monoreg.rowwise: Monotone Regression for Rows or Columns in a Matrix

In sirt: Supplementary Item Response Theory Models

Monotone Regression for Rows or Columns in a Matrix

Description

Monotone (isotone) regression for rows (monoreg.rowwise) or columns (monoreg.colwise) in a matrix.

Usage

monoreg.rowwise(yM, wM)

monoreg.colwise(yM, wM)

Arguments

yM	Matrix with dependent variable for the regression. Values are assumed to be sorted.
wM	Matrix with weights for every entry in the yM matrix.

Value

Matrix with fitted values

Note

This function is used for fitting the ISOP model (see isop.dich).

Author(s)

Alexander Robitzsch

The monoreg function from the fdrtool package is simply extended to handle matrix input.

See Also

See also the monoreg function from the fdrtool package.

Examples

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)

sirt documentation built on May 29, 2024, 8:43 a.m.