Jackknife Covariance and Missing Values


Missing column

We create some data and replace one column with NA.

data <- matrix(rnorm(120), ncol = 10)
data[, 3] <- NA

The covariance, with the implicit use = 'everything' will give us a “cross” of NA in the covariance matrix.


The jackknife covariance does the same thing.


Missing row

When we have some NA values in a row, we have a conceptual problem with the jackknife as the width of the jackknife distribution is linked to the number of measurements.

data <- matrix(rnorm(120), ncol = 10)
data[2, ] <- NA

Also here we get the same behavior by default:


When we use complete, we get the same thing as just dropping the NA rows.

cov(data, use = 'complete')
all(cov(data, use = 'complete') == cov(data[complete.cases(data), ]))

With our jackknife function we get a failure, which should not happen!

jackknife_cov(data, na.rm = TRUE)

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hadron documentation built on Jan. 13, 2021, 8 a.m.