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Results tagged with r
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user 19676
Use this tag for any *on-topic* question that (a) involves `R` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `R`.
36
votes
2
answers
51k
views
Relative importance of a set of predictors in a random forests classification in R
I'd like to determine the relative importance of sets of variables toward a randomForest classification model in R. …
- 820
4
votes
1
answer
1k
views
Integer regression coefficients in R
I'd like to fit integer coefficients, e.g. summing to 10, to a regression equation. The absolute values of the coefficients (i.e. predicted y) aren't important, I just want to retain the appropriate r …
- 820
4
votes
Variablity in cv.glmnet results
Alice's answer works well in most cases, but sometimes errors out due to cv.glmnet$lambda sometimes returning results of different length, e.g.:
Error in rownames<-(tmp, value = c(0.135739830284 …
- 820
2
votes
Using information on both sides of a 'gap' in time series data for imputation
na.interp from the forecast package performs well (similar to na.StructTS as @g_grothendieck recommends, bit faster though) in this analysis of time series interpolation methods.
- 820
1
vote
0
answers
143
views
Computing precision of continuous classifier with ordinal outcome
Hand & Till's multi-outcome AUC (R package) measure is helpful context, but insufficient since the raters aren't predicting each individual outcome. …
- 820
1
vote
Accepted
Integer regression coefficients in R
We can apply the answer to
Round vector of numerics to integer while preserving their sum:
smart.round <- function(x) {
y <- floor(x)
indices <- tail(order(x-y), round(sum(x)) - sum(y))
y[indi …
- 820
1
vote
Methods in R or Python to perform feature selection in unsupervised learning
Principal Feature Analysis looks to be a solution to unsupervised feature selection. It's described in this paper.
- 820
0
votes
Methods in R or Python to perform feature selection in unsupervised learning
The nsprcomp R package provides methods for sparse Principal Component Analysis, which could suit your needs. …
- 820