check in some old simulation updates and a dv examples with real data

[ml_measurement_error_public.git] / simulations / measerr_methods.R

diff --git a/simulations/measerr_methods.R b/simulations/measerr_methods.R
index 63f8bc19df116b2f6d148c55cefbd89cf2dc7b85..fdc4978b72e32c7988634fb2265681c61a033b96 100644 (file)
--- a/simulations/measerr_methods.R
+++ b/simulations/measerr_methods.R
@@ -19,14 +19,29 @@ library(bbmle)
 
 ## 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'){
+    df.obs <- model.frame(outcome_formula, df)
+    proxy.model.matrix <- model.matrix(proxy_formula, df)
+    proxy.variable <- all.vars(proxy_formula)[1]
+
+    df.proxy.obs <- model.frame(proxy_formula,df)
+    proxy.obs <- with(df.proxy.obs, eval(parse(text=proxy.variable)))
+
+    response.var <- all.vars(outcome_formula)[1]
+    y.obs <- with(df.obs,eval(parse(text=response.var)))
+    outcome.model.matrix <- model.matrix(outcome_formula, df.obs)
+
+    df.unobs <- df[is.na(df[[response.var]])]
+    df.unobs.y1 <- copy(df.unobs)
+    df.unobs.y1[[response.var]] <- 1
+    df.unobs.y0 <- copy(df.unobs)
+    df.unobs.y0[[response.var]] <- 0
+
+    outcome.model.matrix.y1 <- model.matrix(outcome_formula, df.unobs.y1)
+    proxy.model.matrix.y1 <- model.matrix(proxy_formula, df.unobs.y1)
+    proxy.model.matrix.y0 <- model.matrix(proxy_formula, df.unobs.y0)
+    proxy.unobs <- with(df.unobs, eval(parse(text=proxy.variable)))
 
     nll <- function(params){
-        df.obs <- model.frame(outcome_formula, df)
-        proxy.variable <- all.vars(proxy_formula)[1]
-        proxy.model.matrix <- model.matrix(proxy_formula, df)
-        response.var <- all.vars(outcome_formula)[1]
-        y.obs <- with(df.obs,eval(parse(text=response.var)))
-        outcome.model.matrix <- model.matrix(outcome_formula, df.obs)
 
         param.idx <- 1
         n.outcome.model.covars <- dim(outcome.model.matrix)[2]
@@ -39,12 +54,9 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
             ll.y.obs[y.obs==0] <- plogis(outcome.params %*% t(outcome.model.matrix[y.obs==0,]),log=TRUE,lower.tail=FALSE)
         }
 
-        df.obs <- model.frame(proxy_formula,df)
         n.proxy.model.covars <- dim(proxy.model.matrix)[2]
         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])
@@ -53,15 +65,8 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
         }
 
         ll.obs <- sum(ll.y.obs + ll.w.obs)
-
-        df.unobs <- df[is.na(df[[response.var]])]
-        df.unobs.y1 <- copy(df.unobs)
-        df.unobs.y1[[response.var]] <- 1
-        df.unobs.y0 <- copy(df.unobs)
-        df.unobs.y0[[response.var]] <- 0
         
         ## integrate out y
-        outcome.model.matrix.y1 <- model.matrix(outcome_formula, df.unobs.y1)
 
         if((outcome_family$family == "binomial") & (outcome_family$link == 'logit')){
             ll.y.unobs.1 <- vector(mode='numeric', length=dim(outcome.model.matrix.y1)[1])
@@ -70,10 +75,6 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
             ll.y.unobs.0 <- plogis(outcome.params %*% t(outcome.model.matrix.y1),log=TRUE,lower.tail=FALSE)
         }
 
-        proxy.model.matrix.y1 <- model.matrix(proxy_formula, df.unobs.y1)
-        proxy.model.matrix.y0 <- model.matrix(proxy_formula, df.unobs.y0)
-        proxy.unobs <- with(df.unobs, eval(parse(text=proxy.variable)))
-
         if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){
             ll.w.unobs.1 <- vector(mode='numeric',length=dim(proxy.model.matrix.y1)[1])
             ll.w.unobs.0 <- vector(mode='numeric',length=dim(proxy.model.matrix.y0)[1])
@@ -431,7 +432,7 @@ measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), code
 ## Experimental, and does not work.
 measerr_irr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), coder_formulas=list(y.obs.0~y+w_pred+y.obs.1,y.obs.1~y+w_pred+y.obs.0), proxy_formula=w_pred~y, proxy_family=binomial(link='logit'),method='optim'){
     integrate.grid <- expand.grid(replicate(1 + length(coder_formulas), c(0,1), simplify=FALSE))
-    print(integrate.grid)
+#    print(integrate.grid)
 
 
     outcome.model.matrix <- model.matrix(outcome_formula, df)
@@ -527,8 +528,8 @@ measerr_irr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link
 
             ## likelihood of observed data 
             target <- -1 * sum(lls)
-            print(target)
-            print(params)
+#            print(target)
+#            print(params)
             return(target)
         }
     }