Description Usage Arguments Value Author(s) See Also Examples
Get the indices or values of a subset of noneliminated coefficients selected via a Tk dialog or by pattern matching.
1 
object 
a model object. 
pattern 
character string containing a regular expression or
(with 
value 
if 
... 
arguments to pass on to pickFrom if

If value = FALSE
(the default), a named vector of indices,
otherwise the values of the selected coefficients. If no coefficients
are selected the returned value will be NULL
.
Heather Turner
regexp
, grep
,
pickFrom
, ofInterest
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45  set.seed(1)
### Extract indices for use with ofInterest
## fit the "UNIDIFF" mobility model across education levels
unidiff < gnm(Freq ~ educ*orig + educ*dest +
Mult(Exp(educ), orig:dest),
family = poisson, data = yaish, subset = (dest != 7))
## set coefficients in first constituent multiplier as 'ofInterest'
## using regular expression
ofInterest(unidiff) < pickCoef(unidiff, "[.]educ")
## summarise model, only showing coefficients of interest
summary(unidiff)
## get contrasts of these coefficients
getContrasts(unidiff, ofInterest(unidiff))
### Extract coefficients to use as starting values
## fit diagonal reference model with constant weights
set.seed(1)
## reconstruct counts voting Labour/nonLabour
count < with(voting, percentage/100 * total)
yvar < cbind(count, voting$total  count)
classMobility < gnm(yvar ~ 1 + Dref(origin, destination),
family = binomial, data = voting)
## create factors indicating movement in and out of salariat (class 1)
upward < with(voting, origin != 1 & destination == 1)
downward < with(voting, origin == 1 & destination != 1)
## extract diagonal effects from first model to use as starting values
diagCoef < pickCoef(classMobility, "Dref(., .)", fixed = TRUE,
value = TRUE)
## fit separate weights for the "socially mobile" groups
##  there are now 3 parameters for each weight
socialMobility < gnm(yvar ~ 1 + Dref(origin, destination,
delta = ~ 1 + downward + upward),
family = binomial, data = voting,
start = c(rep(NA, 6), diagCoef))

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