Description Usage Arguments Value Methods Author(s) References See Also Examples

This function attempts to turn the given values into an `irt.pars`

object
that is used primarily with the `plink`

function.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
as.irt.pars(x, common, cat, poly.mod, dimensions = 1,
location = FALSE, grp.names, ...)
## S4 method for signature 'numeric', 'missing'
as.irt.pars(x, common, cat, poly.mod, dimensions, location,
grp.names, ...)
## S4 method for signature 'matrix', 'missing'
as.irt.pars(x, common, cat, poly.mod, dimensions, location,
grp.names, ...)
## S4 method for signature 'data.frame', 'missing'
as.irt.pars(x, common, cat, poly.mod, dimensions, location,
grp.names, ...)
## S4 method for signature 'list', 'missing'
as.irt.pars(x, common, cat, poly.mod, dimensions, location,
grp.names, ...)
## S4 method for signature 'sep.pars', 'missing'
as.irt.pars(x, common, cat, poly.mod, dimensions, location,
grp.names, ...)
## S4 method for signature 'list', 'matrix'
as.irt.pars(x, common, cat, poly.mod, dimensions, location,
grp.names, ...)
## S4 method for signature 'list', 'list'
as.irt.pars(x, common, cat, poly.mod, dimensions, location,
grp.names, ...)
``` |

`x` |
an |

`common` |
j x 2 matrix or list of matrices identifying the common items between
adjacent groups in |

`cat` |
vector or list of the number of response categories for each item in |

`poly.mod` |
a |

`dimensions` |
numeric vector identifying the number of modeled dimensions in each group. |

`location` |
logical vector identifying whether the parameters in |

`grp.names` |
character vector of group names |

`...` |
further arguments passed to or from other methods |

Returns an object of class `irt.pars`

- x = "numeric", common = "missing"
This method should only be used for the Rasch model where

`x`

is a vector of difficulty parameters (or parameters related to item difficulty in the multidimensional case). Under this method the slopes and lower asymptote values for all items will default to one and zero respectively. This is true for both the unidimensional and multidimensional case.- x = "matrix", common = "missing"
`x`

can include item parameters from multiple models. The general format for structuring`x`

is an additive column approach. That is, the left-most columns are typically for discrimination/slope parameters, the next column, if applicable, is for location parameters, the next set of columns is for difficulty/threshold/step/category parameters, and the final set of columns is for lower asymptote (guessing) parameters. When multiple models are included, or models with differing numbers of response categories, not all cells in`x`

may have data. In these instances, cells with no data should be`NA`

. The resulting matrix will be an n x k matrix for n items and k equal to the maximum number of columns (across all item response models). In essence, the combination of multiple models is equivalent to formatting the item parameters for each response model separately, stacking the matrices on top of one another and then filling in any missing cells with`NA`

s.- x = "data.frame", common = "missing"
See the method for x = "matrix"

- x = "list", common = "missing"
This method can include a list with one, two, or three elements. In general, these elements correspond to discrimination/slope parameters, difficulty/threshold/ step/category parameters, and lower asymptote (guessing) parameters, although this may not be the case depending on the supplied parameters. If a combination of models are used, the number of list elements should correspond with the maximum number of elements across models. For example, if the 3PL model (3 list elements) and nominal response model (2 list elements) are used, the list should include three elements. Within each list element, the parameters should be formatted as vectors or matrices (depending on the specific item response models). For the format of the matrices, see the method for x = "matrix" above and/or the methods corresponding to the specific response models. When location parameters are included, these values should be placed in the first column of the matrix of difficulty/threshold/step/category parameters (in the corresponding list element).

- x = "sep.pars", common = "missing"
`x`

is an object of class`sep.pars`

. The arguments`cat`

,`poly.mod`

, and`location`

do not need to be included.- x = "list", common = "matrix"
This method is intended for the creation of an object with only two groups. Each list element should conform to one of the formats listed above (i.e. a numeric vector, matrix/data.frame, list, or sep.pars object) or be an

`irt.pars`

object. The format of each list element does not need to be the same. That is, the first list element might be a`"sep.pars"`

object while the second element is a list of item parameters. If an object of class`irt.pars`

is included, the object can contain information for a single group or for multiple groups. If the`irt.pars`

object includes multiple groups,`common`

should identify the common items between the last group in`x[[1]]`

and the first group in`x[[2]]`

.For this method

`common`

is a j x 2 matrix where the first column identifies the common items for the first group (`x[[1]]`

). The second column in`common`

identifies the corresponding set of common items from the next list element. For example, if item 4 in group one (row 4 in`x[[1]]`

) is the same as item 6 in group two, the first row of`common`

would be`"4,6"`

.If the objects in

`x`

are of class`sep.pars`

or`irt.pars`

,`cat`

,`poly.mod`

, and`location`

do not need to be included.- x = "list", common = "list"
This method is intended for the creation of an object with more than two groups; however, if there are two groups

`common`

can be defined as a list with length one. Each list element should conform to one of the formats listed above (i.e. a numeric vector, matrix/data.frame, list, or sep.pars object) or be an`irt.pars`

object. The format of each list element does not need to be the same. That is, the first list element might be a`"sep.pars"`

object while the second element is a list of item parameters. If an object of class`irt.pars`

is included, the object can contain information for a single group or for multiple groups. The list elements in`x`

should be ordered such that adjacent elements correspond to adjacent groups. If an`"irt.pars"`

object is included with multiple groups, the list element following this object should contain information for a group that is adjacent to the last group in the`"irt.pars"`

objectFor this method

`common`

is a list of j x 2 matrices where the first column identifies the identifies the common items for the first group of two adjacent list elements in The second column in`common`

identifies the corresponding set of common items from the next list element in`x`

. For example, if`x`

contains only two list elements, a single set of common items links them together. If item 4 in group one (row 4 in slot`pars`

) is the same as item 6 in group two, the first row of`common`

would be`"4,6"`

.If all the objects in

`x`

are of class`sep.pars`

or`irt.pars`

, the arguments`cat`

,`poly.mod`

, and`location`

do not need to be included.

Jonathan P. Weeks weeksjp@gmail.com

Weeks, J. P. (2010)
**plink**: An R package for linking mixed-format tests using IRT-based methods.
*Journal of Statistical Software*, **35(12)**, 1–33.
URL http://www.jstatsoft.org/v35/i12/

`irt.pars`

, `as.poly.mod`

, `poly.mod`

,
`sep.pars`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | ```
# Create object for three dichotomous (1PL) items with difficulties
# equal to -1, 0, 1
x <- as.irt.pars(c(-1,0,1))
# Create object for three dichotomous (3PL) items and two polytomous
# (gpcm) items without a location parameter
# (use signature matrix, missing)
dichot <- matrix(c(1.2, .8, .9, 2.3, -1.1, -.2, .24, .19, .13),3,3)
poly <- matrix(c(.64, -1.8, -.73, .45, NA, .88, .06, 1.4, 1.9, 2.6),
2,5,byrow=TRUE)
pars <- rbind(cbind(dichot,matrix(NA,3,2)),poly)
cat <- c(2,2,2,4,5)
pm <- as.poly.mod(5, c("drm","gpcm"), list(1:3,4:5))
x <- as.irt.pars(pars, cat=cat, poly.mod=pm)
summary(x)
# Create object for three dichotomous (3PL) items and two polytomous
# (gpcm) items without a location parameter
# (use signature list, missing)
a <- c(1.2, .8, .9, .64, .88)
b <- matrix(c(
2.3, rep(NA,3),
-1.1, rep(NA,3),
-.2, rep(NA,3),
-1.8, -.73, .45, NA,
.06, 1.4, 1.9, 2.6),5,4,byrow=TRUE)
c <- c(1.4, 1.9, 2.6, NA, NA)
pars <- list(a,b,c)
cat <- c(2,2,2,4,5)
pm <- as.poly.mod(5, c("drm","gpcm"), list(1:3,4:5))
x <- as.irt.pars(pars, cat=cat, poly.mod=pm)
summary(x)
# Create object for three dichotomous (3PL) items, four polytomous items,
# two gpcm items and two nrm items. Include a location parameter for the
# gpcm items (use signature list, missing)
a <- matrix(c(
1.2, rep(NA,4),
.8, rep(NA,4),
.9, rep(NA,4),
.64, rep(NA,4),
.88, rep(NA,4),
.905, .522, -.469, -.959, NA,
.828, .375, -.357, -.079, -.817),7,5,byrow=TRUE)
b <- matrix(c(
2.3, rep(NA,4),
-1.1, rep(NA,4),
-.2, rep(NA,4),
-.69, -1.11, -.04, 1.14, NA,
1.49, -1.43, -.09, .41, 1.11,
.126, -.206, -.257, .336, NA,
.565, .865, -1.186, -1.199, .993),7,5,byrow=TRUE)
c <- c(.14, .19, .26, rep(NA,4))
pars <- list(a,b,c)
cat <- c(2,2,2,4,5,4,5)
pm <- as.poly.mod(7, c("drm","gpcm","nrm"), list(1:3,4:5,6:7))
x <- as.irt.pars(pars, cat=cat, poly.mod=pm, location=TRUE)
summary(x, TRUE)
# Create object with two groups (all dichotomous items)
pm <- as.poly.mod(36)
x <- as.irt.pars(KB04$pars, KB04$common, cat=list(rep(2,36),rep(2,36)),
list(pm,pm), grp.names=c("form.x","form.y"))
summary(x, TRUE)
# Create object with six groups (all dichotomous items)
pars <- TK07$pars
common <- TK07$common
cat <- list(rep(2,26),rep(2,34),rep(2,37),rep(2,40),rep(2,41),rep(2,43))
pm1 <- as.poly.mod(26)
pm2 <- as.poly.mod(34)
pm3 <- as.poly.mod(37)
pm4 <- as.poly.mod(40)
pm5 <- as.poly.mod(41)
pm6 <- as.poly.mod(43)
pm <- list(pm1, pm2, pm3, pm4, pm5, pm6)
x <- as.irt.pars(pars, common, cat, pm,
grp.names=paste("grade",3:8,sep=""))
# Create an object with two groups using mixed-format items and
# a mixed placement of common items. This example uses the dgn dataset.
pm1=as.poly.mod(55,c("drm","gpcm","nrm"),dgn$items$group1)
pm2=as.poly.mod(55,c("drm","gpcm","nrm"),dgn$items$group2)
x=as.irt.pars(dgn$pars,dgn$common,dgn$cat,list(pm1,pm2))
summary(x, TRUE)
``` |

```
Loading required package: lattice
Total Number of Items: 5
Number of Dichotomous Items: 3
Dichotomous Model: 3PL
Number of Polytomous Items: 2
Polytomous Model: Generalized Partial Credit Model
Total Number of Items: 5
Number of Dichotomous Items: 3
Dichotomous Model: 3PL
Number of Polytomous Items: 2
Polytomous Model: Generalized Partial Credit Model
Total Number of Items: 7
Number of Dichotomous Items: 3
Dichotomous Model: 3PL
Number of Polytomous Items: 4
Generalized Partial Credit Model
Number of Generalized Partial Credit Model Items: 2
Nominal Response Model
Number of Nominal Response Model Items: 2
-------- form.x --------
Total Number of Items: 36
Number of Dichotomous Items: 36
Dichotomous Model: 3PL
Number of Polytomous Items: 0
-------- form.y --------
Total Number of Items: 36
Number of Dichotomous Items: 36
Dichotomous Model: 3PL
Number of Polytomous Items: 0
-------- group1 --------
Total Number of Items: 55
Number of Dichotomous Items: 30
Dichotomous Model: 3PL
Number of Polytomous Items: 25
Generalized Partial Credit Model
Number of Generalized Partial Credit Model Items: 10
Nominal Response Model
Number of Nominal Response Model Items: 15
-------- group2 --------
Total Number of Items: 55
Number of Dichotomous Items: 30
Dichotomous Model: 3PL
Number of Polytomous Items: 25
Generalized Partial Credit Model
Number of Generalized Partial Credit Model Items: 10
Nominal Response Model
Number of Nominal Response Model Items: 15
```

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