Monotone Regression for Rows or Columns in a Matrix
Monotone (isotone) regression for rows (monoreg.rowwise) or
columns (monoreg.colwise) in a matrix.
monoreg.rowwise(yM, wM) monoreg.colwise(yM, wM)
yM |
Matrix with dependent variable for the regression. Values are assumed to be sorted. |
wM |
Matrix with weights for every entry in the |
Matrix with fitted values
This function is used for fitting the ISOP model
(see isop.dich).
Alexander Robitzsch
The monoreg function from the fdrtool
package is simply extended to handle matrix input.
See also the monoreg function from the fdrtool
package.
y <- c(22.5, 23.33, 20.83, 24.25 )
w <- c( 3,3,3,2)
# define matrix input
yM <- matrix( 0, nrow=2, ncol=4 )
wM <- yM
yM[1,] <- yM[2,] <- y
wM[1,] <- w
wM[2,] <- c(1,3,4, 3 )
# fit rowwise monotone regression
monoreg.rowwise( yM, wM )
# compare results with monoreg function from fdrtool package
## Not run:
miceadds::library_install("fdrtool")
fdrtool::monoreg(x=yM[1,], w=wM[1,])$yf
fdrtool::monoreg(x=yM[2,], w=wM[2,])$yf
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.