svdNorm | R Documentation |
Compute theta surrogates by calculating the normalized left singular vector of a (mean-centered) data matrix.
svdNorm(data)
data |
N(subjects)-by-p(items) matrix of 0/1 item response data. |
the normalized left singular vector of the mean centered data
matrix. |
|
Niels Waller
NSubj <- 2000
## example item parameters for sample data: k=1 FMP
b <- matrix(c(
#b0 b1 b2 b3 b4 b5 b6 b7 k
1.675, 1.974, -0.068, 0.053, 0, 0, 0, 0, 1,
1.550, 1.805, -0.230, 0.032, 0, 0, 0, 0, 1,
1.282, 1.063, -0.103, 0.003, 0, 0, 0, 0, 1,
0.704, 1.376, -0.107, 0.040, 0, 0, 0, 0, 1,
1.417, 1.413, 0.021, 0.000, 0, 0, 0, 0, 1,
-0.008, 1.349, -0.195, 0.144, 0, 0, 0, 0, 1,
0.512, 1.538, -0.089, 0.082, 0, 0, 0, 0, 1,
0.122, 0.601, -0.082, 0.119, 0, 0, 0, 0, 1,
1.801, 1.211, 0.015, 0.000, 0, 0, 0, 0, 1,
-0.207, 1.191, 0.066, 0.033, 0, 0, 0, 0, 1,
-0.215, 1.291, -0.087, 0.029, 0, 0, 0, 0, 1,
0.259, 0.875, 0.177, 0.072, 0, 0, 0, 0, 1,
-0.423, 0.942, 0.064, 0.094, 0, 0, 0, 0, 1,
0.113, 0.795, 0.124, 0.110, 0, 0, 0, 0, 1,
1.030, 1.525, 0.200, 0.076, 0, 0, 0, 0, 1,
0.140, 1.209, 0.082, 0.148, 0, 0, 0, 0, 1,
0.429, 1.480, -0.008, 0.061, 0, 0, 0, 0, 1,
0.089, 0.785, -0.065, 0.018, 0, 0, 0, 0, 1,
-0.516, 1.013, 0.016, 0.023, 0, 0, 0, 0, 1,
0.143, 1.315, -0.011, 0.136, 0, 0, 0, 0, 1,
0.347, 0.733, -0.121, 0.041, 0, 0, 0, 0, 1,
-0.074, 0.869, 0.013, 0.026, 0, 0, 0, 0, 1,
0.630, 1.484, -0.001, 0.000, 0, 0, 0, 0, 1),
nrow=23, ncol=9, byrow=TRUE)
# generate data using the above item paramters
data<-genFMPData(NSubj=NSubj, bParam=b, seed=345)$data
# compute (initial) surrogate theta values from
# the normed left singular vector of the centered
# data matrix
thetaInit<-svdNorm(data)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.