re-name function

fix minor things

Author: boennecd

.sandbox/benchmarking.R

@@ -1,6 +1,6 @@
 library(dynamichazard); library(testthat); library(survival)
 
-test_sim_func_logit <- asNamespace("dynamichazard")$test_sim_func_logit
+test_sim_func_logit <- dynamichazard:::test_sim_func_logit
 
 set.seed(599479)
 

.sandbox/profile.R

@@ -1,6 +1,6 @@
 library(dynamichazard);
 library(survival);
-test_sim_func_logit <- asNamespace("dynamichazard")$test_sim_func_logit
+test_sim_func_logit <- dynamichazard:::test_sim_func_logit
 
 set.seed(9997)
 sims <- test_sim_func_logit(n_series = 1e5, n_vars = 10, t_0 = 0, t_max = 10,

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: dynamichazard
 Type: Package
 Title: Dynamic Hazard Models using State Space Models
-Version: 0.6.1
+Version: 0.6.2
 Authors@R: c(person("Benjamin", "Christoffersen", 
                     email = "boennecd@gmail.com", 
                     role = c("cre", "aut")),

R/sims.R

@@ -134,9 +134,21 @@ test_sim_func_logit <- function(
   re_draw = T, beta_start = 3, intercept_start,
   sds = rep(1, n_vars + !missing(intercept_start)),
   is_fixed = c(), lambda = 1,
-  tstart_sampl_func = function(t_0 = t_0, t_max = t_max)
-    t_0,
-  betas, linkfunc = "logit"){
+  tstart_sampl_func = function(t_0 = t_0, t_max = t_max) t_0, betas){
+  cl <- match.call()
+  cl[["linkfunc"]] <- "logit"
+  cl[[1L]] <- bquote(
+    environment(.(cl[[1L]]))$test_sim_func_discrete)
+  eval(cl, parent.frame())
+}
+
+test_sim_func_discrete <- function(
+  n_series, n_vars = 10L, t_0 = 0L, t_max = 10L, x_range = .1, x_mean = -.1,
+  re_draw = T, beta_start = 3, intercept_start,
+  sds = rep(1, n_vars + !missing(intercept_start)),
+  is_fixed = c(), lambda = 1,
+  tstart_sampl_func = function(t_0 = t_0, t_max = t_max) t_0, betas,
+  linkfunc){
   # make output matrix
   n_row_max <- n_row_inc <- 10^5
   res <- matrix(NA_real_, nrow = n_row_inc, ncol = 4 + n_vars,

inst/shiny/app.R

@@ -3,7 +3,6 @@ if(!require(shiny))
   stop("Requires 'shiny' to run the demo")
 
 test_sim_func_exp <- dynamichazard:::test_sim_func_exp
-test_sim_func_logit <- dynamichazard:::test_sim_func_logit
 exp_model_names <- dynamichazard:::exp_model_names
 
 # Global params
@@ -359,7 +358,7 @@ server <- function(input, output) {
 
   sim_input <- reactive({
     f_choice <- if(input$sim_with == "exponential")
-      quote(test_sim_func_exp) else quote(test_sim_func_logit)
+      quote(test_sim_func_exp) else quote(test_sim_func_discrete)
 
     n_fixed <- if(input$sim_fix_options == 1)
       0 else if(input$sim_fix_options == 2)
@@ -384,6 +383,8 @@ server <- function(input, output) {
         tstart_sampl_func = .(start_fun)))
     if(input$sim_with == "cloglog")
       sim_quote[["linkfunc"]] <- "cloglog"
+    else if(input$sim_with == "logit")
+      sim_quote[["linkfunc"]] <- "logit"
 
     sim_exp <- bquote({
       set.seed(.(input$seed))

speedtest/speedtest.Rmd

@@ -24,7 +24,7 @@ matplot(fit$state_vecs, type = "l", lty = 2, add = TRUE)
 
 ```{r func_setup}
 sim_func <- function(n, p){
-  func <- asNamespace("dynamichazard")$test_sim_func_logit
+  func <- dynamichazard:::test_sim_func_logit
   set.seed(101)
   t_max <- 30L
   func(n_series = n, n_vars = p, t_max = t_max, x_range = 1, x_mean = 0,

tests/testthat/helper.R

@@ -198,7 +198,7 @@ bigqr.init <- asNamespace("biglm")$bigqr.init
 # Simulated data sets to test against
 set.seed(6790753)
 
-test_sim_func_logit <- asNamespace("dynamichazard")$test_sim_func_logit
+test_sim_func_logit <- dynamichazard:::test_sim_func_logit
 get_sim <- function(n)
   test_sim_func_logit(n_series = n, n_vars = 10, t_0 = 0, t_max = 10,
                       x_range = 1, x_mean = 0, re_draw = T, beta_start = rnorm(10),

tests/testthat/test_cloglog.R

@@ -1,7 +1,7 @@
 context("testing cloglog link function")
 
 set.seed(57826639)
-dat <- test_sim_func_logit(
+dat <- test_sim_func_discrete(
   n_series = 400, n_vars = 2, t_0 = 0, t_max = 30,
   x_range = 1, x_mean = 0, re_draw = T, beta_start = rnorm(2),
   is_fixed = 2,

vignettes/Comparing_methods_for_logistic_models.Rmd

@@ -12,7 +12,7 @@ csl: bib_style.csl
 vignette: >
   %\VignetteIndexEntry{Comparing methods for time varying logistic models}
   %\VignetteEngine{knitr::rmarkdown}
-  \usepackage[utf8]{inputenc}
+  %\VignetteEncoding{UTF-8}
 ---
 
 ```{r setup, include=FALSE}
@@ -48,7 +48,7 @@ knitr::opts_knit$set(warning = F, message = F)
 \newcommand{\mat}[1]{\mathbf{#1}}
 
 # Introduction
-In this vignette, we compare the dynamic logistic model in `dynamichazard` with others methods within the package and methods from the `timereg` and `mgcv` packages. Further this note will serve as an illustration of how to use the `ddhazard` function for the logistic model. We will use the `pbc2` dataset from the `survival` package. The motivation is that the `pbc2` data set is commonly used in survival analysis for illustrations. It is suggested to first read  or skim `vignette("ddhazard", "dynamichazard")` to get an introduction to the models and estimation methods in this package.
+In this vignette, we compare the dynamic logistic model in `dynamichazard` with other methods within the package and methods from the `timereg` and `mgcv` packages. Further this note will serve as an illustration of how to use the `ddhazard` function for the logistic model. We will use the `pbc2` dataset from the `survival` package. The motivation is that the `pbc2` data set is commonly used in survival analysis for illustrations. It is suggested to first read  or skim `vignette("ddhazard", "dynamichazard")` to get an introduction to the models and estimation methods in this package.
 
 The note is structured as follows: First, we cover the `pbc2` data set. Then we estimate two static (non-dynamic) logistic regression models using `glm`. This is followed by a fit using a Generalized Additive model with the `gam` function in the `mgcv` package. Next, we will estimate a cox-model with time varying parameters using the `timecox` function in the `timereg` package. Finally, we will end by illustrating the methods in this package for time varying parameters in a logistic regression.