npem.sem | R Documentation |
Uses the SEM algorithm to obtain estimated standard errors for the MLEs obtained after fitting the normal-Poisson mixture model to data on a cell proliferation assay.
npem.sem(
y,
npem.em.out,
cells = 10^6,
start = npem.em.out$ests * 1.05 + 0.001,
n = c(24, 24, 24, 22),
n.plates = 1,
use.order.constraint = TRUE,
all.se = TRUE,
tol = 0.000001,
maxk = 20,
prnt = 0,
do.var = TRUE,
maxit = 1000
)
y |
Vector of transformed scintillation counts, in lexicographical order (plate by plate and group by group within a plate.) |
npem.em.out |
Output from the function |
cells |
Number of cells per well. The |
start |
Starting estimates, some small distance away from the MLE. A
vector of the form ( |
n |
Vector giving the number of wells within each group. This may have length either n.groups (if all plates have the same number of wells per group) or n.groups*n.plates. |
n.plates |
The number of plates in the data. |
use.order.constraint |
If TRUE, force the constraint |
all.se |
If TRUE, do the full SEM algorithm; if FALSE, ignore the
plate-specific parameters (a,b, |
tol |
Tolerance to determine when to stop the EM algorithm. |
maxk |
Maximum k value in sum calculating |
prnt |
If 0, don't print anything; if 1, print out (at each step) the rate matrix and which elements have converged. |
do.var |
If TRUE, calculate the variance-covariance matrix and standard errors; if FALSE, only calculate the full-data information matrix and rate matrix. |
maxit |
Maximum number of iterations to perform. |
Calculations are performed in a C routine. It is important to first run
npem.em()
with a very small value for tol
, such as
10^{-13}
.
infor |
The full-data information matrix |
rates |
The rate matrix ("DM" in Meng and Rubin's notation). |
n.iter |
Number of iterations performed in calculating the rate matrix. |
var |
The estimated variance-covariance matrix. |
se |
The estimated standard
errors. (The square root of the diagnol of |
Karl W Broman, broman@wisc.edu
Broman et al. (1996) Estimation of antigen-responsive T cell
frequencies in PBMC from human subjects. J Immunol Meth 198:119-132
Dempster et al. (1977) Maximum likelihood estimation from incomplete data
via the EM algorithm. J Roy Statist Soc Ser B 39:1-38
Meng and Rubin
(1991) Using EM to obtain asymptotic variance-covariance matrices: the SEM
algorithm. J Am Statist Asso 86:899-909
npem.em()
data(p713)
start.pl3 <- npem.start(p713$counts[[3]],n=p713$n)
out.pl3 <- npem.em(p713$counts[[3]],start.pl3,n=p713$n,tol=1e-13)
out.sem.pl3 <- npem.sem(p713$counts[[3]],out.pl3,n=p713$n)
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