Merge pull request #194 from andrjohns/feature/arm_ch4

rok-cesnovar · web-flow · commit b3a64d038383 · 2021-02-06T16:38:35.000+01:00
Update ARM Ch.4 Example Models
diff --git a/ARM/Ch.4/earn_height.stan b/ARM/Ch.4/earn_height.stan
@@ -3,10 +3,14 @@ data {
   vector[N] earn;
   vector[N] height;
 }
+transformed data {
+  matrix[N,1] x = [height']';
+}
 parameters {
-  vector[2] beta;
+  real alpha;
+  vector[1] beta;
   real<lower=0> sigma;
 }
 model {
-  earn ~ normal(beta[1] + beta[2] * height, sigma);
+  earn ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/kidiq_interaction.stan b/ARM/Ch.4/kidiq_interaction.stan
@@ -5,14 +5,14 @@ data {
   vector[N] mom_iq;
 }
 transformed data {           // interaction
-  vector[N] inter;
-  inter = mom_hs .* mom_iq;
+  vector[N] inter = mom_hs .* mom_iq;
+  matrix[N,3] x = [mom_hs', mom_iq', inter']';
 }
 parameters {
-  vector[4] beta;
+  real alpha;
+  vector[3] beta;
   real<lower=0> sigma;
 }
 model {
-  kid_score ~ normal(beta[1] + beta[2] * mom_hs + beta[3] * mom_iq 
-                     + beta[4] * inter, sigma);
+  kid_score ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/kidiq_interaction_c.stan b/ARM/Ch.4/kidiq_interaction_c.stan
@@ -5,18 +5,16 @@ data {
   vector[N] mom_iq;
 }
 transformed data {           // centered predictors
-  vector[N] c_mom_hs;
-  vector[N] c_mom_iq;
-  vector[N] inter;
-  c_mom_hs = mom_hs - mean(mom_hs);
-  c_mom_iq = mom_iq - mean(mom_iq);  
-  inter    = c_mom_hs .* c_mom_iq;
+  vector[N] c_mom_hs = mom_hs - mean(mom_hs);
+  vector[N] c_mom_iq = mom_iq - mean(mom_iq);
+  vector[N] inter = c_mom_hs .* c_mom_iq;
+  matrix[N,3] x = [c_mom_hs', c_mom_iq', inter']';
 }
 parameters {
-  vector[4] beta;
+  real alpha;
+  vector[3] beta;
   real<lower=0> sigma;
 }
 model {
-  kid_score ~ normal(beta[1] + beta[2] * c_mom_hs + beta[3] * c_mom_iq 
-                     + beta[4] * inter, sigma);
+  kid_score ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/kidiq_interaction_c2.stan b/ARM/Ch.4/kidiq_interaction_c2.stan
@@ -5,18 +5,16 @@ data {
   vector[N] mom_iq;
 }
 transformed data {           // centering on reference points
-  vector[N] c2_mom_hs;
-  vector[N] c2_mom_iq;
-  vector[N] inter;
-  c2_mom_hs = mom_hs - 0.5;
-  c2_mom_iq = mom_iq - 100;  
-  inter     = c2_mom_hs .* c2_mom_iq;
+  vector[N] c2_mom_hs = mom_hs - 0.5;
+  vector[N] c2_mom_iq = mom_iq - 100;
+  vector[N] inter = c2_mom_hs .* c2_mom_iq;
+  matrix[N,3] x = [c2_mom_hs', c2_mom_iq', inter']';
 }
 parameters {
-  vector[4] beta;
+  real alpha;
+  vector[3] beta;
   real<lower=0> sigma;
 }
 model {
-  kid_score ~ normal(beta[1] + beta[2] * c2_mom_hs + beta[3] * c2_mom_iq 
-                     + beta[4] * inter, sigma);
+  kid_score ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/kidiq_interaction_z.stan b/ARM/Ch.4/kidiq_interaction_z.stan
@@ -5,18 +5,16 @@ data {
   vector[N] mom_iq;
 }
 transformed data {           // standardizing
-  vector[N] z_mom_hs;
-  vector[N] z_mom_iq;
-  vector[N] inter;
-  z_mom_hs = (mom_hs - mean(mom_hs)) / (2 * sd(mom_hs));
-  z_mom_iq = (mom_iq - mean(mom_iq)) / (2 * sd(mom_iq));
-  inter    = z_mom_hs .* z_mom_iq;
+  vector[N] z_mom_hs = (mom_hs - mean(mom_hs)) / (2 * sd(mom_hs));
+  vector[N] z_mom_iq = (mom_iq - mean(mom_iq)) / (2 * sd(mom_iq));
+  vector[N] inter= z_mom_hs .* z_mom_iq;
+  matrix[N,3] x = [z_mom_hs', z_mom_iq', inter']';
 }
 parameters {
-  vector[4] beta;
+  real alpha;
+  vector[3] beta;
   real<lower=0> sigma;
 }
 model {
-  kid_score ~ normal(beta[1] + beta[2] * z_mom_hs + beta[3] * z_mom_iq 
-                     + beta[4] * inter, sigma);
+  kid_score ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/kidscore_momwork.stan b/ARM/Ch.4/kidscore_momwork.stan
@@ -7,17 +7,21 @@ transformed data {
   vector[N] work2;
   vector[N] work3;
   vector[N] work4;
+  matrix[N,3] x;
+
   for (i in 1:N) {
     work2[i] = mom_work[i] == 2;
     work3[i] = mom_work[i] == 3;
     work4[i] = mom_work[i] == 4;
   }
+
+  x = [work2', work3', work4']';
 }
 parameters {
-  vector[4] beta;
+  real alpha;
+  vector[3] beta;
   real<lower=0> sigma;
 }
 model {
-  kid_score ~ normal(beta[1] + beta[2] * work2 + beta[3] * work3
-                     + beta[4] * work4, sigma);
+  kid_score ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/log10earn_height.stan b/ARM/Ch.4/log10earn_height.stan
@@ -4,15 +4,14 @@ data {
   vector[N] height;
 }
 transformed data {           // log 10 transformation
-  vector[N] log10_earn;      
-  for (i in 1:N) {                       
-    log10_earn[i] = log10(earn[i]);
-  }
+  vector[N] log10_earn = log10(earn);
+  matrix[N,1] x = [height']';
 }
 parameters {
-  vector[2] beta;
+  real alpha;
+  vector[1] beta;
   real<lower=0> sigma;
 }
 model {
-  log10_earn ~ normal(beta[1] + beta[2] * height, sigma);
+  log10_earn ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/logearn_height.stan b/ARM/Ch.4/logearn_height.stan
@@ -4,13 +4,14 @@ data {
   vector[N] height;
 }
 transformed data {           // log transformation
-  vector[N] log_earn;        
-  log_earn = log(earn);
+  vector[N] log_earn = log(earn);
+  matrix[N,1] x = [height']';
 }
 parameters {
-  vector[2] beta;
+  real alpha;
+  vector[1] beta;
   real<lower=0> sigma;
 }
 model {
-  log_earn ~ normal(beta[1] + beta[2] * height, sigma);
+  log_earn ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/logearn_height_male.stan b/ARM/Ch.4/logearn_height_male.stan
@@ -5,13 +5,14 @@ data {
   vector[N] male;
 }
 transformed data {           // log transformation
-  vector[N] log_earn;        
-  log_earn = log(earn);
+  vector[N] log_earn = log(earn);
+  matrix[N,2] x = [height', male']';
 }
 parameters {
-  vector[3] beta;
+  real alpha;
+  vector[2] beta;
   real<lower=0> sigma;
 }
 model {
-  log_earn ~ normal(beta[1] + beta[2] * height + beta[3] * male, sigma);
+  log_earn ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/logearn_interaction.stan b/ARM/Ch.4/logearn_interaction.stan
@@ -5,16 +5,17 @@ data {
   vector[N] male;
 }
 transformed data {
-  vector[N] log_earn;        // log transformation
-  vector[N] inter;           // interaction
-  log_earn = log(earn);        
-  inter    = height .* male;
+  // log transformation
+  vector[N] log_earn = log(earn);
+  // interaction
+  vector[N] inter = height .* male;
+  matrix[N,3] x = [height', male', inter']';
 }
 parameters {
-  vector[4] beta;
+  real alpha;
+  vector[3] beta;
   real<lower=0> sigma;
 }
 model {
-  log_earn ~ normal(beta[1] + beta[2] * height + beta[3] * male 
-                    + beta[4] * inter, sigma);
+  log_earn ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/logearn_interaction_z.stan b/ARM/Ch.4/logearn_interaction_z.stan
@@ -5,18 +5,19 @@ data {
   vector[N] male;
 }
 transformed data {
-  vector[N] log_earn;        // log transformation
-  vector[N] z_height;        // standardization
-  vector[N] inter;           // interaction
-  log_earn = log(earn);
-  z_height = (height - mean(height)) / sd(height);
-  inter    = z_height .* male;
+  // log transformation
+  vector[N] log_earn = log(earn);
+  // standardization
+  vector[N] z_height = (height - mean(height)) / sd(height);
+  // interaction
+  vector[N] inter = z_height .* male;
+  matrix[N,3] x = [z_height', male', inter']';
 }
 parameters {
-  vector[4] beta;
+  real alpha;
+  vector[3] beta;
   real<lower=0> sigma;
 }
 model {
-  log_earn ~ normal(beta[1] + beta[2] * z_height + beta[3] * male
-                      + beta[4] * inter, sigma);
+  log_earn ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/logearn_logheight.stan b/ARM/Ch.4/logearn_logheight.stan
@@ -5,15 +5,16 @@ data {
   vector[N] male;
 }
 transformed data {
-  vector[N] log_earn;        // log transformations
-  vector[N] log_height;
-  log_earn   = log(earn);
-  log_height = log(height);
+  // log transformations
+  vector[N] log_earn = log(earn);
+  vector[N] log_height = log(height);
+  matrix[N,2] x = [log_height', male']';
 }
 parameters {
-  vector[3] beta;
+  real alpha;
+  vector[2] beta;
   real<lower=0> sigma;
 }
-model {                      // vectorization
-  log_earn ~ normal(beta[1] + beta[2] * log_height + beta[3] * male, sigma);
+model {
+  log_earn ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/mesquite.stan b/ARM/Ch.4/mesquite.stan
@@ -8,13 +8,15 @@ data {
   vector[N] density;
   vector[N] group;
 }
+transformed data {
+  matrix[N,6] x = [diam1', diam2', canopy_height',
+                   total_height', density', group']';
+}
 parameters {
-  vector[7] beta;
+  real alpha;
+  vector[6] beta;
   real<lower=0> sigma;
 }
 model {
-  weight ~ normal(beta[1] + beta[2] * diam1 + beta[3] * diam2
-                  + beta[4] * canopy_height + beta[5] * total_height
-                  + beta[6] * density + beta[7] * group,
-                  sigma);
+  weight ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/mesquite_log.stan b/ARM/Ch.4/mesquite_log.stan
@@ -9,26 +9,20 @@ data {
   vector[N] group;
 }
 transformed data {           // log transformations
-  vector[N] log_weight;
-  vector[N] log_diam1;
-  vector[N] log_diam2;
-  vector[N] log_canopy_height;
-  vector[N] log_total_height;
-  vector[N] log_density;
-  log_weight        = log(weight);
-  log_diam1         = log(diam1);
-  log_diam2         = log(diam2);
-  log_canopy_height = log(canopy_height);
-  log_total_height  = log(total_height);
-  log_density       = log(density);
+  vector[N] log_weight = log(weight);
+  vector[N] log_diam1 = log(diam1);
+  vector[N] log_diam2 = log(diam2);
+  vector[N] log_canopy_height = log(canopy_height);
+  vector[N] log_total_height = log(total_height);
+  vector[N] log_density = log(density);
+  matrix[N,6] x = [log_diam1', log_diam2', log_canopy_height',
+                   log_total_height', log_density', group']';
 }
 parameters {
-  vector[7] beta;
+  real alpha;
+  vector[6] beta;
   real<lower=0> sigma;
 }
 model {
-  log_weight ~ normal(beta[1] + beta[2] * log_diam1 + beta[3] * log_diam2
-                  + beta[4] * log_canopy_height + beta[5] * log_total_height
-                  + beta[6] * log_density + beta[7] * group,
-                  sigma);
+  log_weight ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/mesquite_va.stan b/ARM/Ch.4/mesquite_va.stan
@@ -7,19 +7,16 @@ data {
   vector[N] group;
 }
 transformed data {
-  vector[N] log_weight;
-  vector[N] log_canopy_volume;
-  vector[N] log_canopy_area;
-  log_weight        = log(weight);
-  log_canopy_volume = log(diam1 .* diam2 .* canopy_height);
-  log_canopy_area   = log(diam1 .* diam2);
+  vector[N] log_weight = log(weight);
+  vector[N] log_canopy_volume = log(diam1 .* diam2 .* canopy_height);
+  vector[N] log_canopy_area = log(diam1 .* diam2);
+  matrix[N,3] x = [log_canopy_volume', log_canopy_area', group']';
 }
 parameters {
-  vector[4] beta;
+  real alpha;
+  vector[3] beta;
   real<lower=0> sigma;
 }
 model {
-  log_weight ~ normal(beta[1] + beta[2] * log_canopy_volume
-                      + beta[3] * log_canopy_area + beta[4] * group,
-                      sigma);
+  log_weight ~ normal_id_glm(x, alpha, beta, sigma);
 }
diff --git a/ARM/Ch.4/mesquite_vas.stan b/ARM/Ch.4/mesquite_vas.stan
diff --git a/ARM/Ch.4/mesquite_vash.stan b/ARM/Ch.4/mesquite_vash.stan
diff --git a/ARM/Ch.4/mesquite_volume.stan b/ARM/Ch.4/mesquite_volume.stan
diff --git a/ARM/Ch.4/nes.stan b/ARM/Ch.4/nes.stan

Original file line number	Diff line number	Diff line change
`@@ -3,10 +3,14 @@ data {`
`3`	`3`	`vector[N] earn;`
`4`	`4`	`vector[N] height;`
`5`	`5`	`}`
	`6`	`+transformed data {`
	`7`	`+ matrix[N,1] x = [height']';`
	`8`	`+}`
`6`	`9`	`parameters {`
`7`		`- vector[2] beta;`
	`10`	`+ real alpha;`
	`11`	`+ vector[1] beta;`
`8`	`12`	`real<lower=0> sigma;`
`9`	`13`	`}`
`10`	`14`	`model {`
`11`		`- earn ~ normal(beta[1] + beta[2] * height, sigma);`
	`15`	`+ earn ~ normal_id_glm(x, alpha, beta, sigma);`
`12`	`16`	`}`
Original file line number	Diff line number	Diff line change
`@@ -5,14 +5,14 @@ data {`
`5`	`5`	`vector[N] mom_iq;`
`6`	`6`	`}`
`7`	`7`	`transformed data { // interaction`
`8`		`- vector[N] inter;`
`9`		`- inter = mom_hs .* mom_iq;`
	`8`	`+ vector[N] inter = mom_hs .* mom_iq;`
	`9`	`+ matrix[N,3] x = [mom_hs', mom_iq', inter']';`
`10`	`10`	`}`
`11`	`11`	`parameters {`
`12`		`- vector[4] beta;`
	`12`	`+ real alpha;`
	`13`	`+ vector[3] beta;`
`13`	`14`	`real<lower=0> sigma;`
`14`	`15`	`}`
`15`	`16`	`model {`
`16`		`- kid_score ~ normal(beta[1] + beta[2] * mom_hs + beta[3] * mom_iq`
`17`		`- + beta[4] * inter, sigma);`
	`17`	`+ kid_score ~ normal_id_glm(x, alpha, beta, sigma);`
`18`	`18`	`}`
Original file line number	Diff line number	Diff line change
`@@ -4,15 +4,14 @@ data {`
`4`	`4`	`vector[N] height;`
`5`	`5`	`}`
`6`	`6`	`transformed data { // log 10 transformation`
`7`		`- vector[N] log10_earn;`
`8`		`- for (i in 1:N) {`
`9`		`- log10_earn[i] = log10(earn[i]);`
`10`		`- }`
	`7`	`+ vector[N] log10_earn = log10(earn);`
	`8`	`+ matrix[N,1] x = [height']';`
`11`	`9`	`}`
`12`	`10`	`parameters {`
`13`		`- vector[2] beta;`
	`11`	`+ real alpha;`
	`12`	`+ vector[1] beta;`
`14`	`13`	`real<lower=0> sigma;`
`15`	`14`	`}`
`16`	`15`	`model {`
`17`		`- log10_earn ~ normal(beta[1] + beta[2] * height, sigma);`
	`16`	`+ log10_earn ~ normal_id_glm(x, alpha, beta, sigma);`
`18`	`17`	`}`
Original file line number	Diff line number	Diff line change
`@@ -4,13 +4,14 @@ data {`
`4`	`4`	`vector[N] height;`
`5`	`5`	`}`
`6`	`6`	`transformed data { // log transformation`
`7`		`- vector[N] log_earn;`
`8`		`- log_earn = log(earn);`
	`7`	`+ vector[N] log_earn = log(earn);`
	`8`	`+ matrix[N,1] x = [height']';`
`9`	`9`	`}`
`10`	`10`	`parameters {`
`11`		`- vector[2] beta;`
	`11`	`+ real alpha;`
	`12`	`+ vector[1] beta;`
`12`	`13`	`real<lower=0> sigma;`
`13`	`14`	`}`
`14`	`15`	`model {`
`15`		`- log_earn ~ normal(beta[1] + beta[2] * height, sigma);`
	`16`	`+ log_earn ~ normal_id_glm(x, alpha, beta, sigma);`
`16`	`17`	`}`
Original file line number	Diff line number	Diff line change
`@@ -5,13 +5,14 @@ data {`
`5`	`5`	`vector[N] male;`
`6`	`6`	`}`
`7`	`7`	`transformed data { // log transformation`
`8`		`- vector[N] log_earn;`
`9`		`- log_earn = log(earn);`
	`8`	`+ vector[N] log_earn = log(earn);`
	`9`	`+ matrix[N,2] x = [height', male']';`
`10`	`10`	`}`
`11`	`11`	`parameters {`
`12`		`- vector[3] beta;`
	`12`	`+ real alpha;`
	`13`	`+ vector[2] beta;`
`13`	`14`	`real<lower=0> sigma;`
`14`	`15`	`}`
`15`	`16`	`model {`
`16`		`- log_earn ~ normal(beta[1] + beta[2] * height + beta[3] * male, sigma);`
	`17`	`+ log_earn ~ normal_id_glm(x, alpha, beta, sigma);`
`17`	`18`	`}`