sa_sizeChange: Sample size planning for tests in context of measurement of...
In tcl: Testing in Conditional Likelihood Context
View source: R/sa_sizeChange.R
sa_sizeChange R Documentation
Sample size planning for tests in context of measurement of change using LLTM
Description
Returns sample size for Wald (W), likelihood ratio (LR), Rao score (RS)
and gradient (GR) test given probabilities of errors of first and second kinds \alpha and \beta
as well as a deviation from the hypothesis to be tested. The hypothesis to be tested states that
the shift parameter quantifying the constant change for all items between time points 1 and 2
equals 0. The alternative states that the shift parameter is not equal to 0. It is assumed that the same
items are presented at both time points. See function change_test.
Usage
sa_sizeChange(alpha = 0.05, beta = 0.05, eta, persons = rnorm(10^6))
Arguments
alpha
Probability of error of first kind.
beta
Probability of error of second kind.
eta
A vector of eta parameters of the LLTM. The last element represents the constant change or shift for all items
between time points 1 and 2. The other elements of the vector are the item parameters at time point 1. A choice of the eta
parameters constitutes a scenario of deviation from the hypothesis of no change.
persons
A vector of person parameters (drawn from a specified distribution). By default 10^6 parameters
are drawn at random from the standard normal distribution. The larger this number the more accurate are the computations.
See Details.
Details
In general, the sample size is determined from the assumption that the approximate distributions
of the four test statistics are from the family of noncentral \chi^2 distributions with df = 1
and noncentrality parameter \lambda. The latter is, inter alia, a function of the sample size. Hence,
the sample size can be determined from the condition \lambda = \lambda_0, where \lambda_0 is
a predetermined constant which depends on the probabilities of the errors of the first and second kinds
\alpha and \beta (or power). More details about the distributions of the test statistics
and the relationship between \lambda, power, and sample size can be found in Draxler and Alexandrowicz (2015).
In particular, the determination of \lambda and the sample size, respectively, is based on a simple Monte
Carlo approach. As regards the concept of sample size a distinction between informative and total
sample size has to be made. In the conditional maximum likelihood context, the responses of
persons with minimum or maximum person score are completely uninformative. They do not contribute
to the value of the test statistic. Thus, the informative sample size does not include these persons.
The total sample size is composed of all persons. The Monte Carlo approach used in the present
problem to determine \lambda and informative (and total) sample size can briefly be described as follows.
Data (responses of a large number of persons to a number of items presented at two time points) are
generated given a user-specified scenario of a deviation from the hypothesis to be tested. The
hypothesis to be tested assumes no change between time points 1 and 2. A scenario of a deviation
is given by a choice of the item parameters at time point 1 and the shift parameter, i.e., the
LLTM eta parameters, as well as the person parameters (to be drawn randomly from a specified distribution).
The shift parameter represents a constant change of all item parameters from time point 1 to time point 2.
A test statistic T (Wald, LR, score, or gradient) is computed from the simulated data. The observed
value t of the test statistic is then divided by the informative sample size n_{infsim} observed
in the simulated data. This yields the so-called global deviation e = t / n_{infsim}, i.e.,
the chosen scenario of a deviation from the hypothesis to be tested being represented by a
single number. Let the informative sample size sought be denoted by n_{inf} (thus, this is not
the informative sample size observed in the sim. data). The noncentrality parameter \lambda can
be expressed by the product n_{inf} * e. Then, it follows from the condition \lambda = \lambda_0 that
n_{inf} * e = \lambda_0
and
n_{inf} = \lambda_0 / e.
Note that the sample of size n_{inf} is assumed to be composed only of persons with informative person scores, where
the relative frequency distribution of these informative scores is considered to be equal to the
observed relative frequency distribution of the informative scores in the simulated data. The total sample size
n_{total} is then obtained from the relation n_{inf} = n_{total} * pr, where pr is the proportion
or relative frequency of persons observed in the simulated data with a minimum or maximum score. Basing
the tests given a level \alpha on an informative sample of size n_{inf} the probability of rejecting
the hypothesis to be tested will be at least 1 - \beta if the true global deviation \geq e.
Note that in this approach the data have to be generated only once. There are no replications
needed. Thus, the procedure is computationally not very time-consuming.
Since e is determined from the value of the test statistic observed in the simulated data it has
to be treated as a realized value of a random variable E. Consequently, n_{inf} is also a
realization of a random variable N_{inf}. Thus, the (realized) value n_{inf} need not be
equal to the exact value of the informative sample size that follows from the user-specified
(predetermined) \alpha, \beta, and scenario of a deviation from the hypothesis to be
tested, i.e., the selected item parameters and shift parameter used for the simulation of the data.
If the CML estimates of these parameters computed from the simulated data are close to the
predetermined parameters n_{inf} will be close to the exact value. This will generally be the
case if the number of person parameters used for simulating the data is large, e.g.,
10^5 or even 10^6 persons. In such cases, the possible random error of the computation procedure
of n_{inf} based on the sim. data may not be of practical relevance any more. That is why a
large number (of persons for the simulation process) is generally recommended.
For theoretical reasons, the random error involved in computing n_{inf} can be pretty well approximated.
A suitable approach is the well-known delta method. Basically, it is a Taylor polynomial of first order,
i.e., a linear approximation of a function. According to it the variance of a function of a random
variable can be linearly approximated by multiplying the variance of this random variable with the square
of the first derivative of the respective function. In the present problem, the variance of the test
statistic T is (approximately) given by the variance of a noncentral \chi^2 distribution. Thus, Var(T) = 2 (df + 2 \lambda),
with df = 1 and \lambda = t. Since the global deviation
e = (1 / n_{infsim}) * t it follows for the variance of the corresponding random variable E that
Var(E) = (1 / n_{infsim})^2 * Var(T). Since n_{inf} = f(e) = \lambda_0 / e one obtains by the
delta method (for the variance of the corresponding random variable N_{inf})
Var(N_{inf}) = Var(E) * (f'(e))^2,
where f'(e) = - \lambda_0 / e^2 is the derivative of f(e). The square root of Var(N_{inf})
is then used to quantify the random error of the suggested Monte Carlo computation procedure. It is called
Monte Carlo error of informative sample size.
Value
A list results.
informative sample size
Informative sample size for each test, omitting persons with min. and max score.
MC error of sample size
Monte Carlo error of sample size computation for each test.
deviation
Shift parameter estimated from the simulated data representing the constant shift of item
parameters between time points 1 and 2.
person score distribution
Relative frequencies of person scores observed in simulated data. Uninformative scores,
i.e., minimum and maximum score, are omitted.
Note that the person score distribution does also have an influence on the sample size.
degrees of freedom
Degrees of freedom df.
noncentrality parameter
Noncentrality parameter \lambda of \chi^2 distribution from which sample size is determined.
total sample size
Total sample size for each test. See Details.
call
The matched call.
References
Draxler, C., & Alexandrowicz, R. W. (2015). Sample size determination within the scope of conditional
maximum likelihood estimation with special focus on testing the Rasch model. Psychometrika, 80(4), 897-919.
Fischer, G. H. (1995). The Linear Logistic Test Model. In G. H. Fischer & I. W. Molenaar (Eds.),
Rasch models: Foundations, Recent Developments, and Applications (pp. 131-155). New York: Springer.
Fischer, G. H. (1983). Logistic Latent Trait Models with Linear Constraints. Psychometrika, 48(1), 3-26.
See Also
powerChange, and post_hocChange.
Examples
## Not run:
# Numerical example 4 items presented twice, thus 8 virtual items
# eta Parameter, first 4 are nuisance
# (easiness parameters of the 4 items at time point 1),
# last one is the shift parameter
eta <- c(-2,-1,1,2,0.5)
res <- sa_sizeChange(alpha = 0.05, beta = 0.05, eta=eta, persons = rnorm(10^6))
# > res
# $`informative sample size`
# W LR RS GR
# 177 174 175 173
#
# $`MC error of sample size`
# W LR RS GR
# 1.321 1.287 1.299 1.276
#
# $`deviation (estimate of shift parameter)`
# [1] 0.501
#
# $`person score distribution`
#
# 1 2 3 4 5 6 7
# 0.034 0.094 0.181 0.249 0.227 0.147 0.068
#
# $`degrees of freedom`
# [1] 1
#
# $`noncentrality parameter`
# [1] 12.995
#
# $`total sample size`
# W LR RS GR
# 182 179 180 178
#
# $call
# sa_sizeChange(alpha = 0.05, beta = 0.05, eta = eta, persons = rnorm(10^6))
## End(Not run)
tcl documentation built on May 3, 2023, 1:17 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
View source: R/sa_sizeChange.R
| sa_sizeChange | R Documentation |
Sample size planning for tests in context of measurement of change using LLTM
Description
Returns sample size for Wald (W), likelihood ratio (LR), Rao score (RS)
and gradient (GR) test given probabilities of errors of first and second kinds \alpha and \beta
as well as a deviation from the hypothesis to be tested. The hypothesis to be tested states that
the shift parameter quantifying the constant change for all items between time points 1 and 2
equals 0. The alternative states that the shift parameter is not equal to 0. It is assumed that the same
items are presented at both time points. See function change_test.
Usage
sa_sizeChange(alpha = 0.05, beta = 0.05, eta, persons = rnorm(10^6))
Arguments
alpha |
Probability of error of first kind. |
beta |
Probability of error of second kind. |
eta |
A vector of eta parameters of the LLTM. The last element represents the constant change or shift for all items between time points 1 and 2. The other elements of the vector are the item parameters at time point 1. A choice of the eta parameters constitutes a scenario of deviation from the hypothesis of no change. |
persons |
A vector of person parameters (drawn from a specified distribution). By default |
Details
In general, the sample size is determined from the assumption that the approximate distributions
of the four test statistics are from the family of noncentral \chi^2 distributions with df = 1
and noncentrality parameter \lambda. The latter is, inter alia, a function of the sample size. Hence,
the sample size can be determined from the condition \lambda = \lambda_0, where \lambda_0 is
a predetermined constant which depends on the probabilities of the errors of the first and second kinds
\alpha and \beta (or power). More details about the distributions of the test statistics
and the relationship between \lambda, power, and sample size can be found in Draxler and Alexandrowicz (2015).
In particular, the determination of \lambda and the sample size, respectively, is based on a simple Monte
Carlo approach. As regards the concept of sample size a distinction between informative and total
sample size has to be made. In the conditional maximum likelihood context, the responses of
persons with minimum or maximum person score are completely uninformative. They do not contribute
to the value of the test statistic. Thus, the informative sample size does not include these persons.
The total sample size is composed of all persons. The Monte Carlo approach used in the present
problem to determine \lambda and informative (and total) sample size can briefly be described as follows.
Data (responses of a large number of persons to a number of items presented at two time points) are
generated given a user-specified scenario of a deviation from the hypothesis to be tested. The
hypothesis to be tested assumes no change between time points 1 and 2. A scenario of a deviation
is given by a choice of the item parameters at time point 1 and the shift parameter, i.e., the
LLTM eta parameters, as well as the person parameters (to be drawn randomly from a specified distribution).
The shift parameter represents a constant change of all item parameters from time point 1 to time point 2.
A test statistic T (Wald, LR, score, or gradient) is computed from the simulated data. The observed
value t of the test statistic is then divided by the informative sample size n_{infsim} observed
in the simulated data. This yields the so-called global deviation e = t / n_{infsim}, i.e.,
the chosen scenario of a deviation from the hypothesis to be tested being represented by a
single number. Let the informative sample size sought be denoted by n_{inf} (thus, this is not
the informative sample size observed in the sim. data). The noncentrality parameter \lambda can
be expressed by the product n_{inf} * e. Then, it follows from the condition \lambda = \lambda_0 that
n_{inf} * e = \lambda_0
and
n_{inf} = \lambda_0 / e.
Note that the sample of size n_{inf} is assumed to be composed only of persons with informative person scores, where
the relative frequency distribution of these informative scores is considered to be equal to the
observed relative frequency distribution of the informative scores in the simulated data. The total sample size
n_{total} is then obtained from the relation n_{inf} = n_{total} * pr, where pr is the proportion
or relative frequency of persons observed in the simulated data with a minimum or maximum score. Basing
the tests given a level \alpha on an informative sample of size n_{inf} the probability of rejecting
the hypothesis to be tested will be at least 1 - \beta if the true global deviation \geq e.
Note that in this approach the data have to be generated only once. There are no replications needed. Thus, the procedure is computationally not very time-consuming.
Since e is determined from the value of the test statistic observed in the simulated data it has
to be treated as a realized value of a random variable E. Consequently, n_{inf} is also a
realization of a random variable N_{inf}. Thus, the (realized) value n_{inf} need not be
equal to the exact value of the informative sample size that follows from the user-specified
(predetermined) \alpha, \beta, and scenario of a deviation from the hypothesis to be
tested, i.e., the selected item parameters and shift parameter used for the simulation of the data.
If the CML estimates of these parameters computed from the simulated data are close to the
predetermined parameters n_{inf} will be close to the exact value. This will generally be the
case if the number of person parameters used for simulating the data is large, e.g.,
10^5 or even 10^6 persons. In such cases, the possible random error of the computation procedure
of n_{inf} based on the sim. data may not be of practical relevance any more. That is why a
large number (of persons for the simulation process) is generally recommended.
For theoretical reasons, the random error involved in computing n_{inf} can be pretty well approximated.
A suitable approach is the well-known delta method. Basically, it is a Taylor polynomial of first order,
i.e., a linear approximation of a function. According to it the variance of a function of a random
variable can be linearly approximated by multiplying the variance of this random variable with the square
of the first derivative of the respective function. In the present problem, the variance of the test
statistic T is (approximately) given by the variance of a noncentral \chi^2 distribution. Thus, Var(T) = 2 (df + 2 \lambda),
with df = 1 and \lambda = t. Since the global deviation
e = (1 / n_{infsim}) * t it follows for the variance of the corresponding random variable E that
Var(E) = (1 / n_{infsim})^2 * Var(T). Since n_{inf} = f(e) = \lambda_0 / e one obtains by the
delta method (for the variance of the corresponding random variable N_{inf})
Var(N_{inf}) = Var(E) * (f'(e))^2,
where f'(e) = - \lambda_0 / e^2 is the derivative of f(e). The square root of Var(N_{inf})
is then used to quantify the random error of the suggested Monte Carlo computation procedure. It is called
Monte Carlo error of informative sample size.
Value
A list results.
informative sample size |
Informative sample size for each test, omitting persons with min. and max score. |
MC error of sample size |
Monte Carlo error of sample size computation for each test. |
deviation |
Shift parameter estimated from the simulated data representing the constant shift of item parameters between time points 1 and 2. |
person score distribution |
Relative frequencies of person scores observed in simulated data. Uninformative scores, i.e., minimum and maximum score, are omitted. Note that the person score distribution does also have an influence on the sample size. |
degrees of freedom |
Degrees of freedom |
noncentrality parameter |
Noncentrality parameter |
total sample size |
Total sample size for each test. See Details. |
call |
The matched call. |
References
Draxler, C., & Alexandrowicz, R. W. (2015). Sample size determination within the scope of conditional maximum likelihood estimation with special focus on testing the Rasch model. Psychometrika, 80(4), 897-919.
Fischer, G. H. (1995). The Linear Logistic Test Model. In G. H. Fischer & I. W. Molenaar (Eds.), Rasch models: Foundations, Recent Developments, and Applications (pp. 131-155). New York: Springer.
Fischer, G. H. (1983). Logistic Latent Trait Models with Linear Constraints. Psychometrika, 48(1), 3-26.
See Also
powerChange, and post_hocChange.
Examples
## Not run:
# Numerical example 4 items presented twice, thus 8 virtual items
# eta Parameter, first 4 are nuisance
# (easiness parameters of the 4 items at time point 1),
# last one is the shift parameter
eta <- c(-2,-1,1,2,0.5)
res <- sa_sizeChange(alpha = 0.05, beta = 0.05, eta=eta, persons = rnorm(10^6))
# > res
# $`informative sample size`
# W LR RS GR
# 177 174 175 173
#
# $`MC error of sample size`
# W LR RS GR
# 1.321 1.287 1.299 1.276
#
# $`deviation (estimate of shift parameter)`
# [1] 0.501
#
# $`person score distribution`
#
# 1 2 3 4 5 6 7
# 0.034 0.094 0.181 0.249 0.227 0.147 0.068
#
# $`degrees of freedom`
# [1] 1
#
# $`noncentrality parameter`
# [1] 12.995
#
# $`total sample size`
# W LR RS GR
# 182 179 180 178
#
# $call
# sa_sizeChange(alpha = 0.05, beta = 0.05, eta = eta, persons = rnorm(10^6))
## End(Not run)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.