Analyze  R Documentation 
dataList
.
The test or rating scale data have already been processed by function make_dataList
or
or other code to produce the list object dataList
. The user defines a list vector
ParameterList
which stores results from a set of cycles of estimating surprisal curves
followed by estimating optimal score index values for each examinee or respondent. These
score index values are within the interval [0,100]. The number of analysis cycles is the
length of the parmList
list vector.
Analyze(index, indexQnt, dataList, NumDensBasis=7, ncycle=10, itdisp=FALSE,
verbose=FALSE)
index 
A vector of 
indexQnt 
A vector of length 
dataList 
A list that contains the objects needed to analyse the test or rating scale with the following fields:

NumDensBasis 
The number of basis functions for representing the score density. 
ncycle 
The number of cycles executed by function 
itdisp 
If TRUE, the progress of the iterations within each cycle for estimating index are reported. 
verbose 
If TRUE, the stages of analysis within each cycle for estimating index are reported. 
The cycling process is described in detail in the references, and displayed in R code
in the vignette SweSATQuantitativeAnalysis
.
The list vector parmList
where each member is a named list object containing
the results of an analysis cycle. These results are:
index: 
The optimal estimates of the score index values for the
examinees/respondents. This is a vector of length 
indexQnt: 
A vector of length 2*nbin+1 containing bin boundaries alternating with bin edges. 
SfdList: 
A list vector containing results from the estimation of surprisal
curves. The list vector is of length 
meanF: 
For each person, the mean of the optimal fitting function values. 
binctr: 
A vector of length 
bdry: 
A vector of length 
freq: 
A vector of length 
pdf_fd 
Functional probability curves 
logdensfd: 
A functional data object defining the estimate of the log of the probability density function for the distribution of the score index values. 
C: 
The normalizing value for probability density functions. A density value is computed by dividing the exponential of the log density value by this constant. 
denscdf: 
The values over a fine mesh of the cumulative probability
distribution function. These values start at 0 and end with 1 and are increasing.
Ties are often found at the upper boundary, so that using these values for
interpolation purposes may require using the vector 
indcdf 
Equally spaced index values to match the number in denscdf. 
Qvec 
Locations of the marker percents. 
index 
The positions of each test taker on the score index continuum. 
Fval: 
A vector of length N containing the values of the negative log likelihood fitting criterion. 
DFval: 
A vector of length N containing the values of the first derivative of the negative log likelihood fitting criterion. 
D2Fval: 
A vector of length N containing the values of the second derivative of the negative log likelihood fitting criterion. 
active: 
A vector of length N of the activity status of the values of index. If convergence was not achieved, the value is TRUE, otherwise FALSE. 
infoSurp: 
The length of the space curve defined by the surprisal curves. 
Juan Li and James Ramsay
Ramsay, J. O., Li J. and Wiberg, M. (2020) Full information optimal scoring. Journal of Educational and Behavioral Statistics, 45, 297315.
Ramsay, J. O., Li J. and Wiberg, M. (2020) Better rating scale scores with informationbased psychometrics. Psych, 2, 347360.
make_dataList,
TG_analysis,
index_distn,
index2info,
index_fun,
Sbinsmth
## Not run:
# Example 1: Input choice data and key for the short version of the
# SweSAT quantitative multiple choice test with 24 items and 1000 examinees
# input the choice data as 1000 strings of length 24
# setup the input data list object
dataList < Quant_13B_problem_dataList
# define the initial examinee indices and bin locations
index < dataList$percntrnk
indexQnt < dataList$indexQnt
# Set the number of cycles (default 10 but here 5)
ncycle < 5
parmListvec < Analyze(index, indexQnt, ncycle=ncycle, dataList,
verbose=TRUE)
# two column matrix containing the mean fit and arclength values
# for each cycle
HALsave < matrix(0,ncycle,2)
for (icycle in 1:ncycle) {
HALsave[icycle,1] < parmListvec[[icycle]]$meanF
HALsave[icycle,2] < parmListvec[[icycle]]$infoSurp
}
# plot the progress over the cycles of mean fit and arc length
par(mfrow=c(2,1))
plot(1:ncycle, HALsave[,1], type="b", lwd=2,
xlab="Cycle Number",ylab="Mean H")
plot(1:ncycle, HALsave[,2], type="b", lwd=2,
xlab="Cycle Number", ylab="Arc Length")
## End(Not run)
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