X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/fa05dbab6bd2c5db6ed4eccf38cff03bb4fd6683..b8d2048cc5338fbd872b55029c3e5d01c739a397:/simulations/measerr_methods.R

diff --git a/simulations/measerr_methods.R b/simulations/measerr_methods.R
index fdc4978..92309ed 100644
--- a/simulations/measerr_methods.R
+++ b/simulations/measerr_methods.R
@@ -15,7 +15,12 @@ library(bbmle)
 
 ### ideal formulas for example 2
 # test.fit.2 <- measerr_mle(df, y ~ x + z, gaussian(), w_pred ~ x + z + y + y:x, binomial(link='logit'), x ~ z)
-
+likelihood.logistic <- function(model.params, outcome, model.matrix){
+    ll <- vector(mode='numeric', length=length(outcome))
+    ll[outcome == 1] <- plogis(model.params %*% t(model.matrix[outcome==1,]), log=TRUE)
+    ll[outcome == 0] <- plogis(model.params %*% t(model.matrix[outcome==0,]), log=TRUE, lower.tail=FALSE)
+    return(ll)
+}
 
 ## outcome_formula <- y ~ x + z; proxy_formula <- w_pred ~ y + x + z + x:z + x:y + z:y 
 measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit'),method='optim'){
@@ -126,6 +131,31 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
     proxy.model.matrix <- model.matrix(proxy_formula, df)
     y.obs <- with(df.obs,eval(parse(text=response.var)))
 
+    df.proxy.obs <- model.frame(proxy_formula,df)
+    proxy.obs <- with(df.proxy.obs, eval(parse(text=proxy.variable)))
+    n.proxy.model.covars <- dim(proxy.model.matrix)[2]
+
+    df.truth.obs <- model.frame(truth_formula, df)
+    truth.obs <- with(df.truth.obs, eval(parse(text=truth.variable)))
+    truth.model.matrix <- model.matrix(truth_formula,df.truth.obs)
+    n.truth.model.covars <- dim(truth.model.matrix)[2]
+
+    df.unobs <- df[is.na(eval(parse(text=truth.variable)))]
+    df.unobs.x1 <- copy(df.unobs)
+    df.unobs.x1[,truth.variable] <- 1
+    df.unobs.x0 <- copy(df.unobs)
+    df.unobs.x0[,truth.variable] <- 0
+    outcome.unobs <- with(df.unobs, eval(parse(text=response.var)))
+            
+    outcome.model.matrix.x0 <- model.matrix(outcome_formula, df.unobs.x0)
+    outcome.model.matrix.x1 <- model.matrix(outcome_formula, df.unobs.x1)
+        
+    proxy.model.matrix.x0 <- model.matrix(proxy_formula, df.unobs.x0)
+    proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.unobs.x1)
+    proxy.unobs <- df.unobs[[proxy.variable]]
+
+    truth.model.matrix.unobs <- model.matrix(truth_formula, df.unobs.x0)
+
     measerr_mle_nll <- function(params){
         param.idx <- 1
         n.outcome.model.covars <- dim(outcome.model.matrix)[2]
@@ -138,82 +168,48 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
             param.idx <- param.idx + 1
                                         #  outcome_formula likelihood using linear regression
             ll.y.obs <- dnorm(y.obs, outcome.params %*% t(outcome.model.matrix),sd=sigma.y, log=TRUE)
-        }
-        
-        df.obs <- model.frame(proxy_formula,df)
-        n.proxy.model.covars <- dim(proxy.model.matrix)[2]
+        } else if( (outcome_family$family == "binomial") & (outcome_family$link == "logit") )
+            ll.y.obs <- likelihood.logistic(outcome.params, y.obs, outcome.model.matrix)
+
+    
         proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)]
         param.idx <- param.idx + n.proxy.model.covars
-        proxy.obs <- with(df.obs, eval(parse(text=proxy.variable)))
 
-        if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){
-            ll.w.obs <- vector(mode='numeric',length=dim(proxy.model.matrix)[1])
-
-                                        # proxy_formula likelihood using logistic regression
-            ll.w.obs[proxy.obs==1] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==1,]),log=TRUE)
-            ll.w.obs[proxy.obs==0] <- plogis(proxy.params %*% t(proxy.model.matrix[proxy.obs==0,]),log=TRUE, lower.tail=FALSE)
-        }
-
-        df.obs <- model.frame(truth_formula, df)
+        if( (proxy_family$family=="binomial") & (proxy_family$link=='logit'))
+            ll.w.obs <- likelihood.logistic(proxy.params, proxy.obs, proxy.model.matrix)
 
-        truth.obs <- with(df.obs, eval(parse(text=truth.variable)))
-        truth.model.matrix <- model.matrix(truth_formula,df)
-        n.truth.model.covars <- dim(truth.model.matrix)[2]
-        
         truth.params <- params[param.idx:(n.truth.model.covars + param.idx - 1)]
 
-        if( (truth_family$family=="binomial") & (truth_family$link=='logit')){
-            ll.x.obs <- vector(mode='numeric',length=dim(truth.model.matrix)[1])
-
-                                        # truth_formula likelihood using logistic regression
-            ll.x.obs[truth.obs==1] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==1,]),log=TRUE)
-            ll.x.obs[truth.obs==0] <- plogis(truth.params %*% t(truth.model.matrix[truth.obs==0,]),log=TRUE, lower.tail=FALSE)
-        }
+        if( (truth_family$family=="binomial") & (truth_family$link=='logit'))
+            ll.x.obs <- likelihood.logistic(truth.params, truth.obs, truth.model.matrix)
         
-                                        # add the three likelihoods
+                                # add the three likelihoods
         ll.obs <- sum(ll.y.obs + ll.w.obs + ll.x.obs)
 
         ## likelihood for the predicted data
         ## integrate out the "truth" variable. 
         
         if(truth_family$family=='binomial'){
-            df.unobs <- df[is.na(eval(parse(text=truth.variable)))]
-            df.unobs.x1 <- copy(df.unobs)
-            df.unobs.x1[,'x'] <- 1
-            df.unobs.x0 <- copy(df.unobs)
-            df.unobs.x0[,'x'] <- 0
-            outcome.unobs <- with(df.unobs, eval(parse(text=response.var)))
-            
-            outcome.model.matrix.x0 <- model.matrix(outcome_formula, df.unobs.x0)
-            outcome.model.matrix.x1 <- model.matrix(outcome_formula, df.unobs.x1)
             if(outcome_family$family=="gaussian"){
-
                                         # likelihood of outcome
                 ll.y.x0 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x0), sd=sigma.y, log=TRUE)
                 ll.y.x1 <- dnorm(outcome.unobs, outcome.params %*% t(outcome.model.matrix.x1), sd=sigma.y, log=TRUE)
+            } else if( (outcome_family$family == "binomial") & (outcome_family$link == "logit") ){
+                ll.y.x1 <- likelihood.logistic(outcome.params, outcome.unobs, outcome.model.matrix.x1)
+                ll.y.x0 <- likelihood.logistic(outcome.params, outcome.unobs, outcome.model.matrix.x0)
             }
 
             if( (proxy_family$family=='binomial') & (proxy_family$link=='logit')){
 
-                proxy.model.matrix.x0 <- model.matrix(proxy_formula, df.unobs.x0)
-                proxy.model.matrix.x1 <- model.matrix(proxy_formula, df.unobs.x1)
-                proxy.unobs <- df.unobs[[proxy.variable]]
-                ll.w.x0 <- vector(mode='numeric', length=dim(df.unobs)[1])
-                ll.w.x1 <- vector(mode='numeric', length=dim(df.unobs)[1])
-
-                                        # likelihood of proxy
-                ll.w.x0[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.unobs==1,]), log=TRUE)
-                ll.w.x1[proxy.unobs==1] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.unobs==1,]), log=TRUE)
+                ll.w.x0 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.x0)
+                ll.w.x1 <- likelihood.logistic(proxy.params, proxy.unobs, proxy.model.matrix.x1)
 
-                ll.w.x0[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x0[proxy.unobs==0,]), log=TRUE,lower.tail=FALSE)
-                ll.w.x1[proxy.unobs==0] <- plogis(proxy.params %*% t(proxy.model.matrix.x1[proxy.unobs==0,]), log=TRUE,lower.tail=FALSE)
             }
 
             if(truth_family$link=='logit'){
-                truth.model.matrix <- model.matrix(truth_formula, df.unobs.x0)
                                         # likelihood of truth
-                ll.x.x1 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE)
-                ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix), log=TRUE, lower.tail=FALSE)
+                ll.x.x1 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE)
+                ll.x.x0 <- plogis(truth.params %*% t(truth.model.matrix.unobs), log=TRUE, lower.tail=FALSE)
             }
         }