Function to fit an iterated weighted least squares model.
Fits an iterated weighted least squares model.
iwlsm(x, ...) ## S3 method for class 'formula' iwlsm(formula, data, weights, ses, ..., subset, na.action, method = c("M", "MM", "model.frame"), wt.method = c("inv.var", "case"), model = TRUE, x.ret = TRUE, y.ret = FALSE, contrasts = NULL) ## Default S3 method: iwlsm(x, y, weights, ses, ..., w = rep(1/nrow(x), nrow(x)), init = "ls", psi = psi.iwlsm, scale.est = c("MAD", "Huber", "proposal 2"), k2 = 1.345, method = c("M", "MM"), wt.method = c("inv.var", "case"), maxit = 20, acc = 1e-4, test.vec = "resid", lqs.control = NULL) psi.iwlsm(u, k, deriv = 0, w, sj2, hh)
formula: a formula of the form y ~ x1 + x2 + ....
data: data frame from which variables specified in formula are preferentially to be taken.
weights: a vector of prior weights for each case.
subset: An index vector specifying the cases to be used in fitting.
ses: Estimated variance of the responses. Will be paseed to psi as sj2
na.action: A function to specify the action to be taken if NAs are found. The factory-fresh default action in is na.omit, and can be changed by options(na.action=).
x: a matrix or data frame containing the explanatory variables.
y: the response: a vector of length the number of rows of x.
method: Must be "M". (argument not used here).
wt.method: are the weights case weights (giving the relative importance of case, so a weight of 2 means there are two of these) or the inverse of the variances, so a weight of two means this error is half as variable? This will not work at present.
model: should the model frame be returned in the object?
x.ret: should the model matrix be returned in the object?
y.ret: should the response be returned in the object?
contrasts: optional contrast specifications: se lm.
w: (optional) initial down-weighting for each case. Will not work at present.
init: (optional) initial values for the coefficients OR a method to find initial values OR the result of a fit with a coef component. Known methods are "ls" (the default) for an initial least-squares fit using weights w*weights, and "lts" for an unweighted least-trimmed squares fit with 200 samples. Probably not functioning.
psi: the psi function is specified by this argument. It must give (possibly by name) a function g(x, ..., deriv, w) that for deriv=0 returns psi(x)/x and for deriv=1 returns some value. Extra arguments may be passed in via ....
scale.est: method of scale estimation: re-scaled MAD of the residuals (default) or Huber"s proposal 2 (which can be selected by either "Huber"
or "proposal 2").
k2: tuning constant used for Huber proposal 2 scale estimation.
maxit: the limit on the number of IWLS iterations.
acc: the accuracy for the stopping criterion.
test.vec: the stopping criterion is based on changes in this vector.
...: additional arguments to be passed to iwlsm.default or to the psi
function.
lqs.control: An optional list of control values for lqs.
u: numeric vector of evaluation points.
k: tuning constant. Not used.
deriv: 0 or 1: compute values of the psi function or of its first derivative. (Latter not used).
sj2: Estimated variance of the responses
hh: Diagonal values of the hat matrix
This function is very slightly adapted from rlm in packages MASS. It alternates between weighted least squares and estimation of variance on the basis of a common variance. The function psi.iwlsm calculates the weights for the next iteration. Used by siena08 to combine estimates from different sienaFits.
An object of class "iwlsm" inheriting from "lm". Note that the df.residual component is deliberately set to NA to avoid inappropriate estimation of the residual scale from the residual mean square by "lm" methods.
The additional components not in an lm object are
s: the robust scale estimate used
w: the weights used in the IWLS process
psi: the psi function with parameters substituted
conv: the convergence criteria at each iteration
converged: did the IWLS converge?
wresid: a working residual, weighted for "inv.var" weights only.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. See also https://www.stats.ox.ac.uk/~snijders/siena/
Ruth Ripley
The function has been changed as little as possible, but has only been used with default arguments. The other options have been retained just in case they may prove useful.
siena08, sienaMeta, sienaFit
## Not run: ##not enough data here for a sensible example, but shows the idea. myalgorithm <- sienaAlgorithmCreate(nsub=2, n3=100) mynet1 <- sienaDependent(array(c(s501, s502), dim=c(50, 50, 2))) mynet2 <- sienaDependent(array(c(s502, s503), dim=c(50, 50, 2))) mydata1 <- sienaDataCreate(mynet1) mydata2 <- sienaDataCreate(mynet2) myeff1 <- getEffects(mydata1) myeff2 <- getEffects(mydata2) myeff1 <- setEffect(myeff1, transTrip, fix=TRUE, test=TRUE) myeff2 <- setEffect(myeff2, transTrip, fix=TRUE, test=TRUE) myeff1 <- setEffect(myeff1, cycle3, fix=TRUE, test=TRUE) myeff2 <- setEffect(myeff2, cycle3, fix=TRUE, test=TRUE) ans1 <- siena07(myalgorithm, data=mydata1, effects=myeff1, batch=TRUE) ans2 <- siena07(myalgorithm, data=mydata2, effects=myeff2, batch=TRUE) meta <- siena08(ans1, ans2) metadf <- split(meta$thetadf, meta$thetadf$effects)[[1]] metalm <- iwlsm(theta ~ tconv, metadf, ses=se^2) ## End(Not run)