as.weight {plink} | R Documentation |
This function facilitates the creation of weights for a set of theta values for
use in the function plink
as.weight(theta1 = NULL, weight1 = NULL, theta2 = NULL, weight2 = NULL, normal.wt = FALSE)
theta1 |
vector of theta values for the set of TO item parameters
(i.e. the item parameters that will not be transformed) |
weight1 |
vector of weights corresponding to theta1 . They must
sum to one. |
theta2 |
vector of theta values for the set of FROM item parameters
(i.e. the item parameters that will be transformed) |
weight2 |
vector of weights corresponding to theta2 . They must
sum to one. |
normal.wt |
if TRUE and weight1 is NULL , the weights for
theta1 and theta2 will be computed to correspond to the
densities from a normal distribution. |
If no theta values are supplied, the values for theta1
and theta2
will
be equal interval values ranging from -4 to 4 with an increment of 0.05. If values are
only supplied for theta1
, the values for theta2
will equal those of
theta1
. If values are supplied for theta1
and theta2
, the length
of the two vectors must be equal.
If no weights are supplied, the values for weight1
and weight2
will
be equal to 1/n for n theta values. If values are only supplied for weight1
, the
values for weight2
will equal those of weight1
. If values are supplied
for weight1
and/or weight2
, the length of the vector(s) should be equal
to the length of the theta vector(s). If values are supplied for weight1
or
weight2
, normal.wt
will be ignored.
See Kolen & Brennan (2004) for more information on calibration weights.
Returns a list of length two. The first list element is an n x 2 matrix of theta values
corresponding to theta1
and theta2
. The second list element is an
n x 2 matrix of weights corresponding to weight1
and weight2
.
When normal.wt
equals TRUE
, theta1
and theta2
are
standardized to determine normal density weights
Jonathan Weeks weeksjp@gmail.com
Kolen, M. J., & Brennan, R. L. (2004) Test Equating, Scaling, and Linking. New York: Springer
# Create a set of default theta values and weights wt <- as.weight() # Create weights for a set of random normal theta values wt <- as.weight(rnorm(100)) # Create an object with equal interval theta values and normal density weights wt <- as.weight(normal.wt=TRUE) # Create an object with random normal theta values and normal density weights wt <- as.weight(rnorm(100),normal.wt=TRUE)