Description Usage Arguments Details Value Author(s) See Also Examples
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 poission 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.
1 |
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 |
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 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 ~
....
An object of class glmlist
, of length order+1
containing the 0-way, 1-way, ...
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 <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 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 <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
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 <2e-16 ***
kway.1 1155.95 1187.86 852.37 247 <2e-16 ***
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 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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