diff --git a/Projects/SE_AGRI/parameters_SE Design-Agri.R b/Projects/SE_AGRI/parameters_SE Design-Agri.R
index 1d51a9f9850ce8568ef72b2ecff24315eafec007..52e7994e13c44d9a3e41447fe465e2fb08b51378 100644
--- a/Projects/SE_AGRI/parameters_SE Design-Agri.R
+++ b/Projects/SE_AGRI/parameters_SE Design-Agri.R
@@ -14,6 +14,8 @@ bforeign = 0.3
bdamage = 0.6
bprice = 0.2
+
+
manipulations = list(alt1.professional= expr(alt1.initiator==1),
alt2.professional= expr(alt2.initiator==1),
alt1.expert = expr(alt1.initiator==2),
diff --git a/Projects/ValuGaps/code/GIS_data.R b/Projects/ValuGaps/code/GIS_data.R
index c278b5f6c480f2a2fd7226cd07d60f8d23dda634..ac73130f5737924aef3b7487d8f17c14e3d7e45a 100644
--- a/Projects/ValuGaps/code/GIS_data.R
+++ b/Projects/ValuGaps/code/GIS_data.R
@@ -27,7 +27,7 @@ process_data <- function(data, name_prefix, cover_data) {
#### Draw respondents ####
# Load population density data for Germany
-density_raster <- rast("data/gis/BBSR_Landnutzung_Bevoelkerung/ewz250_2011_v1_multi.tif")
+density_raster <- rast("Projects/ValuGaps/data/gis/BBSR_Landnutzung_Bevoelkerung/ewz250_2011_v1_multi.tif")
##### Do plot of the pop density ####
diff --git a/Projects/ValuGaps/parameters_valugaps.R b/Projects/ValuGaps/parameters_valugaps.R
index b156f442508dcbd9cb6ac89114d2ccb2bd3d3c9f..9df3ee3664291ad849930406521d4de95eaf14cd 100644
--- a/Projects/ValuGaps/parameters_valugaps.R
+++ b/Projects/ValuGaps/parameters_valugaps.R
@@ -96,7 +96,7 @@ protected_areas <- mask(protected_areas, vect(germany_geo_c))
#### Define simulation parameters ####
resps = 96 # number of respondents
-nosim=2 # number of simulations to run (about 500 is minimum)
+nosim=3 # number of simulations to run (about 500 is minimum)
#buffer_dist <- 15000
buffer_dist_ls <- list(10000, 15000, 25000, 50000) # define radius for the respondents in meter
@@ -110,6 +110,7 @@ bc= 1.4
bc2 = -0.0175
bp=-0.03
+mani1 = expr(left_join(final_set, by="ID"))
manipulations = list(
#expr(across(c("HNV_on_corine", "Protected_on_corine"), ~replace_na(.,0)) ),
@@ -135,3 +136,11 @@ u<-list(
# v1 =V.1~ basc + bb*alt1.b+ bc * alt1.c + bp * alt1.p ,
# v2 =V.2~ basc + bb*alt2.b+ bc * alt2.c + bp * alt2.p ,
# v3 =V.3~ 0)
+
+
+## this function will be called before the data is simulated
+sou_gis <- function() {
+
+ source("Projects/ValuGaps/code/GIS_data.R")
+
+}
diff --git a/functions.R b/functions.R
index 1ff2fc90c95987df907852ae38f6173060b1bba1..1feaa7ed8f1733c3845f56346befe5feaf2e6d4c 100644
--- a/functions.R
+++ b/functions.R
@@ -1,165 +1,192 @@
-sim_choice <- function(designfile, no_sim=10, respondents=330, mnl_U,utils=u ) {
-
-
- require("tictoc")
- require("readr")
- require("psych")
- require("dplyr")
- require("evd")
- require("tidyr")
- require("kableExtra")
- require("gridExtra")
- require("stringr")
- require("mixl")
- require("furrr")
- require("purrr")
- require("ggplot2")
- require("formula.tools")
-
-
- mnl_U <-paste(map_chr(u,as.character,keep.source.attr = TRUE),collapse = "",";") %>%
- str_replace_all( c( "priors\\[\"" = "" , "\"\\]" = "" , "~" = "=", "\\." = "_" , " b" = " @b" , "V."="U_", " alt"="$alt"))
-
-
-
-
-
- by_formula <- function(equation){ #used to take formulas as inputs in simulation utility function
- # //! cur_data_all may get deprecated in favor of pick
- # pick(everything()) %>%
- cur_data_all() %>%
- transmute(!!lhs(equation) := !!rhs(equation) )
- }
-
-
-
-
-
- simulate_choices <- function(data=database) { #the part in dataset that needs to be repeated in each run
-
-
- # browser()
-
- n=seq_along(1:length(utils))
-
- print("DANGER")
-
- data <- data %>%
- group_by(ID) %>%
- mutate(
- across(.cols=n,.fns = ~ rgumbel(setpp,loc=0, scale=1), .names = "{'e'}_{n}" ),
- across(starts_with("V_"), .names = "{'U'}_{n}") + across(starts_with("e_")) ) %>% ungroup() %>%
- mutate(CHOICE=max.col(.[,grep("U_",names(.))])
- ) %>%
- as.data.frame()
-
-
-
-
- return(data)
-
- }
-
- estimate_sim <- function(run=1) { #start loop
-
- cat(run)
-
- database <- simulate_choices()
-
-
-
-
- model<-mixl::estimate(model_spec,start_values = est, availabilities = availabilities, data= database,)
-
- return(model)
-
- }
-
- designs_all <- list()
-
- design <- read_delim(designfile,delim = "\t",
- escape_double = FALSE,
- trim_ws = TRUE ,
- col_select = c(-Design, -starts_with("...")) ,
- name_repair = "universal") %>%
- filter(!is.na(Choice.situation))
-
-
-
- nsets<-nrow(design)
- nblocks<-max(design$Block)
- setpp <- nsets/nblocks # Choice Sets per respondent; in this 'no blocks' design everyone sees all 24 sets
- #respondents <- replications*nblocks
- replications <- respondents/nblocks
- #browser()
-
- if(!exists("manipulations")) manipulations=list()
-
- database<- design %>%
- arrange(Block,Choice.situation) %>%
- slice(rep(row_number(), replications)) %>% ## replicate design according to number of replications
- mutate(RID = rep(1:respondents, each=setpp)) %>% # create Respondent ID.
- relocate(RID,`Choice.situation`) %>%
- mutate( !!! manipulations ,
- map_dfc(utils,by_formula) #our functions to create utility variables. They need to be entered as a formula list as an argument
-
- ) %>%
- rename(ID ="RID") %>%
- rename_with(~ stringr::str_replace(.,pattern = "\\.","_"), everything()) %>%
- as.data.frame()
-
-
-
-
-
- database <- simulate_choices()
-
- model_spec <- mixl::specify_model(mnl_U, database, disable_multicore=F)
-
- est=setNames(rep(0,length(model_spec$beta_names)), model_spec$beta_names)
-
-
- availabilities <- mixl::generate_default_availabilities(
- database, model_spec$num_utility_functions)
-
- plan(multisession, workers = 8)
-
- output<- 1:no_sim %>% map(estimate_sim)
-
- coefs<-map(1:length(output),~summary(output[[.]])[["coefTable"]][c(1,8)] %>%
- tibble::rownames_to_column() %>%
- pivot_wider(names_from = rowname, values_from = c(est, rob_pval0)) ) %>%
- bind_rows(.id = "run")
-
- output[["summary"]] <-describe(coefs[,-1], fast = TRUE)
-
- output[["coefs"]] <-coefs
-
- pvals <- output[["coefs"]] %>% select(starts_with("rob_pval0"))
-
- output[["power"]] <- 100*table(apply(pvals,1, function(x) all(x<0.05)))/nrow(pvals)
-
-
- output[["metainfo"]] <- c(Path = designfile, NoSim = no_sim, NoResp =respondents)
-
-
- print(kable(output[["summary"]],digits = 2, format = "rst"))
-
-
- print(output[["power"]])
-
-
- return(output)
-
-
-}
-
-
-plot_multi_histogram <- function(df, feature, label_column) { #function to create nice multi histograms, taken somewhere from the web
- plt <- ggplot(df, aes(x=eval(parse(text=feature)), fill=eval(parse(text=label_column)))) +
- #geom_histogram(alpha=0.7, position="identity", aes(y = ..density..), color="black") +
- geom_density(alpha=0.5) +
- geom_vline(aes(xintercept=mean(eval(parse(text=feature)))), color="black", linetype="dashed", linewidth=1) + ## this makes a vertical line of the mean
- labs(x=feature, y = "Density")
- plt + guides(fill=guide_legend(title=label_column))
+sim_choice <- function(designfile, no_sim=10, respondents=330, mnl_U,utils=u ) {
+
+
+ require("tictoc")
+ require("readr")
+ require("psych")
+ require("dplyr")
+ require("evd")
+ require("tidyr")
+ require("kableExtra")
+ require("gridExtra")
+ require("stringr")
+ require("mixl")
+ require("furrr")
+ require("purrr")
+ require("ggplot2")
+ require("formula.tools")
+
+
+ mnl_U <-paste(map_chr(u,as.character,keep.source.attr = TRUE),collapse = "",";") %>%
+ str_replace_all( c( "priors\\[\"" = "" , "\"\\]" = "" , "~" = "=", "\\." = "_" , " b" = " @b" , "V_"="U_", " alt"="$alt"))
+
+ cat("mixl \n")
+ print(mnl_U)
+
+ cat("\n Simulation \n")
+
+ print(u)
+
+
+
+ by_formula <- function(equation){ #used to take formulas as inputs in simulation utility function
+ # //! cur_data_all may get deprecated in favor of pick
+ # pick(everything()) %>%
+ cur_data_all() %>%
+ transmute(!!lhs(equation) := !!rhs(equation) )
+ }
+
+
+
+
+
+ simulate_choices <- function(data=datadet) { #the part in dataset that needs to be repeated in each run
+cat("does sou_gis exist")
+ print( exists("sou_gis"))
+ # browser()
+
+ if (exists("sou_gis") && is.function(sou_gis)) {
+ sou_gis()
+
+ cat("source of gis has been done")
+ }
+
+ # source("Projects/ValuGaps/code/GIS_data.R")
+
+ if(!exists("manipulations")) manipulations=list() ## If no user input on further data manipulations
+
+ n=seq_along(1:length(utils)) # number of utility functions
+
+
+
+ print("DANGER")
+
+ print(exists("final_set"))
+
+ if(exists("final_set")) data = left_join(data,final_set, by = "ID")
+
+ data <- data %>%
+ # rename(ID="RID") %>%
+ group_by(ID) %>%
+
+ #rename(ID ="RID") %>%
+ #rename_with(~ stringr::str_replace(.,pattern = "\\.","_"), everything()) %>%
+ mutate(!!! manipulations ,
+ map_dfc(utils,by_formula)) %>% #our functions to create utility variables. They need to be entered as a formula list as an argument
+ rename_with(~ stringr::str_replace(.,pattern = "\\.","_"), everything()) %>%
+
+ mutate(across(.cols=n,.fns = ~ rgumbel(setpp,loc=0, scale=1), .names = "{'e'}_{n}" ),
+ across(starts_with("V_"), .names = "{'U'}_{n}") + across(starts_with("e_")) ) %>% ungroup() %>%
+ mutate(CHOICE=max.col(.[,grep("U_",names(.))])
+ ) %>%
+ as.data.frame()
+
+
+print("data has been made")
+
+cat(head(data$CHOICE))
+
+ return(data)
+
+ }
+
+ estimate_sim <- function(run=1) { #start loop
+
+ cat("This is Run number ", run)
+
+ database <- simulate_choices(datadet)
+
+
+ cat("This is the utility functions \n" , mnl_U)
+
+ model<-mixl::estimate(model_spec,start_values = est, availabilities = availabilities, data= database,)
+
+ return(model)
+
+ }
+
+ designs_all <- list()
+
+ design <- read_delim(designfile,delim = "\t",
+ escape_double = FALSE,
+ trim_ws = TRUE ,
+ col_select = c(-Design, -starts_with("...")) ,
+ name_repair = "universal") %>%
+ filter(!is.na(Choice.situation))
+
+
+
+ nsets<-nrow(design)
+ nblocks<-max(design$Block)
+ setpp <- nsets/nblocks # Choice Sets per respondent; in this 'no blocks' design everyone sees all 24 sets
+ #respondents <- replications*nblocks
+ replications <- respondents/nblocks
+ #browser()
+
+
+
+ datadet<- design %>%
+ arrange(Block,Choice.situation) %>%
+ slice(rep(row_number(), replications)) %>% ## replicate design according to number of replications
+ mutate(ID = rep(1:respondents, each=setpp)) %>% # create Respondent ID.
+ relocate(ID,`Choice.situation`) %>%
+as.data.frame()
+
+
+
+
+
+ database <- simulate_choices()
+
+ model_spec <- mixl::specify_model(mnl_U, database, disable_multicore=F)
+
+ est=setNames(rep(0,length(model_spec$beta_names)), model_spec$beta_names)
+
+
+ availabilities <- mixl::generate_default_availabilities(
+ database, model_spec$num_utility_functions)
+
+ plan(multisession, workers = 8)
+
+ output<- 1:no_sim %>% map(estimate_sim)
+
+
+
+
+ coefs<-map(1:length(output),~summary(output[[.]])[["coefTable"]][c(1,8)] %>%
+ tibble::rownames_to_column() %>%
+ pivot_wider(names_from = rowname, values_from = c(est, rob_pval0)) ) %>%
+ bind_rows(.id = "run")
+
+ output[["summary"]] <-describe(coefs[,-1], fast = TRUE)
+
+ output[["coefs"]] <-coefs
+
+ pvals <- output[["coefs"]] %>% dplyr::select(starts_with("rob_pval0"))
+
+ output[["power"]] <- 100*table(apply(pvals,1, function(x) all(x<0.05)))/nrow(pvals)
+
+
+ output[["metainfo"]] <- c(Path = designfile, NoSim = no_sim, NoResp =respondents)
+
+
+ print(kable(output[["summary"]],digits = 2, format = "rst"))
+
+
+ print(output[["power"]])
+
+
+ return(output)
+
+
+}
+
+
+plot_multi_histogram <- function(df, feature, label_column) { #function to create nice multi histograms, taken somewhere from the web
+ plt <- ggplot(df, aes(x=eval(parse(text=feature)), fill=eval(parse(text=label_column)))) +
+ #geom_histogram(alpha=0.7, position="identity", aes(y = ..density..), color="black") +
+ geom_density(alpha=0.5) +
+ geom_vline(aes(xintercept=mean(eval(parse(text=feature)))), color="black", linetype="dashed", linewidth=1) + ## this makes a vertical line of the mean
+ labs(x=feature, y = "Density")
+ plt + guides(fill=guide_legend(title=label_column))
}
\ No newline at end of file
diff --git a/simulationcore_purrr.R b/simulationcore_purrr.R
index b95ce7573862d654e9fa53f7f5d23da156eb5ead..67f02e0460bd932d6e5f36ead8b6ce58002d6cb1 100644
--- a/simulationcore_purrr.R
+++ b/simulationcore_purrr.R
@@ -6,8 +6,9 @@ library("formula.tools")
library(ggplot2)
library(stringr)
-source("Projects/ValuGaps/parameters_valugaps.R")
-source(params$file)
+source("Projects/SE_AGRI/parameters_SE Design-Agri.R")
+#source("Projects/ValuGaps/parameters_valugaps.R",echo = TRUE)
+#source(params$file)
designfile<-list.files(designpath,full.names = T)
@@ -39,7 +40,7 @@ powa <- map(all_designs, ~ .x$power)
summaryall <- as.data.frame(map(all_designs, ~.x$summary)) %>%
- select(!ends_with("vars")) %>%
+ dplyr::select(!ends_with("vars")) %>%
relocate(ends_with(c(".n", "mean","sd", "min" ,"max", "range" , "se" )))
coefall <- map(all_designs, ~ .x$coefs)
@@ -47,7 +48,7 @@ coefall <- map(all_designs, ~ .x$coefs)
pat<-paste0("(",paste(designname,collapse = "|"),").") # needed to identify pattern to be replaced
s<-as.data.frame(coefall) %>%
- select(!matches("pval|run")) %>%
+ dplyr::select(!matches("pval|run")) %>%
rename_with(~ sub("est_b", "", .x), everything()) %>%
# rename_with(~ sub("est_asc_", "asc", .x), everything()) %>%
rename_with( ~ paste0(.,"_",stringr::str_extract(.,pat )), everything() ) %>% # rename attributes for reshape part 1
@@ -57,7 +58,7 @@ s<-as.data.frame(coefall) %>%
p=list()
-for (att in names(select(s,-c("design","run")))) {
+for (att in names(dplyr::select(s,-c("design","run")))) {
p[[att]] <- plot_multi_histogram(s,att,"design")