Description Usage Arguments Details Value Note Author(s) References See Also Examples
The functions use Gaussian basis functions to estimate the noncentrality parameters (ncp) from a large number of t-statistics.
1 2 3 4 5 6 |
tstat |
Numeric vector of noncentrality parameters |
df |
Numeric vector of degrees of freedom |
penalty |
An integer scalar among 1 through 5, indicating the order of derivatives of the estimated density funciton of ncp. The integral of square of such derivatives is the penalty to the log likelihood function. A character value among |
lambdas |
Numeric vector of smoothness tuning parameter |
starts |
Optional numeric vector of starting values. If missing, |
IC |
Character; one of |
K |
The number of basis Gaussian density functions. |
bounds |
A numeric vector of length 2, giving the approximate bounds where most of the probability of ncp lies. |
solver |
Character. The name of the function for solving quadratic programming problems. Note that |
plotit |
logical; indicating if |
verbose |
logical; if |
approx.hess |
either logical or a number between 0 and 1. This helps in reducing time in evaluating the hessian matrix. If it is set to |
... |
other paramters passed to |
nparncpt
is a wrapper for nparncpt.sqp
, the latter of which uses a sequential quadratic programming algorithm to find the mixing proportions
of the basis Gaussian density functions.
A list with class attribute c("nparncpt", "ncpest")
pi0 |
estimated proportion of true nulls |
mu.ncp |
mean of ncp |
sd.ncp |
SD of ncp |
logLik |
an object of class |
enp |
estimated ENP |
par |
estimated parameters |
lambda |
the lambda that minimizes NIC |
gradiant |
analytic gradiant at the estimate |
hessian |
analytic hessian at the estimate |
beta |
estimated mixing proportions for the NCP distribution |
IC |
the information criterion specified by the user |
all.mus |
mean of each basis Gaussian density |
all.sigs |
SD of each basis Gaussian density |
data |
a list of |
i.final |
the index of |
all.pi0s |
estimated pi0 for each lambda |
all.enps |
ENP for each lambda |
all.thetas |
parameter estimates for each lambda |
all.nics |
Network information criterion (NIC) for each lambda |
all.nic.sd |
SD of NIC for each lambda |
all.lambdas |
the |
nobs |
the number of test statistics |
df
could be Inf
for z-tests. When this is the case, approximation
is ignored.
Long Qu
Qu L, Nettleton D, Dekkers JCM. (2012) Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of $t$-statistics, with Applications to False Discovery Rate Estimation in Microarray Data Analysis. Biometrics, 68, 1178–1187.
parncpt
, sparncpt
,
fitted.nparncpt
, plot.nparncpt
, summary.nparncpt
,
coef.ncpest
, logLik.ncpest
, vcov.ncpest
,
AIC
, dncp
1 2 3 4 5 6 7 8 | ## Not run:
data(simulatedTstat)
(npfit=nparncpt(tstat=simulatedTstat, df=8));
(pfit=parncpt(tstat=simulatedTstat, df=8, zeromean=FALSE)); plot(pfit)
(pfit0=parncpt(tstat=simulatedTstat, df=8, zeromean=TRUE)); plot(pfit0)
(spfit=sparncpt(npfit,pfit)); plot(spfit)
## End(Not run)
|
pi0= 0.7483634
mu.ncp= -0.02254265
sd.ncp= 1.523897
enp= 2.408478
lambda= 100
Warning message:
In nparncpt.sqp(tstat, df, ...) :
Less than half of the estimated coefficients (betas) are less than 0.01. Your might want to try enlarging the `bounds` argument.
pi0 (proportion of null hypotheses) = 0.7483103
mu.ncp (mean of noncentrality parameters) = -0.03791745
sd.ncp (SD of noncentrality parameters) = 1.624555
pi0 (proportion of null hypotheses) = 0.7486391
mu.ncp (mean of noncentrality parameters) = 0
sd.ncp (SD of noncentrality parameters) = 1.626181
pi0= 0.7483134
mu.ncp= -0.03704109
sd.ncp= 1.534416
rho= 0.943
enp= 3.966283
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