use seq_len instead of 1:x

statgenlmu · May 9, 2016 · b9da5db · b9da5db
1 parent 1ec85d4
commit b9da5db
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Showing 7 changed files with 12 additions and 9 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: jaatha
-Version: 3.1.1.9007
+Version: 3.1.1.9008
 License: GPL (>= 3)
 Title: Simulation-Based Maximum Likelihood Parameter Estimation
 Authors@R: c(

diff --git a/R/block.R b/R/block.R
@@ -31,7 +31,7 @@ block_class <- R6::R6Class("Block",
       m
     },
     get_corners = function() {
-      corners <- expand.grid(lapply(1:nrow(private$border), function(i) {
+      corners <- expand.grid(lapply(seq_len(nrow(private$border)), function(i) {
         private$border[i, , drop = FALSE] #nolint
       }), KEEP.OUT.ATTRS = FALSE) #nolint
       colnames(corners) <- rownames(private$border)

diff --git a/R/fit_glm.R b/R/fit_glm.R
@@ -18,7 +18,7 @@ fit_glm.jaatha_stat_basic <- function(x, sim_data, ...) {
   X <- cbind(1, 
              do.call(rbind, lapply(sim_data, function(data) data$pars_normal)))
 
-  glms <- lapply(1:ncol(Y), function(i) {
+  glms <- lapply(seq_len(ncol(Y)), function(i) {
     suppressWarnings(
       stats::glm.fit(X, Y[, i], family = stats::poisson("log"), 
                      control = list(maxit = 100))[c("coefficients", 

diff --git a/R/initialization.R b/R/initialization.R
@@ -102,7 +102,7 @@ do_zoom_in_search <- function(model, data, reps, sim, cores, sim_cache,
                               block_width, n_steps = 3) {
   "Starts with estimating parameters in the complete parameter space, an then 
    iteratively deceases the size of the block"
-  best_est <- vapply(1:reps, function(i) {
+  best_est <- vapply(seq_len(reps), function(i) {
     middle <- rep(.5, model$get_par_number())
     block_widths <- utils::head(seq(1, block_width, 
                                     length.out = n_steps + 1), -1)

diff --git a/R/jaatha_log.R b/R/jaatha_log.R
@@ -17,7 +17,7 @@ jaatha_log_class <- R6::R6Class("jaatha_log",
     initialize = function(model, data, reps, max_steps, verbose = TRUE) {
       par_number <- model$get_par_number()
       par_names <- model$get_par_ranges()$get_par_names()
-      private$estimates <- lapply(1:reps, function(i) {
+      private$estimates <- lapply(seq_len(reps), function(i) {
         estimates <- matrix(NA, max_steps, par_number + 3)
         colnames(estimates) <- c("rep", "step", "llh", par_names)
         as.data.frame(estimates)

diff --git a/R/jaatha_model.R b/R/jaatha_model.R
@@ -36,15 +36,18 @@ jaatha_model_class <- R6::R6Class("jaatha_model",
       "conducts a simulation for each parameter combination in pars"
       assert_that(is.matrix(pars))
       assert_that(ncol(pars) == private$par_ranges$get_par_number())
+      assert_that(nrow(pars) >= 1)
       assert_that(all(0 - 1e-5 <= pars & pars <= 1 + 1e-5))
       assert_that(is_jaatha_data(data))
       assert_that(is.count(cores))
 
       # Generate a seed for each simulation
-      seeds <- sample_seed(length(pars) + 1)
+      n_pars <- nrow(pars)
+      seeds <- sample_seed(n_pars + 1)
 
       # Simulate
-      sim_data <- mclapply(1:nrow(pars), function(i) {
+      sim_data <- mclapply(seq_len(n_pars), function(i) {
+        assert_that(is.count(i))
         set.seed(seeds[i])
         sim_pars <- private$par_ranges$denormalize(pars[i, ])
 
@@ -80,7 +83,7 @@ jaatha_model_class <- R6::R6Class("jaatha_model",
         stop("Simulations failed, check your model.")
       }
 
-      set.seed(seeds[length(seeds)])
+      set.seed(tail(seeds, 1))
       sim_data
     },
     get_par_ranges = function() private$par_ranges,

diff --git a/R/stat_cube.R b/R/stat_cube.R
@@ -58,7 +58,7 @@ stat_cube_class <- R6::R6Class("stat_cube", inherit = stat_basic_class,
       data_matrix <- private$calculate_matrix(data)
       assert_that(is.numeric(data_matrix))
       if (!is.matrix(data_matrix)) data_matrix <- matrix(data_matrix, ncol = 1)
-      list(break_values = lapply(1:ncol(data_matrix), function(i) {
+      list(break_values = lapply(seq_len(ncol(data_matrix)), function(i) {
         private$calc_break_values(data_matrix[, i], private$break_values)
       }))
     }