term"]],

R/estimatr_difference_in_means.R

@@ -64,10 +64,10 @@
 #'
 #' An object of class \code{"difference_in_means"} is a list containing at
 #' least the following components:
-#'   \item{estimate}{the estimated difference in means}
+#'   \item{coefficients}{the estimated difference in means}
 #'   \item{std.error}{the estimated standard error}
 #'   \item{df}{the estimated degrees of freedom}
-#'   \item{p.value}{the p-value from a two-sided t-test using \code{estimate}, \code{std.error}, and \code{df}}
+#'   \item{p.value}{the p-value from a two-sided t-test using \code{coefficients}, \code{std.error}, and \code{df}}
 #'   \item{ci.lower}{the lower bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{ci.upper}{the upper bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{term}{a character vector of coefficient names}
@@ -320,7 +320,7 @@ difference_in_means <-
       N_overall <- with(block_estimates, sum(N))
 
       # Blocked design, (Gerber Green 2012, p73, eq3.10)
-      diff <- with(block_estimates, sum(estimate * N / N_overall))
+      diff <- with(block_estimates, sum(coefficients * N / N_overall))
 
       df <- NA
       n_blocks <- nrow(block_estimates)
@@ -332,7 +332,7 @@ difference_in_means <-
           std.error <-
             with(
               block_estimates,
-              sqrt((1 / (n_blocks * (n_blocks - 1))) * sum((estimate - diff) ^ 2))
+              sqrt((1 / (n_blocks * (n_blocks - 1))) * sum((coefficients - diff) ^ 2))
             )
         } else {
           design <- "Matched-pair clustered"
@@ -342,7 +342,7 @@ difference_in_means <-
               block_estimates,
               sqrt(
                 (n_blocks / ((n_blocks - 1) * N_overall ^ 2)) *
-                  sum((N * estimate - (N_overall * diff) / n_blocks) ^ 2)
+                  sum((N * coefficients - (N_overall * diff) / n_blocks) ^ 2)
               )
             )
         }
@@ -367,7 +367,7 @@ difference_in_means <-
       }
 
       return_frame <- data.frame(
-        estimate = diff,
+        coefficients = diff,
         std.error = std.error,
         df = df,
         N = N_overall,
@@ -465,7 +465,7 @@ difference_in_means_internal <-
         has_int = TRUE
       )
 
-      diff <- cr2_out$estimate[2]
+      diff <- coef(cr2_out)[2]
       std.error <- cr2_out$std.error[2]
       df <- cr2_out$df[2]
     } else {
@@ -513,15 +513,15 @@ difference_in_means_internal <-
           has_int = TRUE
         )
 
-        diff <- w_hc2_out$estimate[2]
+        diff <- coef(w_hc2_out)[2]
         std.error <- w_hc2_out$std.error[2]
         df <- w_hc2_out$df[2]
       }
     }
 
     return_frame <-
       data.frame(
-        estimate = diff,
+        coefficients = diff,
         std.error = std.error,
         df = df,
         stringsAsFactors = FALSE

R/estimatr_horvitz_thompson.R

@@ -121,10 +121,10 @@
 #' An object of class \code{"horvitz_thompson"} is a list containing at
 #' least the following components:
 #'
-#'   \item{estimate}{the estimated difference in totals}
+#'   \item{coefficients}{the estimated difference in totals}
 #'   \item{std.error}{the estimated standard error}
 #'   \item{df}{the estimated degrees of freedom}
-#'   \item{p.value}{the p-value from a two-sided z-test using \code{estimate} and \code{std.error}}
+#'   \item{p.value}{the p-value from a two-sided z-test using \code{coefficients} and \code{std.error}}
 #'   \item{ci.lower}{the lower bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{ci.upper}{the upper bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{term}{a character vector of coefficient names}
@@ -545,12 +545,12 @@ horvitz_thompson <-
 
       n_blocks <- nrow(block_estimates)
 
-      diff <- with(block_estimates, sum(estimate * N / N_overall))
+      diff <- with(block_estimates, sum(coefficients * N / N_overall))
 
       std.error <- with(block_estimates, sqrt(sum(std.error ^ 2 * (N / N_overall) ^ 2)))
 
       return_frame <- data.frame(
-        estimate = diff,
+        coefficients = diff,
         std.error = std.error,
         N = N_overall
       )
@@ -749,7 +749,7 @@ horvitz_thompson_internal <-
 
     return_frame <-
       data.frame(
-        estimate = diff,
+        coefficients = diff,
         std.error = std.error,
         N = N,
         stringsAsFactors = FALSE

R/estimatr_iv_robust.R

@@ -61,10 +61,10 @@
 #'
 #' An object of class \code{"iv_robust"} is a list containing at least the
 #' following components:
-#'   \item{estimate}{the estimated coefficients}
+#'   \item{coefficients}{the estimated coefficients}
 #'   \item{std.error}{the estimated standard errors}
 #'   \item{df}{the estimated degrees of freedom}
-#'   \item{p.value}{the p-values from a two-sided t-test using \code{estimate}, \code{std.error}, and \code{df}}
+#'   \item{p.value}{the p-values from a two-sided t-test using \code{coefficients}, \code{std.error}, and \code{df}}
 #'   \item{ci.lower}{the lower bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{ci.upper}{the upper bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{term}{a character vector of coefficient names}

R/estimatr_lm_lin.R

@@ -51,17 +51,19 @@
 #' return results in a \code{data.frame}. To get useful data out of the return,
 #' you can use these data frames, you can use the resulting list directly, or
 #' you can use the generic accessor functions \code{coef}, \code{vcov},
-#' \code{confint}, and \code{predict}.
+#' \code{confint}, and \code{predict}. Marginal effects and uncertainty about
+#' them can be gotten by passing this object to
+#' \code{\link[margins]{margins}} from the \pkg{margins}.
 #'
 #' Users who want to print the results in TeX of HTML can use the
 #' \code{\link[texreg]{extract}} function and the \pkg{texreg} package.
 #'
 #' An object of class \code{"lm_robust"} is a list containing at least the
 #' following components:
-#'   \item{estimate}{the estimated coefficients}
+#'   \item{coefficients}{the estimated coefficients}
 #'   \item{std.error}{the estimated standard errors}
 #'   \item{df}{the estimated degrees of freedom}
-#'   \item{p.value}{the p-values from a two-sided t-test using \code{estimate}, \code{std.error}, and \code{df}}
+#'   \item{p.value}{the p-values from a two-sided t-test using \code{coefficients}, \code{std.error}, and \code{df}}
 #'   \item{ci.lower}{the lower bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{ci.upper}{the upper bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{term}{a character vector of coefficient names}

R/estimatr_lm_robust.R

@@ -60,7 +60,9 @@
 #' return results in a \code{data.frame}. To get useful data out of the return,
 #' you can use these data frames, you can use the resulting list directly, or
 #' you can use the generic accessor functions \code{coef}, \code{vcov},
-#' \code{confint}, and \code{predict}.
+#' \code{confint}, and \code{predict}. Marginal effects and uncertainty about
+#' them can be gotten by passing this object to
+#' \code{\link[margins]{margins}} from the \pkg{margins}.
 #'
 #' Users who want to print the results in TeX of HTML can use the
 #' \code{\link[texreg]{extract}} function and the \pkg{texreg} package.
@@ -71,10 +73,10 @@
 #'
 #' An object of class \code{"lm_robust"} is a list containing at least the
 #' following components:
-#'   \item{estimate}{the estimated coefficients}
+#'   \item{coefficients}{the estimated coefficients}
 #'   \item{std.error}{the estimated standard errors}
 #'   \item{df}{the estimated degrees of freedom}
-#'   \item{p.value}{the p-values from a two-sided t-test using \code{estimate}, \code{std.error}, and \code{df}}
+#'   \item{p.value}{the p-values from a two-sided t-test using \code{coefficients}, \code{std.error}, and \code{df}}
 #'   \item{ci.lower}{the lower bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{ci.upper}{the upper bound of the \code{1 - alpha} percent confidence interval}
 #'   \item{term}{a character vector of coefficient names}
@@ -122,7 +124,7 @@
 #' # Can use summary() to get more statistics
 #' summary(lmro)
 #' # Can also get coefficients three ways
-#' lmro$estimate
+#' lmro$coefficients
 #' coef(lmro)
 #' tidy(lmro)$estimate
 #' # Can also get confidence intervals from object or with new 1 - `alpha`

R/helper_cis_pvals.R

@@ -17,23 +17,23 @@ add_cis_pvals <- function(return_frame, alpha, ci, ttest = TRUE) {
 
       return_frame$p.value <- with(
         return_frame,
-        2 * pt(abs(estimate / std.error), df = df, lower.tail = FALSE)
+        2 * pt(abs(coefficients / std.error), df = df, lower.tail = FALSE)
       )
 
       crit_se <- with(return_frame, qt(1 - alpha / 2, df = df) * std.error)
     } else {
       return_frame$p.value <- with(
         return_frame,
-        2 * pnorm(abs(estimate / std.error), lower.tail = FALSE)
+        2 * pnorm(abs(coefficients / std.error), lower.tail = FALSE)
       )
 
       crit_se <- with(return_frame, qnorm(1 - alpha / 2) * std.error)
 
       return_frame$df <- NA
     }
 
-    return_frame$ci.lower <- with(return_frame, estimate - crit_se)
-    return_frame$ci.upper <- with(return_frame, estimate + crit_se)
+    return_frame$ci.lower <- with(return_frame, coefficients - crit_se)
+    return_frame$ci.upper <- with(return_frame, coefficients + crit_se)
 
     return(as.list(return_frame))
   } else {

R/helper_extract.R

@@ -21,14 +21,14 @@ extract.lm_robust <- function(model, include.ci = TRUE, include.rsquared = TRUE,
   s <- summary(model, ...)
 
   names <- rownames(s$coefficients)
-  co <- s$coefficients[, 1]
-  se <- s$coefficients[, 2]
-  pval <- s$coefficients[, 3]
+  co <- coef(s)[, 1]
+  se <- coef(s)[, 2]
+  pval <- coef(s)[, 3]
   cilow <- numeric()
   ciupper <- numeric()
   if (include.ci) {
-    cilow <- s$coefficients[, 4]
-    ciupper <- s$coefficients[, 5]
+    cilow <- coef(s)[, 4]
+    ciupper <- coef(s)[, 5]
   }
 
   rs <- s$r.squared  #extract R-squared

R/helper_lm_robust_fit.R

@@ -148,13 +148,13 @@ lm_robust_fit <- function(y,
 
   if (multivariate) {
     return_list <- list(
-      estimate = fit$beta_hat,
+      coefficients = fit$beta_hat,
       std.error = matrix(NA, k, ny),
       df = matrix(NA, k, ny)
     )
   } else {
     return_list <- list(
-      estimate = setNames(as.vector(fit$beta_hat), variable_names),
+      coefficients = setNames(as.vector(fit$beta_hat), variable_names),
       std.error = NA,
       df = NA
     )

R/helper_return.R

@@ -43,7 +43,7 @@ dim_like_return <- function(return_list, alpha, formula, conditions) {
 
   return_list[["outcome"]] <- deparse(formula[[2]], nlines = 5)
 
-  names(return_list[["estimate"]]) <- return_list[["term"]]
+  names(return_list[["coefficients"]]) <- return_list[["term"]]
 
   return(return_list)
 }