index_batch: The main function to compute the point estimates and 95%...
In lmeNB: Compute the Personalized Activity Index Based on a Negative Binomial Model
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Let m[i] be the number of pre-measurements and
n[i] be the total number of repeated measures.
Then the repeated measure of a subject can be divided into a pre-measurement set and a new measurement set as
Y[i]=(Y[i,pre],Y[i,new])
, where
Y[i,pre]=(y[i,1],\cdots,Y[i,m[i]])
and
Y[i,new]=(Y[i,m[i]+1],...,Y[i,n[i]])
.
Given an output of fitParaIND, fitParaAR1,
fitSemiIND, fitSemiAR1 or lmeNB,
this function computes the probability of observing the response counts as large as those new observations of subject i,
y[i,new]
conditional on the subject's previous observations
y[i,pre]
for subject i.
That is, this function returns a point estimate and its asymptotic 95% confidence interval (for a parametric model) of the conditional probability for each subject:
Pr(q(Y[i,new]) ≥ q(y[i,new])| Y[i,pre]=y[i,pre])
.
When the semiparametric approach is employed, the standard error and 95% confidence intervals are computed using bootstrap samples.
A scalar statistic to summarize the new response counts can be either the total count,
q(Y[i,new])=∑[j=m[i]+1]^{n[i]} Y[ij] ,
or the maximum,
q(Y[i,new])=\max{ Y[ij];j=m[i]+1,...,n[i] }
.
See Zhao et al.(2013), for more details.
Usage
1
2
Arguments
data
See lmeNB.
This dataset does not have to be the same as the one used in the computations of negative binomial mixed effect regression
(fitParaIND, fitParaAR1,
fitSemiIND, fitSemiAR1 or lmeNB).
labelnp
A vector of length the total number of repeated measures
(= ∑[i=1]^N n[i] ), indicating new measures by TRUE and pre-measures by FALSE. For examples, suppose there are three subjects of interest.
The first subject has a n[1]=7 repeated measures and the last 3 measures are new.
The second and the third subjects both have n[2]=n[3]=5 repeated measures and the last 2 measures are new. In this scenario,
labelnp=c(rep(FALSE,4), rep(TRUE,3), rep(FALSE,3), rep(TRUE,2) ,rep(FALSE,3) ,rep(TRUE,2)).
ID
See lmeNB. The length of ID must be the same as nrow(data).
Vcode
Necessary only if the olmeNB is an output of AR(1) models.
See lmeNB.
olmeNB
Output of fitParaIND,fitParaAR1,fitSemiIND,fitSemiAR1
or lmeNB.
subset
An optional expression indicating the subset of the subjects of that the index should be computed.
qfun
If qfun="sum",
a scalar statistic to summarize the new response counts is the total count.
If qfun="max",
a scalar statistic to summarize the new response counts is the maximum.
IPRT
print control.
i.se
If i.se=TRUE then the standard errors of the estimator of the conditional probability are returned for the output of
fitParaIND or fitParaAR1.
The semi-parametric approach, fitSemiIND or fitSemiAR1, do not return the standard errors.
MC
Necessary when olmeNB if the AR(1) model outputs.
See CP.ar1.se.
C
See lmeNB.
C=TRUE option could make computations of CPI faster for some patients.
i.tol
See lmeNB.
Details
The standard error of the point estimate on the logit scale is constructed using the delta method for the parametric model, where distributional assumption was made for random effects.
Value
The N by 4 (3, if hide the SE) numeric matrix, containing the point estimate of the conditional probability, and the lower and the upper bounds of the 95
Author(s)
Zhao, Y. and Kondo, Y.
References
Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients. (2013) Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., Journal of the American Statistical Association.
See Also
The main function to fit the Negative Binomial mixed-effect model:
lmeNB,
The internal functions of lmeNB for fitting relevant models:
fitParaIND,
fitParaAR1,
fitSemiIND,
fitSemiAR1,
The subroutines of index.batch:
jCP.ar1,
CP1.ar1,
MCCP.ar1,
CP.ar1.se,
CP.se,
jCP,
The functions to generate simulated datasets:
rNBME.R.
Examples
1
2
## See the examples in help files of
## fitParaIND, fitAR1IND, fitSemiIND, fitSemiAR1 and rNBME.R
lmeNB documentation built on May 2, 2019, 3:34 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Let m[i] be the number of pre-measurements and
n[i] be the total number of repeated measures.
Then the repeated measure of a subject can be divided into a pre-measurement set and a new measurement set as
Y[i]=(Y[i,pre],Y[i,new])
, where
Y[i,pre]=(y[i,1],\cdots,Y[i,m[i]])
and
Y[i,new]=(Y[i,m[i]+1],...,Y[i,n[i]])
.
Given an output of fitParaIND, fitParaAR1,
fitSemiIND, fitSemiAR1 or lmeNB,
this function computes the probability of observing the response counts as large as those new observations of subject i,
y[i,new]
conditional on the subject's previous observations
y[i,pre]
for subject i.
That is, this function returns a point estimate and its asymptotic 95% confidence interval (for a parametric model) of the conditional probability for each subject:
Pr(q(Y[i,new]) ≥ q(y[i,new])| Y[i,pre]=y[i,pre]) .
When the semiparametric approach is employed, the standard error and 95% confidence intervals are computed using bootstrap samples. A scalar statistic to summarize the new response counts can be either the total count, q(Y[i,new])=∑[j=m[i]+1]^{n[i]} Y[ij] , or the maximum, q(Y[i,new])=\max{ Y[ij];j=m[i]+1,...,n[i] } . See Zhao et al.(2013), for more details.
Usage
1 2 |
Arguments
data |
See |
labelnp |
A vector of length the total number of repeated measures
(= ∑[i=1]^N n[i] ), indicating new measures by The first subject has a n[1]=7 repeated measures and the last 3 measures are new. The second and the third subjects both have n[2]=n[3]=5 repeated measures and the last 2 measures are new. In this scenario,
|
ID |
See |
Vcode |
Necessary only if the |
olmeNB |
Output of |
subset |
An optional expression indicating the subset of the subjects of that the index should be computed. |
qfun |
If If |
IPRT |
print control. |
i.se |
If |
MC |
Necessary when |
C |
See |
i.tol |
See |
Details
The standard error of the point estimate on the logit scale is constructed using the delta method for the parametric model, where distributional assumption was made for random effects.
Value
The N by 4 (3, if hide the SE) numeric matrix, containing the point estimate of the conditional probability, and the lower and the upper bounds of the 95
Author(s)
Zhao, Y. and Kondo, Y.
References
Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients. (2013) Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., Journal of the American Statistical Association.
See Also
The main function to fit the Negative Binomial mixed-effect model:
lmeNB,
The internal functions of lmeNB for fitting relevant models:
fitParaIND,
fitParaAR1,
fitSemiIND,
fitSemiAR1,
The subroutines of index.batch:
jCP.ar1,
CP1.ar1,
MCCP.ar1,
CP.ar1.se,
CP.se,
jCP,
The functions to generate simulated datasets:
rNBME.R.
Examples
1 2 | ## See the examples in help files of
## fitParaIND, fitAR1IND, fitSemiIND, fitSemiAR1 and rNBME.R
|
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