feat: model performance

Author: aravindhebbali

NAMESPACE

@@ -2,6 +2,7 @@
 
 S3method(ols_coll_diag,default)
 S3method(ols_correlations,default)
+S3method(ols_model_performance,default)
 S3method(ols_pure_error_anova,default)
 S3method(ols_regress,default)
 S3method(ols_regress,lm)
@@ -53,6 +54,7 @@ S3method(plot,ols_step_forward_sbc)
 S3method(plot,ols_step_forward_sbic)
 S3method(print,ols_coll_diag)
 S3method(print,ols_correlations)
+S3method(print,ols_model_performance)
 S3method(print,ols_pure_error_anova)
 S3method(print,ols_regress)
 S3method(print,ols_step_all_possible)
@@ -111,6 +113,7 @@ export(ols_launch_app)
 export(ols_leverage)
 export(ols_mallows_cp)
 export(ols_model_info)
+export(ols_model_performance)
 export(ols_msep)
 export(ols_plot_added_variable)
 export(ols_plot_comp_plus_resid)

R/ols-model-performance.R

@@ -0,0 +1,66 @@
+#' Model Performance
+#'
+#' @description
+#' Evaluate performance of regression models.
+#'
+#' @param model An object of class \code{lm}.
+#' @param ... Other arguments.
+#'
+#' @return \code{ols_model_performance} returns an object of class
+#' \code{"ols_model_performance"}. An object of class \code{"ols_regress"} is a
+#' list containing the following components:
+#'
+#' \item{r}{square root of rsquare, correlation between observed and predicted values of dependent variable}
+#' \item{rsq}{coefficient of determination or r-square}
+#' \item{adjr}{adjusted rsquare}
+#' \item{prsq}{predicted rsquare}
+#' \item{aic}{akaike information criteria}
+#' \item{sbc}{bayesian information criteria}
+#' \item{sbic}{sawa bayesian information criteria}
+#' \item{rmse}{root mean squared error}
+#'
+#' @examples
+#' # model
+#' model <- lm(mpg ~ disp + hp + wt, data = mtcars)
+#'
+#' # model performance
+#' ols_model_performance(model)
+#'
+#' @export
+#'
+ols_model_performance <- function(model, ...) UseMethod("ols_model_performance")
+
+#' @export
+#'
+ols_model_performance.default <- function(model, ...) {
+  rsq  <- summary(model)$r.squared
+  r    <- sqrt(rsq)
+  adjr <- summary(model)$adj.r.squared
+  rmse <- sqrt(mean(model$residuals ^ 2))
+  aic  <- ols_aic(model)
+  sbc  <- ols_sbc(model)
+  sbic <- ols_sbic(model, model)
+  prsq <- ols_pred_rsq(model)
+
+  result <- list(
+    r    = r,
+    rsq  = rsq,
+    adjr = adjr,
+    prsq = prsq,
+    aic  = aic,
+    sbc  = sbc,
+    sbic = sbic,
+    rmse = rmse
+  )
+
+  class(result) <- "ols_model_performance"
+
+  return(result)
+
+}
+
+#' @export
+#'
+print.ols_model_performance <- function(x, ...) {
+  print_model_perf(x)
+}

R/output.R

@@ -784,4 +784,42 @@ print_step_rsquared <- function(data) {
     print_step_output(data, "both")
   }
 
-}
+}
+
+print_model_perf <- function(data) {
+  a <- c("R", "R-Squared", "Adj. R-Squared", "Pred R-Squared")
+  b <- c(
+    format(round(data$r, 3), nsmall = 3),
+    format(round(data$rsq, 3), nsmall = 3),
+    format(round(data$adjr, 3), nsmall = 3),
+    format(round(data$prsq, 3), nsmall = 3)
+  )
+  d <- c("AIC", "SBC", "SBIC", "RMSE")
+  e <- c(
+    format(round(data$aic, 3), nsmall = 3),
+    format(round(data$sbc, 3), nsmall = 3),
+    format(round(data$sbic, 3), nsmall = 3),
+    format(round(data$rmse, 3), nsmall = 3)
+  )
+
+  w1 <- max(nchar(a))
+  w2 <- max(nchar(format(b, nsmall = 3)))
+  w3 <- max(nchar(d))
+  w4 <- max(nchar(format(e, nsmall = 3)))
+  w5 <- sum(w1, w2, w3, w4, 15)
+  nw <- length(b)
+
+  # model summary
+  cat(fc("Model Performance", w5), "\n")
+  cat(rep("-", w5), sep = "", "\n")
+  for (i in seq_len(nw)) {
+    cat(fl(a[i], w1), fs(), fg(b[i], w2), fs(), fs(), fl(d[i], w3),
+      fs(), fg(e[i], w4), "\n")
+  }
+  cat(rep("-", w5), sep = "", "\n")
+  cat("  AIC: Akaike Information Criteria", "\n",
+    " SBC: Schwarz Bayesian Criteria", "\n",
+    "SBIC: Sawa's Bayesian Criteria", "\n",
+    "RMSE: Root Mean Square Error", "\n\n")
+
+}

_pkgdown.yml

@@ -64,6 +64,11 @@ reference:
     contents:
       - ols_regress
 
+  - title: Model Performance
+
+    contents:
+      - ols_model_performance
+
   - title: All Possible Regression
 
     contents:
@@ -172,6 +177,30 @@ reference:
       - ols_pure_error_anova
       - ols_plot_diagnostics
 
+  - title: Model Object Info
+
+    contents:
+      - ols_model_info
+      - ols_get_formula
+      - ols_get_interaction_terms
+      - ols_get_variables
+      - ols_get_data
+      - ols_get_df
+      - ols_get_intercept
+      - ols_get_model_matrix
+      - ols_get_predicted
+      - ols_get_residuals
+      - ols_get_sigma
+      - ols_get_vcov
+      - ols_get_deviance
+      - ols_get_parameters
+      - ols_get_predictors
+      - ols_get_response
+      - ols_get_call
+      - ols_count_obs
+      - ols_count_parameters
+      - ols_has_intercept
+
   - title: Plot Data
 
     contents: