separate models and data 1

Author: martin-smira

Bayesian_Cognitive_Modeling/GettingStarted/Rate_1.data.R

@@ -0,0 +1,2 @@
+k <- 5
+n <- 10

Bayesian_Cognitive_Modeling/GettingStarted/Rate_1_Stan.R

@@ -3,31 +3,12 @@
 # what it does.
 
 # clears workspace:  
-rm(list=ls(all=TRUE)) 
+rm(list=ls()) 
 
 library(rstan)
 
-model <- "
-// Inferring a Rate
-data { 
-  int<lower=1> n; 
-  int<lower=0> k;
-} 
-parameters {
-  real<lower=0,upper=1> theta;
-} 
-model {
-  // Prior Distribution for Rate Theta
-  theta ~ beta(1, 1);
-  
-  // Observed Counts
-  k ~ binomial(n, theta);
-}"
-
-k <- 5
-n <- 10
-
-data <- list(k=k, n=n) # to be passed on to Stan
+# to be passed on to Stan
+data <- read_rdump("Rate_1.data.R") 
 
 myinits <- list(
   list(theta=.1),  # chain 1 starting value
@@ -38,16 +19,16 @@ parameters <- c("theta")
 
 # The following command calls Stan with specific options.
 # For a detailed description type "?rstan".
-samples <- stan(model_code=model,   
+samples <- stan(file="Rate_1_model.stan",   
                 data=data, 
                 init=myinits,  # If not specified, gives random inits
                 pars=parameters,
-                iter=40000, 
+                iter=20000, 
                 chains=2, 
                 thin=1,
                 # warmup = 100,  # Stands for burn-in; Default = iter/2
                 # seed = 123  # Setting seed; Default is random seed
-                )
+)
 # Now the values for the monitored parameters are in the "samples" object, 
 # ready for inspection.
 

Bayesian_Cognitive_Modeling/GettingStarted/Rate_1_model.stan

@@ -0,0 +1,15 @@
+// Inferring a Rate
+data { 
+  int<lower=1> n; 
+  int<lower=0> k;
+} 
+parameters {
+  real<lower=0,upper=1> theta;
+} 
+model {
+  // Prior Distribution for Rate Theta
+  theta ~ beta(1, 1);
+  
+  // Observed Counts
+  k ~ binomial(n, theta);
+}

Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial/Rate_1.data.R

@@ -0,0 +1,2 @@
+k <- 5
+n <- 10

Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial/Rate_1_Stan.R

@@ -7,46 +7,28 @@ rm(list=ls())
 
 library(rstan)
 
-model <- "
-// Inferring a Rate
-data { 
-  int<lower=1> n; 
-  int<lower=0> k;
-} 
-parameters {
-  real<lower=0,upper=1> theta;
-} 
-model {
-  // Prior Distribution for Rate Theta
-  theta ~ beta(1, 1);
-  // Observed Counts
-  k ~ binomial(n, theta);
-}"
-
-k <- 5
-n <- 10
-
-data <- list(k=k, n=n) # to be passed on to Stan
+# to be passed on to Stan
+data <- read_rdump("Rate_1.data.R") 
 
 myinits <- list(
-  list(theta=0.1), #chain 1 starting value
-  list(theta=0.9)) #chain 2 starting value
+  list(theta=.1),  # chain 1 starting value
+  list(theta=.9))  # chain 2 starting value
 
 # parameters to be monitored:  
 parameters <- c("theta")
 
 # The following command calls Stan with specific options.
-# For a detailed description type "?rstan".
-samples <- stan(model_code=model,   
+# For a detailed description type "?stan".
+samples <- stan(file="Rate_1_model.stan",   
                 data=data, 
                 init=myinits,  # If not specified, gives random inits
                 pars=parameters,
-                iter=40000, 
+                iter=20000, 
                 chains=2, 
                 thin=1,
-                # warmup = 100,  # Stands for burn-in; Default = iter/2
-                # seed = 123  # Setting seed; Default is random seed
-                )
+                # warmup=100,  # Stands for burn-in; Default = iter/2
+                # seed=123  # Setting seed; Default is random seed
+)
 # Now the values for the monitored parameters are in the "samples" object, 
 # ready for inspection.
 

Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial/Rate_1_model.stan

@@ -0,0 +1,15 @@
+// Inferring a Rate
+data { 
+  int<lower=1> n; 
+  int<lower=0> k;
+} 
+parameters {
+  real<lower=0,upper=1> theta;
+} 
+model {
+  // Prior Distribution for Rate Theta
+  theta ~ beta(1, 1);
+  
+  // Observed Counts
+  k ~ binomial(n, theta);
+}

Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial/Rate_2,3.data.R

@@ -0,0 +1,4 @@
+k1 <- 5
+k2 <- 7
+n1 <- 10
+n2 <- 10

Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial/Rate_2_Stan.R

@@ -3,37 +3,7 @@ rm(list=ls())
 
 library(rstan)
 
-model <- "
-// Difference Between Two Rates
-data { 
-  int<lower=1> n1; 
-  int<lower=1> n2; 
-  int<lower=0> k1;
-  int<lower=0> k2;
-} 
-parameters {
-  real<lower=0,upper=1> theta1;
-  real<lower=0,upper=1> theta2;
-} 
-transformed parameters {
-  real<lower=-1,upper=1> delta;
-  delta <- theta1 - theta2;
-}
-model {
-  // Prior Distribution for Rate Theta
-  theta1 ~ beta(1, 1);
-  theta2 ~ beta(1, 1);
-  // Observed Counts
-  k1 ~ binomial(n1, theta1);
-  k2 ~ binomial(n2, theta2);
-}"
-
-k1 <- 5
-k2 <- 7
-n1 <- 10
-n2 <- 10
-
-data <- list(k1=k1, k2=k2, n1=n1, n2=n2)  # to be passed on to Stan
+data <- read_rdump("Rate_2,3.data.R")  # to be passed on to Stan
 
 myinits <- list(
   list(theta1=0.1, theta2=0.9))
@@ -42,8 +12,8 @@ myinits <- list(
 parameters <- c("delta", "theta1", "theta2")
 
 # The following command calls Stan with specific options.
-# For a detailed description type "?rstan".
-samples <- stan(model_code=model,   
+# For a detailed description type "?stan".
+samples <- stan(file="Rate_2_model.stan",   
                 data=data, 
                 init=myinits,  # If not specified, gives random inits
                 pars=parameters,
@@ -52,7 +22,7 @@ samples <- stan(model_code=model,
                 thin=1,
                 # warmup=100,  # Stands for burn-in; Default = iter/2
                 # seed=123  # Setting seed; Default is random seed
-                )
+)
 # Now the values for the monitored parameters are in the "samples" object, 
 # ready for inspection.
 

Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial/Rate_2_model.stan

@@ -0,0 +1,23 @@
+// Difference Between Two Rates
+data { 
+  int<lower=1> n1; 
+  int<lower=1> n2; 
+  int<lower=0> k1;
+  int<lower=0> k2;
+} 
+parameters {
+  real<lower=0,upper=1> theta1;
+  real<lower=0,upper=1> theta2;
+} 
+transformed parameters {
+  real<lower=-1,upper=1> delta;
+  delta <- theta1 - theta2;
+}
+model {
+  // Prior Distribution for Rate Theta
+  theta1 ~ beta(1, 1);
+  theta2 ~ beta(1, 1);
+  // Observed Counts
+  k1 ~ binomial(n1, theta1);
+  k2 ~ binomial(n2, theta2);
+}

Bayesian_Cognitive_Modeling/ParameterEstimation/Binomial/Rate_3_Stan.R

@@ -3,31 +3,7 @@ rm(list=ls())
 
 library(rstan)
 
-model <- "
-// Inferring a Common Rate
-data { 
-  int<lower=1> n1; 
-  int<lower=1> n2; 
-  int<lower=0> k1;
-  int<lower=0> k2;
-} 
-parameters {
-  real<lower=0,upper=1> theta;
-} 
-model {
-  // Prior on Single Rate Theta
-  theta ~ beta(1, 1);
-  // Observed Counts
-  k1 ~ binomial(n1, theta);
-  k2 ~ binomial(n2, theta);
-}"
-
-k1 <- 5
-k2 <- 7
-n1 <- 10
-n2 <- 10
-
-data <- list(k1=k1, k2=k2, n1=n1, n2=n2)  # to be passed on to Stan
+data <- read_rdump("Rate_2,3.data.R")  # to be passed on to Stan
 
 myinits <- list(
   list(theta=0.5))
@@ -36,8 +12,8 @@ myinits <- list(
 parameters <- c("theta")
 
 # The following command calls Stan with specific options.
-# For a detailed description type "?rstan".
-samples <- stan(model_code=model,   
+# For a detailed description type "?stan".
+samples <- stan(file="Rate_3_model.stan",   
                 data=data, 
                 init=myinits,  # If not specified, gives random inits
                 pars=parameters,
@@ -46,7 +22,7 @@ samples <- stan(model_code=model,
                 thin=1,
                 # warmup = 100,  # Stands for burn-in; Default = iter/2
                 # seed = 123  # Setting seed; Default is random seed
-                )
+)
 print(samples)
 
 theta <- extract(samples)$theta