groceryheist/ml_measurement_error_public
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Merge branch 'master' of code:ml_measurement_error_public

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Nathan TeBlunthuis 2023-08-12 13:10:19 -07:00
commit bb6f5e4731
4 changed files with 14 additions and 6 deletions
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6
.gitmodules vendored
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@ -1,3 +1,9 @@
[submodule "paper"]
path = paper
url = git@github.com:chainsawriot/measure.git
[submodule "overleaf"]
path = overleaf
url = https://git.overleaf.com/62a956eb9b9254783cc84c82
[submodule "misclassificationmodels"]
path = misclassificationmodels
url = https://github.com/chainsawriot/misclassificationmodels.git

1
misclassificationmodels Submodule

@ -0,0 +1 @@
Subproject commit 1dc39a1af5698e409c7c1d997a8b2fbc06faa6eb

1
overleaf Submodule

@ -0,0 +1 @@
Subproject commit c5e0a0171397981e8f6a0ad1ad1ee3ce8e0fa15f

12
simulations/measerr_methods.R
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@ -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)