From: Nathan TeBlunthuis <nathante@uw.edu>
Date: Wed, 1 Mar 2023 18:39:35 +0000 (-0800)
Subject: pass through optimization parameters
X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/commitdiff_plain/ce6d12d4e6955ba501c9b7ca6715646cf52a5815?ds=sidebyside;hp=-c

pass through optimization parameters
---

ce6d12d4e6955ba501c9b7ca6715646cf52a5815
diff --git a/simulations/measerr_methods.R b/simulations/measerr_methods.R
index 92309ed..98ab28d 100644
--- a/simulations/measerr_methods.R
+++ b/simulations/measerr_methods.R
@@ -23,7 +23,7 @@ likelihood.logistic <- function(model.params, outcome, model.matrix){
 }
 
 ## 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'){
+measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit'),maxit=1e6, method='optim'){
     df.obs <- model.frame(outcome_formula, df)
     proxy.model.matrix <- model.matrix(proxy_formula, df)
     proxy.variable <- all.vars(proxy_formula)[1]
@@ -106,7 +106,7 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
     names(start) <- params
     
     if(method=='optim'){
-        fit <- optim(start, fn = nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
+        fit <- optim(start, fn = nll, lower=lower, method=optim_method, hessian=TRUE, control=list(maxit=maxit))
     } else {
         quoted.names <- gsub("[\\(\\)]",'',names(start))
         print(quoted.names)
@@ -115,13 +115,13 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
         measerr_mle_nll <- eval(parse(text=text))
         names(start) <- quoted.names
         names(lower) <- quoted.names
-        fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B')
+        fit <- mle2(minuslogl=measerr_mle_nll, start=start, lower=lower, parnames=params,control=list(maxit=maxit),method=optim_method)
     }
     return(fit)
 }
 
 
-measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim'){
+measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_formula, proxy_family=binomial(link='logit'), truth_formula, truth_family=binomial(link='logit'),method='optim', maxit=1e6, optim_method='L-BFGS-B'){
 
     df.obs <- model.frame(outcome_formula, df)
     response.var <- all.vars(outcome_formula)[1]
@@ -240,7 +240,7 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
     names(start) <- params
 
     if(method=='optim'){
-        fit <- optim(start, fn = measerr_mle_nll, lower=lower, method='L-BFGS-B', hessian=TRUE, control=list(maxit=1e6))
+        fit <- optim(start, fn = measerr_mle_nll, lower=lower, method=optim_method, hessian=TRUE, control=list(maxit=maxit))
     } else { # method='mle2'
                 
         quoted.names <- gsub("[\\(\\)]",'',names(start))
@@ -250,7 +250,7 @@ measerr_mle <- function(df, outcome_formula, outcome_family=gaussian(), proxy_fo
         measerr_mle_nll_mle <- eval(parse(text=text))
         names(start) <- quoted.names
         names(lower) <- quoted.names
-        fit <- mle2(minuslogl=measerr_mle_nll_mle, start=start, lower=lower, parnames=params,control=list(maxit=1e6),method='L-BFGS-B')
+        fit <- mle2(minuslogl=measerr_mle_nll_mle, start=start, lower=lower, parnames=params,control=list(maxit=maxit),method=optim_method)
     }
 
     return(fit)