Description Usage Arguments Details Value Author(s) See Also Examples
Generate and fit all 0way, 1way, 2way, ... kway terms in a glm.
This function is designed mainly for hierarchical
loglinear models (or glm
s
in the poission family), where it is desired to find the
highestorder terms necessary to achieve a satisfactory fit.
Using anova
on the resulting glmlist
object will then give sequential tests of the pooled contributions of
all terms of degree k+1 over and above those of degree k.
This function is also intended as an example of a generating function
for glmlist
objects, to facilitate model comparison, extraction,
summary and plotting of model components, etc., perhaps using lapply
or similar.
1 
formula 
a twosided formula for the 1way effects in the model.
The LHS should be the response, and the RHS should be the firstorder terms
connected by 
family 
a description of the error distribution and link function to be used in the
model. This can be a character string naming a family function, a family
function or the result of a call to a family function.
(See 
data 
an optional data frame, list or environment (or object coercible by

... 
Other arguments passed to 
order 
Highest order interaction of the models generated. Defaults to the number of terms in the model formula. 
prefix 
Prefix used to label the models fit in the 
With y
as the response in the formula
, the 0way (null) model
is y ~ 1
.
The 1way ("main effects") model is that specified in the
formula
argument. The kway model is generated using the formula
. ~ .^k
.
With the default order = nt
, the final model is the saturated model.
As presently written, the function requires a twosided formula with an explicit
response on the LHS. For frequency data in table form (e.g., produced by xtabs
)
you the data
argument is coerced to a data.frame, so you
should supply the formula
in the form Freq ~
....
An object of class glmlist
, of length order+1
containing the 0way, 1way, ...
models up to degree order
.
Michael Friendly and Heather Turner
glmlist
,
Summarise
(soon to be deprecated),
LRstats
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  ## artificial data
factors < expand.grid(A=factor(1:3), B=factor(1:2), C=factor(1:3), D=factor(1:2))
Freq < rpois(nrow(factors), lambda=40)
df < cbind(factors, Freq)
mods3 < Kway(Freq ~ A + B + C, data=df, family=poisson)
LRstats(mods3)
mods4 < Kway(Freq ~ A + B + C + D, data=df, family=poisson)
LRstats(mods4)
# JobSatisfaction data
data(JobSatisfaction, package="vcd")
modSat < Kway(Freq ~ management+supervisor+own, data=JobSatisfaction,
family=poisson, prefix="JobSat")
LRstats(modSat)
anova(modSat, test="Chisq")
# Rochdale data: very sparse, in table form
data(Rochdale, package="vcd")
## Not run:
modRoch < Kway(Freq~EconActive + Age + HusbandEmployed + Child +
Education + HusbandEducation + Asian + HouseholdWorking,
data=Rochdale, family=poisson)
LRstats(modRoch)
## End(Not run)

Loading required package: vcd
Loading required package: grid
Loading required package: gnm
Likelihood summary table:
AIC BIC LR Chisq Df Pr(>Chisq)
kway.0 236.43 238.02 35.431 35 0.4479
kway.1 240.10 249.60 29.103 30 0.5122
kway.2 249.35 271.52 22.353 22 0.4390
kway.3 253.47 281.98 18.470 18 0.4251
Likelihood summary table:
AIC BIC LR Chisq Df Pr(>Chisq)
kway.0 236.43 238.02 35.431 35 0.4479
kway.1 239.20 250.28 26.194 29 0.6151
kway.2 251.80 283.47 12.798 16 0.6875
kway.3 264.38 315.05 1.379 4 0.8479
kway.4 271.00 328.01 0.000 0 1.0000
Likelihood summary table:
AIC BIC LR Chisq Df Pr(>Chisq)
JobSat.0 260.251 260.330 208.775 7 <2e16 ***
JobSat.1 175.472 175.790 117.997 4 <2e16 ***
JobSat.2 63.541 64.097 0.065 1 0.7989
JobSat.3 65.476 66.111 0.000 0 1.0000

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Deviance Table
Model 1: Freq ~ 1
Model 2: Freq ~ management + supervisor + own
Model 3: Freq ~ management + supervisor + own + management:supervisor +
management:own + supervisor:own
Model 4: Freq ~ management + supervisor + own + management:supervisor +
management:own + supervisor:own + management:supervisor:own
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1 7 208.775
2 4 117.997 3 90.778 <2e16 ***
3 1 0.065 3 117.932 <2e16 ***
4 0 0.000 1 0.065 0.7989

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning messages:
1: glm.fit: fitted rates numerically 0 occurred
2: glm.fit: algorithm did not converge
3: glm.fit: fitted rates numerically 0 occurred
4: glm.fit: algorithm did not converge
5: glm.fit: fitted rates numerically 0 occurred
6: glm.fit: fitted rates numerically 0 occurred
Likelihood summary table:
AIC BIC LR Chisq Df Pr(>Chisq)
kway.0 2620.23 2623.78 2332.66 255 <2e16 ***
kway.1 1155.95 1187.86 852.37 247 <2e16 ***
kway.2 504.14 635.31 144.56 219 1
kway.3 534.29 863.99 62.71 163 1
kway.4 611.58 1189.44 0.00 93 1
kway.5 723.58 1499.97 0.00 37 1
kway.6 779.58 1655.24 0.00 9 1
kway.7 795.58 1699.60 0.00 1 1
kway.8 797.58 1705.15 0.00 0 <2e16 ***

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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