replace all 'sensemade' with 'sensemakr'

with emphasis on class

R/plots.R

@@ -2,7 +2,7 @@
 ##' @title Sensitivity plots
 ##' @description Several sensitivity plots.
 ##'
-##' @param x sensemade object
+##' @param x sensemakr object
 ##' @param showvars chooses which subsets of benchmarks to display
 ##' Valid options are: 'masked', 'all', or list('foo1','foo2')
 ##' @param type type of the plot
@@ -11,7 +11,7 @@
 ##'
 ##' @return a ggplot object with the plot.
 ##' @export
-plot.sensemade = function(x,
+plot.sensemakr = function(x,
                            showvars='masked',
                            type = c("contour", "worst-case"),
                            ...){
@@ -114,7 +114,7 @@ contourplot = function(x,
   # subset the benchmark data
   ######################################################
 
-  # 'working benchmarks' assigned earlier in generic plot.sensemade()
+  # 'working benchmarks' assigned earlier in generic plot.sensemakr()
   # based on showvars subset
   # x$benchmarks$benchmarks_2plot1
   # x$benchmarks$benchmarks_2plot2
@@ -271,7 +271,7 @@ worstcaseplot = function(x,
                           main = "Sensitivity of estimate to unobserved confounder(s)\n\"Worst-case\" scenarios of partial R2 with outcome"){
 
 
-  # 'working benchmarks' assigned earlier in generic plot.sensemade()
+  # 'working benchmarks' assigned earlier in generic plot.sensemakr()
   # based on showvars subset
   # x$benchmarks$benchmarks_2plot1
   # x$benchmarks$benchmarks_2plot2

R/print_and_summary.R

@@ -7,7 +7,7 @@
 # A method must have arguments in exactly the same order as the generic.
 
 ##' @export
-print.sensemade = function(x, str = TRUE){
+print.sensemakr = function(x, str = TRUE){
 
   cat("Sensitivity Analysis\n\n")
   cat("Model:", trimws(deparse(formula(x$info$model))), "\n\n")
@@ -20,36 +20,36 @@ print.sensemade = function(x, str = TRUE){
 
 
 ##' @export
-summary.sensemade = function(object, q=1,scenarios=c(1,.25)){
+summary.sensemakr = function(object, q=1,scenarios=c(1,.25)){
   # bunch of useful things
   # return list with several useful things
-  # returns a obj of class summary.sensemade
+  # returns a obj of class summary.sensemakr
   out = object
-  class(out) = "summary.sensemade"
-  print.summary.sensemade(out, q=q, scenarios=scenarios)
+  class(out) = "summary.sensemakr"
+  print.summary.sensemakr(out, q=q, scenarios=scenarios)
 }
 
 ##' @export
-interpret = function(sensemade, q = 1){
+interpret = function(sensemakr, q = 1){
 
-  # from: sensemade$benchmarks$benchmark_eachvar$r2y
-  # to: sensemade$benchmarks$benchmark_eachvar$r2y
+  # from: sensemakr$benchmarks$benchmark_eachvar$r2y
+  # to: sensemakr$benchmarks$benchmark_eachvar$r2y
 
   # make sure 'max()' is choosing right benchmark point
   # currently points at 'female' not 'village',
   # since 'village' is in (sense$benchmarks$benchmark_group)
-  # currently, only looking at (sensemade$benchmarks$benchmark_eachvar)
+  # currently, only looking at (sensemakr$benchmarks$benchmark_eachvar)
 
-  idxr2y = which.max(sensemade$benchmarks$benchmark_eachvar$r2y)
-  idxr2d = which.max(sensemade$benchmarks$benchmark_eachvar$r2d)
-  estimate = sensemade$treat.stats$estimate
-  se = sensemade$treat.stats$se
+  idxr2y = which.max(sensemakr$benchmarks$benchmark_eachvar$r2y)
+  idxr2d = which.max(sensemakr$benchmarks$benchmark_eachvar$r2d)
+  estimate = sensemakr$treat.stats$estimate
+  se = sensemakr$treat.stats$se
   t = estimate/se
-  df = sensemade$treat.stats$df
-  varR2D = sensemade$benchmarks$benchmark_eachvar$covariate[idxr2d]
-  maxR2d = sensemade$benchmarks$benchmark_eachvar$r2d[idxr2d]
-  varR2Y = sensemade$benchmarks$benchmark_eachvar$covariate[idxr2y]
-  maxR2y = sensemade$benchmarks$benchmark_eachvar$r2y[idxr2d]
+  df = sensemakr$treat.stats$df
+  varR2D = sensemakr$benchmarks$benchmark_eachvar$covariate[idxr2d]
+  maxR2d = sensemakr$benchmarks$benchmark_eachvar$r2d[idxr2d]
+  varR2Y = sensemakr$benchmarks$benchmark_eachvar$covariate[idxr2y]
+  maxR2y = sensemakr$benchmarks$benchmark_eachvar$r2y[idxr2d]
   r2dc   = t^2/(t^2 + (maxR2y/q^2)*df)
   r2yc   = ((q*t)^2)*((1 - maxR2d)/(maxR2d*df))
 
@@ -82,11 +82,11 @@ interpret = function(sensemade, q = 1){
 }
 
 ##' @export
-worstcaseinterpret = function(sensemade, scenarios = c(1, 0.25), q = 1){
-  estimate = sensemade$treat.stats$estimate
-  se = sensemade$treat.stats$se
+worstcaseinterpret = function(sensemakr, scenarios = c(1, 0.25), q = 1){
+  estimate = sensemakr$treat.stats$estimate
+  se = sensemakr$treat.stats$se
   t = estimate/se
-  df = sensemade$treat.stats$df
+  df = sensemakr$treat.stats$df
   r2dc   = t^2/(t^2 + (scenarios/q^2)*df)
 
   cat("Considering the extreme scenarios of unobserved confounders explaining ",
@@ -96,7 +96,7 @@ worstcaseinterpret = function(sensemade, scenarios = c(1, 0.25), q = 1){
 }
 
 ##' @export
-print.summary.sensemade = function(x, q = 1, scenarios = c(1,.25)){
+print.summary.sensemakr = function(x, q = 1, scenarios = c(1,.25)){
  # pretty print for the summary
   cat("Sensitivity Analysis\n\n")
   cat("Model:", trimws(deparse(formula(x$info$model))), "\n\n")

R/sensemakr.R

@@ -82,7 +82,7 @@ sensemakr.lm = function(model, treatment, group_list=NULL){
   # stats = get stats()
   # benchmarks = get benchmarks()
   # compute bias and include ob data.frames
-  # returns pretty list with class "sensemade"
+  # returns pretty list with class "sensemakr"
 
   treat.stats = getstats(model, D)
   benchmarks  = benchmarkr(model, D, # X,
@@ -97,7 +97,7 @@ sensemakr.lm = function(model, treatment, group_list=NULL){
                           treatment = D,
                           # maybe return 'treatment model'
                           model = model))
-  class(out) = "sensemade"
+  class(out) = "sensemakr"
   return(out)
 }