# monoreg.rowwise: Monotone Regression for Rows or Columns in a Matrix In sirt: Supplementary Item Response Theory Models

## Description

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

## Usage

 ```1 2 3``` ```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 the `monoreg` function from the fdrtool package.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```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) ```