plotICC {eRm} | R Documentation |

Plot functions for visualizing the item characteristic curves

```
## S3 method for class 'Rm'
plotICC(object, item.subset = "all", empICC = NULL, empCI = NULL,
mplot = NULL, xlim = c(-4, 4), ylim = c(0, 1),
xlab = "Latent Dimension", ylab = "Probability to Solve", main=NULL,
col = NULL, lty = 1, legpos = "left", ask = TRUE, ...)
## S3 method for class 'dRm'
plotjointICC(object, item.subset = "all", legend = TRUE,
xlim = c(-4, 4), ylim = c(0, 1), xlab = "Latent Dimension",
ylab = "Probability to Solve", lty = 1, legpos = "topleft",
main="ICC plot",col=NULL,...)
```

`object` |
object of class |

`item.subset` |
Subset of items to be plotted. Either a numeric vector indicating
the column in |

`empICC` |
Plotting the empirical ICCs for objects of class |

`empCI` |
Plotting confidence intervals for the the empirical ICCs.
If |

`mplot` |
if |

`xlab` |
Label of the x-axis. |

`ylab` |
Label of the y-axis. |

`xlim` |
Range of person parameters. |

`ylim` |
Range for probability to solve. |

`legend` |
If |

`col` |
If not specified or |

`lty` |
Line type. |

`main` |
Title of the plot. |

`legpos` |
Position of the legend with possible values |

`ask` |
If |

`...` |
Additional plot parameters. |

Empirical ICCs for objects of class `dRm`

can be plotted using the option `empICC`

, a
list where the first element specifies the type of calculation of the empirical values.
If `empICC=list("raw", other specifications)`

relative frequencies of the positive responses are calculated for each rawscore group and plotted
at the position of the corresponding person parameter. The other options use the default versions
of various smoothers: `"tukey"`

(see `smooth`

), `"loess"`

(see `loess`

),
and `"kernel"`

(see `ksmooth`

). For `"loess"`

and `"kernel"`

a further
element, `smooth`

,
may be specified to control the span (default is 0.75) or the bandwith (default is 0.5),
respectively. For example, the specification could be `empirical = list("loess", smooth=0.9)`

or `empirical = list("kernel",smooth=2)`

.
Higher values result in smoother estimates of the empirical ICCs.

The optional confidence intervals are obtained by a procedure first given in
Clopper and Pearson (1934) based on the beta distribution (see `binom.test`

).

For most of the plot options see `plot`

and `par`

.

Patrick Mair, Reinhold Hatzinger

```
## Not run:
# Rating scale model, ICC plot for all items
rsm.res <- RSM(rsmdat)
thresholds(rsm.res)
plotICC(rsm.res)
# now items 1 to 4 in one figure without legends
plotICC(rsm.res, item.subset = 1:4, mplot = TRUE, legpos = FALSE)
# Rasch model for items 1 to 8 from raschdat1
# empirical ICCs displaying relative frequencies (default settings)
rm8.res <- RM(raschdat1[,1:8])
plotICC(rm8.res, empICC=list("raw"))
# the same but using different plotting styles
plotICC(rm8.res, empICC=list("raw",type="b",col="blue",lty="dotted"))
# kernel-smoothed empirical ICCs using bandwidth = 2
plotICC(rm8.res, empICC = list("kernel",smooth=3))
# raw empirical ICCs with confidence intervals
# displaying only items 2,3,7,8
plotICC(rm8.res, item.subset=c(2,3,7,8), empICC=list("raw"), empCI=list())
# Joint ICC plot for items 2, 6, 8, and 15 for a Rasch model
res <- RM(raschdat1)
plotjointICC(res, item.subset = c(2,6,8,15), legpos = "left")
## End(Not run)
```

[Package *eRm* version 1.0-2 Index]