
Fit All K-way Models in a GLM
Kway.Rd
Generate and fit all 0-way, 1-way, 2-way, ... k-way terms in a glm.
This function is designed mainly for hierarchical
loglinear models (or glm
s
in the poisson family), where it is desired to find the
highest-order 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.
Arguments
- formula
a two-sided formula for the 1-way effects in the model. The LHS should be the response, and the RHS should be the first-order terms connected by
+
signs.- 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
family
for details of family functions.)- data
an optional data frame, list or environment (or object coercible by
as.data.frame
to a data frame) containing the variables in the model. If not found in data, the variables are taken fromenvironment(formula)
, typically the environment from whichglm
is called.- ...
Other arguments passed to
glm
- 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
glmlist
object.
Details
With y
as the response in the formula
, the 0-way (null) model
is y ~ 1
.
The 1-way ("main effects") model is that specified in the
formula
argument. The k-way 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 two-sided 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 ~
....
Value
An object of class glmlist
, of length order+1
containing the 0-way, 1-way, ...
models up to degree order
.
Examples
## 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)
#> Likelihood summary table:
#> AIC BIC LR Chisq Df Pr(>Chisq)
#> kway.0 230.64 232.22 30.407 35 0.6895
#> kway.1 237.61 247.11 27.380 30 0.6033
#> kway.2 246.68 268.85 20.448 22 0.5550
#> kway.3 253.84 282.34 19.607 18 0.3554
mods4 <- Kway(Freq ~ A + B + C + D, data=df, family=poisson)
LRstats(mods4)
#> Likelihood summary table:
#> AIC BIC LR Chisq Df Pr(>Chisq)
#> kway.0 230.64 232.22 30.4071 35 0.6895
#> kway.1 239.53 250.61 27.2945 29 0.5558
#> kway.2 251.25 282.92 13.0215 16 0.6712
#> kway.3 263.72 314.40 1.4904 4 0.8283
#> kway.4 270.23 327.24 0.0000 0 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# JobSatisfaction data
data(JobSatisfaction, package="vcd")
modSat <- Kway(Freq ~ management+supervisor+own,
data=JobSatisfaction,
family=poisson, prefix="JobSat")
LRstats(modSat)
#> Likelihood summary table:
#> AIC BIC LR Chisq Df Pr(>Chisq)
#> JobSat.0 260.251 260.330 208.775 7 <2e-16 ***
#> JobSat.1 175.472 175.790 117.997 4 <2e-16 ***
#> JobSat.2 63.541 64.097 0.065 1 0.7989
#> JobSat.3 65.476 66.111 0.000 0 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(modSat, test="Chisq")
#> 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 <2e-16 ***
#> 3 1 0.065 3 117.932 <2e-16 ***
#> 4 0 0.000 1 0.065 0.7989
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Rochdale data: very sparse, in table form
data(Rochdale, package="vcd")
if (FALSE) {
modRoch <- Kway(Freq~EconActive + Age + HusbandEmployed + Child +
Education + HusbandEducation + Asian + HouseholdWorking,
data=Rochdale, family=poisson)
LRstats(modRoch)
}