nrsq() calculates the coefficient of determination, or
R-squared value, between an independent variable
x and a dependent
y. Note that when
na.rm = FALSE (default), missing
x will be replaced with
1 and a flag identifying the
missings will be included as an additive term in the model. Otherwise, only
non-missing records are fit.
numeric vector (independent variable)
numeric vector (dependent variable)
logical value indicating whether missing values of
A numeric value with class "
mt_nrsq" that indicates the
proportion of the variance in the dependent variable that is predictable
from the independent variable.
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# basic example without missing values nrsq(mtcars$hp, mtcars$mpg) # add some missing values x <- ifelse(runif(length(mtcars$hp)) < 0.30, NA, mtcars$hp) # include missings with adjustment nrsq(x, mtcars$mpg, na.rm = FALSE) # exclude missings entirely nrsq(x, mtcars$mpg, na.rm = TRUE) # evaluate a whole data frame of predictors temp <- lapply(mtcars[c("disp", "hp", "wt", "qsec")], nrsq, y = mtcars$mpg) data.frame( VarName = names(temp), RSquared = as.numeric(unlist(temp)), stringsAsFactors = FALSE )
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