Merge 8e9f41d into 47714b1

R/multi_arm_designer.R

@@ -25,8 +25,8 @@
 #'
 # A design with fixed sds and means. N is the sole modifiable argument.
 #' design <- multi_arm_designer(N = 80, m_arms = 4, means = 1:4,
-#'                              fixed = list(m_arms = 4, sds = rep(1, 4),
-#'                                           means = 1:4))
+#'                              fixed = c("m_arms" , "sds" ,
+#'                                           "means" ))
 #'
 
 multi_arm_designer <- function(N = 30,
@@ -47,9 +47,9 @@ multi_arm_designer <- function(N = 30,
       length(conditions) != m_arms)
     stop("means, sds and conditions arguments must be of length m_arms.")
   if (any(sds <= 0)) stop("sds should be positive.")
-  if (!"sds" %in% names(fixed)) sds_ <-  sapply(1:m_arms, function(i) expr(sds[!!i]))
-  if (!"means" %in% names(fixed)) means_ <-  sapply(1:m_arms, function(i) expr(means[!!i]))
-  if (!"N" %in% names(fixed)) N_ <- expr(N)
+  if (!"sds" %in% fixed) sds_ <-  sapply(1:m_arms, function(i) expr(sds[!!i]))
+  if (!"means" %in% fixed) means_ <-  sapply(1:m_arms, function(i) expr(means[!!i]))
+  if (!"N" %in% fixed) N_ <- expr(N)
 
   # Create helper vars to be used in design
   errors <- sapply(1:m_arms, function(x) quos(rnorm(!!N_, 0, !!!sds_[x])))
@@ -126,23 +126,23 @@ multi_arm_designer <- function(N = 30,
 
 
   {{{
-    # Model
+    # M: Model
     population <- eval_bare(population_expr)
 
     potential_outcomes <- eval_bare(potential_outcomes_expr)
 
-    # Inquiry
+    # I: Inquiry
     estimand  <- eval_bare(estimand_expr)
 
-    # Design
+    # D: Data Strategy
     assignment <- eval_bare(assignment_expr)
 
     reveal <-  declare_reveal(assignment_variables = Z)
 
-    # Answer
+    # A: Answer Strategy
     estimator <- eval_bare(estimator_expr)
 
-
+    # Design
     multi_arm_design <-
       population + potential_outcomes + assignment + reveal + estimand +  estimator
 

vignettes/multi_arm.Rmd

@@ -40,7 +40,7 @@ diagnosis <- diagnose_design(multi_arm_design)
 ```
 
 ```{r,eval = TRUE, echo = FALSE}
-kable(reshape_diagnosis(diagnosis)[,-c(1,2,3,5)], digits = 2)
+kable(reshape_diagnosis(diagnosis)[,-c(1,12)], digits = 2)
 ```
 
 The diagnosis of our design indicates that the regression provides unbiased estimates of  the average treatment effect (ATE) in each arm. These estimates, however, are not so precise;  The estimated standard deviation is large yielding wide confidence intervals that contain the true value of the ATEs more than 95\% of the time.