A-priori size regression for missing data estimation

Description

Estimates missing data using regression on a designated size variable. Any values of the size variable missing are estimated with the variable best correlated with size.

Usage

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est.reg(x, col_indep)

Arguments

x

A n X m matrix of morphometric data with n specimens and m variables, containing some percentage of missing values input as NA

col_indep

The number of the column in which the independant size variable is stored. This column will be used to estimate missing values in the other columns.

Value

Returns a n X m matrix containing both the original morphometric values as well as estimates for all previously missing values.

Author(s)

J. Arbour and C. Brown

References

Brown, C., Arbour, J. and Jackson, D. 2012. Testing of the Effect of Missing Data Estimation and Distribution in Morphometric Multivariate Data Analyses. Systematic Biology 61(6):941-954.

See Also

best.reg

Examples

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data(crocs)

## remove 30% of data points
croc.miss<-missing.data(crocs,0.3)
croc.miss

## assume col 1 is the size variable
croc.new<-est.reg(croc.miss,1)
croc.new