CRS_scripts/Chapter_2_plots.R
In W-Mohammed/calibrater: Calibration of Decision-Analytic Models
##################################################
# Confirmation Review case studies #
##################################################
# Model: CRS_markov_2
# Parameters: c(p_Mets, p_DieMets)
# Targets: c(Survival, PropSick)
## Load the calibR package:
devtools::load_all() # 1 parent 6b344f8 commit d93ff4733193a2bcbdc276dab553fb0c2ba3de24
## Chapter 2 plots:----
## Saving path:----
path = "../../2. Confirmation Review/CR_data/"
chapter_dir <- "Chap_2/"
image_dir <- "images/"
data_dir <- "data/"
image_saving_path <- glue::glue("{path}{chapter_dir}{image_dir}")
data_saving_path <- glue::glue("{path}{chapter_dir}{data_dir}")
### 1. Parameter space:----
seed_no <- 1
set.seed(seed = seed_no)
#### 1.1. Full parameter space:----
parameters_list <- calibR::CR_CRS_data_2t$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
param_space <- calibR::sample_prior_FGS_(
.n_samples = 100,
.l_params = parameters_list)
param_space_plot <- plotly::plot_ly(
data = param_space,
x = param_space[["p_Mets"]],
y = param_space[["p_DieMets"]],
type = 'scatter',
mode = 'markers',
marker = list(size = 0.001)) %>%
plotly::layout(
xaxis = list(
title = parameters_list$v_params_labels[["p_Mets"]],
range = list(
parameters_list$Xargs[["p_Mets"]]$min,
parameters_list$Xargs[["p_Mets"]]$max),
showline = TRUE,
linewidth = 1,
linecolor = "grey",
mirror = TRUE),
yaxis = list(
title = parameters_list$v_params_labels[["p_DieMets"]],
range = list(
parameters_list$Xargs[["p_DieMets"]]$min,
parameters_list$Xargs[["p_DieMets"]]$max),
showline = TRUE,
linewidth = 1,
linecolor = "grey",
mirror = TRUE))
##### Save plot:----
image_name = "parameter_space.jpeg"
reticulate::py_run_string("import sys")
plotly::save_image(
p = param_space_plot,
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
#### 1.2. Explored parameter space:----
parameters_list <- calibR::CR_CRS_data_2t_lp$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
param_space <- calibR::sample_prior_FGS_(
.n_samples = 100,
.l_params = parameters_list)
param_space_plot <- plotly::plot_ly(
data = param_space,
x = param_space[["p_Mets"]],
y = param_space[["p_DieMets"]],
type = 'scatter',
mode = 'markers',
marker = list(size = 0.001)) %>%
plotly::layout(
xaxis = list(
title = parameters_list$v_params_labels[["p_Mets"]],
range = list(
parameters_list$Xargs[["p_Mets"]]$min,
parameters_list$Xargs[["p_Mets"]]$max),
showline = TRUE,
linewidth = 1,
linecolor = "grey",
mirror = TRUE),
yaxis = list(
title = parameters_list$v_params_labels[["p_DieMets"]],
range = list(
parameters_list$Xargs[["p_DieMets"]]$min,
parameters_list$Xargs[["p_DieMets"]]$max),
showline = TRUE,
linewidth = 1,
linecolor = "grey",
mirror = TRUE))
##### Save plot:----
image_name = "parameter_space_lp.jpeg"
reticulate::py_run_string("import sys")
plotly::save_image(
p = param_space_plot,
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
### 2. Fitness plots:----
#### 2.1. Full parameter space:----
seed_no <- 1
set.seed(seed = seed_no)
parameters_list <- calibR::CR_CRS_data_2t$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
targets_list <- calibR::CR_CRS_data_2t$l_targets
gof_measure <- c("SSE", "LLK")
##### Initiate CalibR R6 object:----
CR_CRS_2P2T <- calibR_R6$new(
.model = CRS_markov_2,
.params = parameters_list,
.targets = targets_list,
.args = NULL,
.transform = FALSE)
##### GOF without True values:----
###### Fill plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = TRUE,
.greys_ = FALSE,
.coloring_ = "heatmap",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
####### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
###### Gray plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = FALSE,
.greys_ = TRUE,
.coloring_ = "heatmap",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
####### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_bw.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
###### Line plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = FALSE,
.greys_ = FALSE,
.coloring_ = "none",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
###### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_ln.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
#### 2.2. Explored parameter space:----
seed_no <- 1
set.seed(seed = seed_no)
parameters_list <- calibR::CR_CRS_data_2t_lp$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
targets_list <- calibR::CR_CRS_data_2t_lp$l_targets
gof_measure <- c("SSE", "LLK")
##### Initiate CalibR R6 object:----
CR_CRS_2P2T <- calibR_R6$new(
.model = CRS_markov_2,
.params = parameters_list,
.targets = targets_list,
.args = NULL,
.transform = FALSE)
##### GOF function without True values:----
###### Fill plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = TRUE,
.greys_ = FALSE,
.coloring_ = "heatmap",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
####### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_lp.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
###### Gray plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = FALSE,
.greys_ = TRUE,
.coloring_ = "heatmap",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
####### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_lp_bw.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
###### Line plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = FALSE,
.greys_ = FALSE,
.coloring_ = "none",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
###### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_lp_ln.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
### 3. Undirected methods (RGS, FGS and LHS) with fitness plot:----
seed_no <- 1
set.seed(seed = seed_no)
parameters_list <- calibR::CR_CRS_data_2t_lp$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
targets_list <- calibR::CR_CRS_data_2t_lp$l_targets
gof_measure <- c("SSE", "LLK")
sample_method <- c("RGS", "FGS", "LHS")
#### Initiate CalibR R6 object:----
CR_CRS_2P2T <- calibR_R6$new(
.model = CRS_markov_2,
.params = parameters_list,
.targets = targets_list,
.args = NULL,
.transform = FALSE)
#### Generate samples using FGS, RGS and LHS:----
CR_CRS_2P2T$
sampleR(
.n_samples = 1e2,
.sampling_method = sample_method)
##### Unguided searching methods:----
CR_CRS_2P2T$
calibrateR_random(
.optim = FALSE,
.weighted = FALSE,
.sample_method = sample_method,
.calibration_method = gof_measure)
###### Fitness function without True values:----
random_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = FALSE,
.zoom_ = FALSE,
.legend_ = FALSE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
random
####### Save plots:----
######## Walk through and save files:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = random_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(random_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = random_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
###### Fitness function with True values:----
random_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = TRUE,
.zoom_ = FALSE,
.legend_ = FALSE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
random
####### Save plots:----
######## Walk through and save files:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = random_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(random_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}_w_true.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = random_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
### 4. Directed methods (BFGS, NM, and SANN) with fitness plot:----
seed_no <- 1
set.seed(seed = seed_no)
parameters_list <- calibR::CR_CRS_data_2t_lp$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
targets_list <- calibR::CR_CRS_data_2t_lp$l_targets
gof_measure <- c("SSE", "LLK")
sample_method <- "RGS"
directed_method <- c("NM", "BFGS", "SANN")
#### Initiate CalibR R6 object:----
CR_CRS_2P2T <- calibR_R6$new(
.model = CRS_markov_2,
.params = parameters_list,
.targets = targets_list,
.args = NULL,
.transform = FALSE)
#### Generate samples using Random Grid Search:----
CR_CRS_2P2T$
sampleR(
.n_samples = 10,
.sampling_method = sample_method)
#### Directed calibration:----
CR_CRS_2P2T$
calibrateR_directed(
.gof = gof_measure,
.n_samples = 10,
.maximise = FALSE,
.calibration_method = directed_method,
.sample_method = sample_method,
.max_iterations = 100,
temp = 1,
trace = FALSE)
##### Fitness function with True values:----
####### Save plots:----
######## Walk through and save files:----
######### Save original view:----
directed_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = TRUE,
.zoom_ = FALSE,
.legend_ = FALSE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
directed
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = directed_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(directed_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = directed_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
######### Save zoomed view:----
directed_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = TRUE,
.zoom_ = TRUE,
.legend_ = FALSE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
directed
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = directed_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(directed_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}_zm_true.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = directed_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
##### Fitness function zoomed without True values:----
####### Save plots:----
######## Walk through and save files:----
######### Save zoomed view:----
directed_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = FALSE,
.legend_ = FALSE,
.zoom_ = TRUE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
directed
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = directed_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(directed_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}_zm.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = directed_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
W-Mohammed/calibrater documentation built on Oct. 14, 2023, 1:57 a.m.
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##################################################
# Confirmation Review case studies #
##################################################
# Model: CRS_markov_2
# Parameters: c(p_Mets, p_DieMets)
# Targets: c(Survival, PropSick)
## Load the calibR package:
devtools::load_all() # 1 parent 6b344f8 commit d93ff4733193a2bcbdc276dab553fb0c2ba3de24
## Chapter 2 plots:----
## Saving path:----
path = "../../2. Confirmation Review/CR_data/"
chapter_dir <- "Chap_2/"
image_dir <- "images/"
data_dir <- "data/"
image_saving_path <- glue::glue("{path}{chapter_dir}{image_dir}")
data_saving_path <- glue::glue("{path}{chapter_dir}{data_dir}")
### 1. Parameter space:----
seed_no <- 1
set.seed(seed = seed_no)
#### 1.1. Full parameter space:----
parameters_list <- calibR::CR_CRS_data_2t$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
param_space <- calibR::sample_prior_FGS_(
.n_samples = 100,
.l_params = parameters_list)
param_space_plot <- plotly::plot_ly(
data = param_space,
x = param_space[["p_Mets"]],
y = param_space[["p_DieMets"]],
type = 'scatter',
mode = 'markers',
marker = list(size = 0.001)) %>%
plotly::layout(
xaxis = list(
title = parameters_list$v_params_labels[["p_Mets"]],
range = list(
parameters_list$Xargs[["p_Mets"]]$min,
parameters_list$Xargs[["p_Mets"]]$max),
showline = TRUE,
linewidth = 1,
linecolor = "grey",
mirror = TRUE),
yaxis = list(
title = parameters_list$v_params_labels[["p_DieMets"]],
range = list(
parameters_list$Xargs[["p_DieMets"]]$min,
parameters_list$Xargs[["p_DieMets"]]$max),
showline = TRUE,
linewidth = 1,
linecolor = "grey",
mirror = TRUE))
##### Save plot:----
image_name = "parameter_space.jpeg"
reticulate::py_run_string("import sys")
plotly::save_image(
p = param_space_plot,
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
#### 1.2. Explored parameter space:----
parameters_list <- calibR::CR_CRS_data_2t_lp$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
param_space <- calibR::sample_prior_FGS_(
.n_samples = 100,
.l_params = parameters_list)
param_space_plot <- plotly::plot_ly(
data = param_space,
x = param_space[["p_Mets"]],
y = param_space[["p_DieMets"]],
type = 'scatter',
mode = 'markers',
marker = list(size = 0.001)) %>%
plotly::layout(
xaxis = list(
title = parameters_list$v_params_labels[["p_Mets"]],
range = list(
parameters_list$Xargs[["p_Mets"]]$min,
parameters_list$Xargs[["p_Mets"]]$max),
showline = TRUE,
linewidth = 1,
linecolor = "grey",
mirror = TRUE),
yaxis = list(
title = parameters_list$v_params_labels[["p_DieMets"]],
range = list(
parameters_list$Xargs[["p_DieMets"]]$min,
parameters_list$Xargs[["p_DieMets"]]$max),
showline = TRUE,
linewidth = 1,
linecolor = "grey",
mirror = TRUE))
##### Save plot:----
image_name = "parameter_space_lp.jpeg"
reticulate::py_run_string("import sys")
plotly::save_image(
p = param_space_plot,
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
### 2. Fitness plots:----
#### 2.1. Full parameter space:----
seed_no <- 1
set.seed(seed = seed_no)
parameters_list <- calibR::CR_CRS_data_2t$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
targets_list <- calibR::CR_CRS_data_2t$l_targets
gof_measure <- c("SSE", "LLK")
##### Initiate CalibR R6 object:----
CR_CRS_2P2T <- calibR_R6$new(
.model = CRS_markov_2,
.params = parameters_list,
.targets = targets_list,
.args = NULL,
.transform = FALSE)
##### GOF without True values:----
###### Fill plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = TRUE,
.greys_ = FALSE,
.coloring_ = "heatmap",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
####### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
###### Gray plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = FALSE,
.greys_ = TRUE,
.coloring_ = "heatmap",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
####### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_bw.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
###### Line plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = FALSE,
.greys_ = FALSE,
.coloring_ = "none",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
###### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_ln.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
#### 2.2. Explored parameter space:----
seed_no <- 1
set.seed(seed = seed_no)
parameters_list <- calibR::CR_CRS_data_2t_lp$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
targets_list <- calibR::CR_CRS_data_2t_lp$l_targets
gof_measure <- c("SSE", "LLK")
##### Initiate CalibR R6 object:----
CR_CRS_2P2T <- calibR_R6$new(
.model = CRS_markov_2,
.params = parameters_list,
.targets = targets_list,
.args = NULL,
.transform = FALSE)
##### GOF function without True values:----
###### Fill plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = TRUE,
.greys_ = FALSE,
.coloring_ = "heatmap",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
####### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_lp.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
###### Gray plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = FALSE,
.greys_ = TRUE,
.coloring_ = "heatmap",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
####### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_lp_bw.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
###### Line plots:----
GOF_plots_list <- CR_CRS_2P2T$draw_GOF_measure(
.legend_ = FALSE,
.greys_ = FALSE,
.coloring_ = "none",
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
blank
###### Save plot:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
image_name = glue::glue("GOF_{.gof_measure_}_measure_lp_ln.jpeg")
plotly::save_image(
p = GOF_plots_list[[.gof_measure_]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
### 3. Undirected methods (RGS, FGS and LHS) with fitness plot:----
seed_no <- 1
set.seed(seed = seed_no)
parameters_list <- calibR::CR_CRS_data_2t_lp$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
targets_list <- calibR::CR_CRS_data_2t_lp$l_targets
gof_measure <- c("SSE", "LLK")
sample_method <- c("RGS", "FGS", "LHS")
#### Initiate CalibR R6 object:----
CR_CRS_2P2T <- calibR_R6$new(
.model = CRS_markov_2,
.params = parameters_list,
.targets = targets_list,
.args = NULL,
.transform = FALSE)
#### Generate samples using FGS, RGS and LHS:----
CR_CRS_2P2T$
sampleR(
.n_samples = 1e2,
.sampling_method = sample_method)
##### Unguided searching methods:----
CR_CRS_2P2T$
calibrateR_random(
.optim = FALSE,
.weighted = FALSE,
.sample_method = sample_method,
.calibration_method = gof_measure)
###### Fitness function without True values:----
random_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = FALSE,
.zoom_ = FALSE,
.legend_ = FALSE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
random
####### Save plots:----
######## Walk through and save files:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = random_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(random_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = random_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
###### Fitness function with True values:----
random_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = TRUE,
.zoom_ = FALSE,
.legend_ = FALSE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
random
####### Save plots:----
######## Walk through and save files:----
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = random_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(random_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}_w_true.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = random_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
### 4. Directed methods (BFGS, NM, and SANN) with fitness plot:----
seed_no <- 1
set.seed(seed = seed_no)
parameters_list <- calibR::CR_CRS_data_2t_lp$l_params
parameters_list$v_params_labels <- c(
'p_Mets' = "Parameter 1",
'p_DieMets' = "Parameter 2")
targets_list <- calibR::CR_CRS_data_2t_lp$l_targets
gof_measure <- c("SSE", "LLK")
sample_method <- "RGS"
directed_method <- c("NM", "BFGS", "SANN")
#### Initiate CalibR R6 object:----
CR_CRS_2P2T <- calibR_R6$new(
.model = CRS_markov_2,
.params = parameters_list,
.targets = targets_list,
.args = NULL,
.transform = FALSE)
#### Generate samples using Random Grid Search:----
CR_CRS_2P2T$
sampleR(
.n_samples = 10,
.sampling_method = sample_method)
#### Directed calibration:----
CR_CRS_2P2T$
calibrateR_directed(
.gof = gof_measure,
.n_samples = 10,
.maximise = FALSE,
.calibration_method = directed_method,
.sample_method = sample_method,
.max_iterations = 100,
temp = 1,
trace = FALSE)
##### Fitness function with True values:----
####### Save plots:----
######## Walk through and save files:----
######### Save original view:----
directed_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = TRUE,
.zoom_ = FALSE,
.legend_ = FALSE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
directed
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = directed_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(directed_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = directed_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
######### Save zoomed view:----
directed_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = TRUE,
.zoom_ = TRUE,
.legend_ = FALSE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
directed
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = directed_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(directed_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}_zm_true.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = directed_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
##### Fitness function zoomed without True values:----
####### Save plots:----
######## Walk through and save files:----
######### Save zoomed view:----
directed_GOF_measure <- CR_CRS_2P2T$draw_GOF_measure(
.true_points_ = FALSE,
.legend_ = FALSE,
.zoom_ = TRUE,
.coloring_ = "none",
.greys_ = FALSE,
.scale_ = NULL,
.gof_ = gof_measure)$
plots$
GOF_plots$
directed
purrr::walk(
.x = gof_measure,
.f = function(.gof_measure_) {
purrr::walk(
.x = directed_GOF_measure[[.gof_measure_]] %>%
names(.) %>%
`names<-`(names(directed_GOF_measure[[.gof_measure_]])),
.f = function(.gof_name) {
image_name = glue::glue("{.gof_name}_zm.jpeg")
reticulate::py_run_string("import sys")
plotly::save_image(
p = directed_GOF_measure[[.gof_measure_]][[.gof_name]][[1]][[1]],
file = glue::glue("{image_saving_path}{image_name}"),
scale = 5)
})
})
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