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data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ] %>%
rename(status = seropositive)
aggregated <- transform_data(data, stratum_col = "age", status_col="status")data.frameFunction compare_models() is used for quickly computing
comparison metrics for a set of models on a given dataset.
The function takes the following arguments:
data the dataset that will be fitted to the
models
method the method to generate comparison metrics. It
can be the name of one of the built-in methods, or a user-defined
function.
… functions that take a data and return a fitted
serosv model.
It will then return a data.frame with the following
columns
label model identifier. Either user defined name or
index based on the order provided.
type type of model (a serosv model
class)
metrics depending on the method selected
Built-in method
serosv currently provide 2 built-in metrics generating
methods
"AIC/BIC" which returns fitted model’s AIC, BIC and
Log-likelihood (where applicable)
"CV" which split the data into “train” and “test”
set then return logloss and AUC from model’s prediction on the test
set
Sample usage
# ----- Return AIC, BIC -----
aic_bic_out <- compare_models(
data = data,
method = "AIC/BIC",
griffith = ~polynomial_model(.x, k=2),
penalized_spline = penalized_spline_model,
farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
local_polynomial = lp_model # expect to not return any values
) %>% suppressWarnings()
# ----- Return Cross-validation metrics -----
cv_out <- compare_models(
data,
method = "CV",
griffith = ~polynomial_model(.x, k=2),
penalized_spline = penalized_spline_model,
farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
local_polynomial = lp_model
) %>% suppressWarnings()
aic_bic_out## # A tibble: 4 × 8
## label type AIC BIC logLik df mod_out plots
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <list> <lis>
## 1 griffith polynomial_model 1353. 1363. -674. 2 <plynml_m> <gg>
## 2 penalized_spline penalized_spline_m… 1330. 1365. -658. 6.88 <pnlzd_s_> <gg>
## 3 farrington farrington_model 1337. NA -665. 3 <frrngtn_> <gg>
## 4 local_polynomial lp_model NA NA NA NA <lp_model> <gg>## # A tibble: 4 × 6
## label logloss auc type mod_out plots
## <chr> <dbl> <dbl> <chr> <list> <list>
## 1 griffith 277. 0.594 polynomial_model <plynml_m> <gg>
## 2 penalized_spline 197. 0.601 penalized_spline_model <pnlzd_s_> <gg>
## 3 farrington 185. 0.600 farrington_model <frrngtn_> <gg>
## 4 local_polynomial 237. 0.588 lp_model <lp_model> <gg>With aggregated data
# ----- Return AIC, BIC -----
aic_bic_out <- compare_models(
data = aggregated,
method = "AIC/BIC",
griffith = ~polynomial_model(.x, k=2),
penalized_spline = penalized_spline_model,
farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
local_polynomial = lp_model # expect to not return any values
) %>% suppressWarnings()
# ----- Return Cross-validation metrics (default with 4 folds) -----
cv_out <- compare_models(
data = aggregated,
method = "CV",
griffith = ~polynomial_model(.x, k=2),
penalized_spline = penalized_spline_model,
farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
local_polynomial = lp_model
) %>% suppressWarnings()
aic_bic_out## # A tibble: 4 × 8
## label type AIC BIC logLik df mod_out plots
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <list> <lis>
## 1 griffith polynomial_model 489. 496. -243. 2 <plynml_m> <gg>
## 2 penalized_spline penalized_spline_m… 467. 490. -227. 6.88 <pnlzd_s_> <gg>
## 3 farrington farrington_model 1337. NA -665. 3 <frrngtn_> <gg>
## 4 local_polynomial lp_model NA NA NA NA <lp_model> <gg>## # A tibble: 4 × 6
## label mse logloss type mod_out plots
## <chr> <dbl> <dbl> <chr> <list> <list>
## 1 griffith 0.217 178. polynomial_model <plynml_m> <gg>
## 2 penalized_spline 0.128 81.8 penalized_spline_model <pnlzd_s_> <gg>
## 3 farrington 0.117 65.5 farrington_model <frrngtn_> <gg>
## 4 local_polynomial 0.117 71.3 lp_model <lp_model> <gg>The users can also provide a custom function to generate the comparison metrics.
This function must accepts 2 parameters:
dat the input data
model_func a function that takes an input data and
returns a serosv model
And it must returns a data frame with 1 row where each column represents one metric.
Example:
The following implements holdout validation and returns MAE:
generate_mae <- function(dat, model_func){
n_train <- round(nrow(dat)*0.8)
train <- dat[1:n_train,]
test <- dat[n_train:nrow(dat),]
fit <- model_func(dat)
pred <- predict(fit, test[, 1, drop=FALSE])
# handle error differently depending on datatype
mae <- if(fit$datatype == "linelisting"){
sum(abs(test$status - pred), na.rm=TRUE)/nrow(test)
}else{
sum(abs(test$pos/test$tot - pred), na.rm=TRUE)/nrow(test)
}
data.frame(
mae = mae
)
}We can then run compare_models with the custom metrics
function
compare_models(
data = aggregated,
method = generate_mae,
griffith = ~polynomial_model(.x, k=2),
penalized_spline = penalized_spline_model,
farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
local_polynomial = lp_model
) %>% suppressWarnings()## label mae
## 1 griffith 1.9127037
## 2 penalized_spline 0.5810419
## 3 farrington 0.3710685
## 4 local_polynomial 0.3285755compare_models(
data = data,
method = generate_mae,
griffith = ~polynomial_model(.x, k=2),
penalized_spline = penalized_spline_model,
farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
local_polynomial = lp_model
) %>% suppressWarnings()## label mae
## 1 griffith 1.6883332
## 2 penalized_spline 0.5215807
## 3 farrington 0.4636040
## 4 local_polynomial 0.4536016These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.