Description Usage Arguments Value Author(s) See Also Examples
Modeling results can be converted to tabular format and manipulated using
dplyr and other Hadleyverse packages. This is accomplished by a class
specific select_
function that differs somewhat in syntax
from the default select_
.
1 2 | ## S3 method for class 'list'
select_(.data, ..., .dots)
|
.data |
Modeling results, as returned by |
... |
Not used, kept for consistency with |
.dots |
Indices to select on each level of The names of the In summary the
|
A data.frame
in long format.
Christofer Bäcklin
subtree
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | # Produce some results
x <- iris[-5]
y <- iris$Species
names(y) <- sprintf("orchid%03i", seq_along(y))
cv <- resample("crossvalidation", y, nfold=3, nrepeat=2)
procedures <- list(nsc = modeling_procedure("pamr"),
rf = modeling_procedure("randomForest"))
result <- evaluate(procedures, x, y, resample=cv)
# Get the foldwise error for the NSC method
result %>% select(fold = TRUE, "nsc", error = "error")
# Compare both methods
require(tidyr)
result %>%
select(fold = TRUE, method = TRUE, error = "error") %>%
spread(method, error)
require(dplyr)
result %>%
select(fold = TRUE, method = TRUE, error = "error") %>%
group_by(method) %>% summarize(mean_error = mean(error))
# Investigate the variability in estimated class 2 probability across folds
result %>%
select(fold = cv, "nsc", "prediction", probability = function(x) x$probability[,2]) %>%
spread(fold, probability)
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