update.bab: Update method for objects of class bab
In exactLoglinTest: Monte Carlo Exact Tests for Log-linear models
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
An update method for objects created by mcexact when
method = 'bab'.
Usage
1
2
3
Arguments
object
Output from mcexact
...
Alternative arguments for the update
args
Output from mcexact
nosim
The desired number of simulations to be performed in the
update
maxiter
The maximum number of iterations allowed.
savechain
Saves the chain of goodness-of-fit statistics and
their importance weights
Details
The method update.bab calls the function bab, which is the
engine for mcexact when method = 'bab'.
Value
A list of the form outputted from mcexact
Author(s)
Brian S. Caffo
See Also
Examples
1
2
3
4
5
Example output
$conde1
15 14 10
294.8800 166.8939 238.3175
$condv1
56.78443 -39.34468 36.79749
-39.34468 51.14382 -39.59345
36.79749 -39.59345 59.74533
$dens
function (y)
sum(-lgamma(y + 1))
<environment: namespace:exactLoglinTest>
$dobs
[1] 2.985962 2.981987
$mu.hat
15 14 10 16 13 12
294.87999 166.89392 238.31746 10192.00000 63.22609 261.12001
11 9 8 7 6 5
17819.00000 167.56253 311.10608 501.68254 13677.00000 91.21138
4 3 2 1
123.77391 370.43747 95.78862 11607.00000
$n
[1] 16
$n1
[1] 3
$nosim
[1] 1000
$s
[,1]
(Intercept) 55981
res.1985NE 11929
res.1985S 18986
res.1985W 10888
res.1980NE 12197
res.1980S 18486
res.1980W 10717
factor(sym.pair)2 187
factor(sym.pair)3 538
factor(sym.pair)4 187
factor(sym.pair)5 13677
factor(sym.pair)6 740
factor(sym.pair)8 17819
$stat
function (y = NULL, mu = NULL, rowlabels = FALSE)
{
if (rowlabels)
c("deviance", "Pearson")
else {
temp <- y != 0
c(2 * sum(y[temp] * log(y[temp]/mu[temp])), sum((y -
mu)^2/mu))
}
}
<environment: namespace:exactLoglinTest>
$tdf
[1] 3
$x
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
15 1 0 1 0 0 0 1
14 1 0 0 0 0 0 1
10 1 0 0 0 0 1 0
16 1 0 0 1 0 0 1
13 1 1 0 0 0 0 1
12 1 0 0 1 0 1 0
11 1 0 1 0 0 1 0
9 1 1 0 0 0 1 0
8 1 0 0 1 0 0 0
7 1 0 1 0 0 0 0
6 1 0 0 0 0 0 0
5 1 1 0 0 0 0 0
4 1 0 0 1 1 0 0
3 1 0 1 0 1 0 0
2 1 0 0 0 1 0 0
1 1 1 0 0 1 0 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
15 0 0 0 0
14 0 0 0 0
10 0 0 0 0
16 0 0 0 0
13 0 0 1 0
12 0 0 0 0
11 0 0 0 0
9 0 1 0 0
8 0 0 0 0
7 0 0 0 0
6 0 0 0 1
5 1 0 0 0
4 0 0 1 0
3 0 1 0 0
2 1 0 0 0
1 0 0 0 0
factor(sym.pair)6 factor(sym.pair)8
15 0 0
14 0 0
10 1 0
16 0 0
13 0 0
12 0 0
11 0 1
9 0 0
8 0 0
7 1 0
6 0 0
5 0 0
4 0 0
3 0 0
2 0 0
1 0 0
$x1
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
15 1 0 1 0 0 0 1
14 1 0 0 0 0 0 1
10 1 0 0 0 0 1 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
15 0 0 0 0
14 0 0 0 0
10 0 0 0 0
factor(sym.pair)6 factor(sym.pair)8
15 0 0
14 0 0
10 1 0
$x2invt
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
16 -1 0.5 0 1 0.5 0 1
13 1 -0.5 0 -1 -0.5 0 0
12 0 0.0 1 0 0.0 1 0
11 0 0.0 0 0 0.0 0 0
9 0 0.0 -1 0 0.0 0 0
8 2 -1.0 -1 -1 -1.0 -1 -1
7 0 0.0 0 0 0.0 0 0
6 0 0.0 0 0 0.0 0 0
5 -1 1.0 1 1 0.0 0 0
4 -1 0.5 0 1 0.5 0 0
3 0 0.0 1 0 0.0 0 0
2 1 -1.0 -1 -1 0.0 0 0
1 0 0.5 0 0 0.5 0 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
16 0.5 0.5 -0.5 1
13 -0.5 -0.5 0.5 -1
12 0.0 -1.0 0.0 0
11 0.0 0.0 0.0 0
9 0.0 1.0 0.0 0
8 -1.0 0.0 0.0 -2
7 0.0 0.0 0.0 0
6 0.0 0.0 0.0 1
5 1.0 0.0 0.0 1
4 0.5 0.5 0.5 1
3 0.0 0.0 0.0 0
2 0.0 0.0 0.0 -1
1 -0.5 -0.5 -0.5 0
factor(sym.pair)6 factor(sym.pair)8
16 1 1
13 -1 -1
12 -1 -2
11 0 1
9 1 1
8 -1 0
7 1 0
6 0 0
5 0 0
4 1 1
3 -1 -1
2 0 0
1 0 0
$y
15 14 10 16 13 12 11 9 8 7 6 5 4
286 176 225 10192 63 270 17819 172 302 515 13677 87 124
3 2 1
366 100 11607
$ord
[1] 15 14 10 16 13 12 11 9 8 7 6 5 4 3 2 1
$glm.fit
Call: glm(formula = formula, family = poisson, data = data, x = TRUE,
y = TRUE)
Coefficients:
(Intercept) res.1985NE res.1985S res.1985W
1.6281 3.8411 0.5692 4.1120
res.1980NE res.1980S res.1980W factor(sym.pair)2
3.8901 -0.1752 3.4892 -0.9561
factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5 factor(sym.pair)6
-0.1728 -4.8118 7.8953 4.0206
factor(sym.pair)7 factor(sym.pair)8 factor(sym.pair)9 factor(sym.pair)10
NA 7.7658 NA NA
Degrees of Freedom: 15 Total (i.e. Null); 3 Residual
Null Deviance: 131000
Residual Deviance: 2.986 AIC: 159.2
$maxiter
[1] 1000
$startiter
[1] 994
$sumdw
[1] 237.1418 235.6738
$sumdwsq
[1] 289.0064 286.8513
$sumw
[1] 616.3756
$sumwsq
[1] 865.8051
$impconst
[1] -466390.8
$phat
[1] 0.3847359 0.3823542
$mcse
[1] 0.01405482 0.01402910
$perpos
[1] 0.993
attr(,"class")
[1] "babSummary"
$conde1
15 14 10
294.8800 166.8939 238.3175
$condv1
56.78443 -39.34468 36.79749
-39.34468 51.14382 -39.59345
36.79749 -39.59345 59.74533
$dens
function (y)
sum(-lgamma(y + 1))
<environment: namespace:exactLoglinTest>
$dobs
[1] 2.985962 2.981987
$mu.hat
15 14 10 16 13 12
294.87999 166.89392 238.31746 10192.00000 63.22609 261.12001
11 9 8 7 6 5
17819.00000 167.56253 311.10608 501.68254 13677.00000 91.21138
4 3 2 1
123.77391 370.43747 95.78862 11607.00000
$n
[1] 16
$n1
[1] 3
$nosim
[1] 10000
$s
[,1]
(Intercept) 55981
res.1985NE 11929
res.1985S 18986
res.1985W 10888
res.1980NE 12197
res.1980S 18486
res.1980W 10717
factor(sym.pair)2 187
factor(sym.pair)3 538
factor(sym.pair)4 187
factor(sym.pair)5 13677
factor(sym.pair)6 740
factor(sym.pair)8 17819
$stat
function (y = NULL, mu = NULL, rowlabels = FALSE)
{
if (rowlabels)
c("deviance", "Pearson")
else {
temp <- y != 0
c(2 * sum(y[temp] * log(y[temp]/mu[temp])), sum((y -
mu)^2/mu))
}
}
<environment: namespace:exactLoglinTest>
$tdf
[1] 3
$x
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
15 1 0 1 0 0 0 1
14 1 0 0 0 0 0 1
10 1 0 0 0 0 1 0
16 1 0 0 1 0 0 1
13 1 1 0 0 0 0 1
12 1 0 0 1 0 1 0
11 1 0 1 0 0 1 0
9 1 1 0 0 0 1 0
8 1 0 0 1 0 0 0
7 1 0 1 0 0 0 0
6 1 0 0 0 0 0 0
5 1 1 0 0 0 0 0
4 1 0 0 1 1 0 0
3 1 0 1 0 1 0 0
2 1 0 0 0 1 0 0
1 1 1 0 0 1 0 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
15 0 0 0 0
14 0 0 0 0
10 0 0 0 0
16 0 0 0 0
13 0 0 1 0
12 0 0 0 0
11 0 0 0 0
9 0 1 0 0
8 0 0 0 0
7 0 0 0 0
6 0 0 0 1
5 1 0 0 0
4 0 0 1 0
3 0 1 0 0
2 1 0 0 0
1 0 0 0 0
factor(sym.pair)6 factor(sym.pair)8
15 0 0
14 0 0
10 1 0
16 0 0
13 0 0
12 0 0
11 0 1
9 0 0
8 0 0
7 1 0
6 0 0
5 0 0
4 0 0
3 0 0
2 0 0
1 0 0
$x1
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
15 1 0 1 0 0 0 1
14 1 0 0 0 0 0 1
10 1 0 0 0 0 1 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
15 0 0 0 0
14 0 0 0 0
10 0 0 0 0
factor(sym.pair)6 factor(sym.pair)8
15 0 0
14 0 0
10 1 0
$x2invt
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
16 -1 0.5 0 1 0.5 0 1
13 1 -0.5 0 -1 -0.5 0 0
12 0 0.0 1 0 0.0 1 0
11 0 0.0 0 0 0.0 0 0
9 0 0.0 -1 0 0.0 0 0
8 2 -1.0 -1 -1 -1.0 -1 -1
7 0 0.0 0 0 0.0 0 0
6 0 0.0 0 0 0.0 0 0
5 -1 1.0 1 1 0.0 0 0
4 -1 0.5 0 1 0.5 0 0
3 0 0.0 1 0 0.0 0 0
2 1 -1.0 -1 -1 0.0 0 0
1 0 0.5 0 0 0.5 0 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
16 0.5 0.5 -0.5 1
13 -0.5 -0.5 0.5 -1
12 0.0 -1.0 0.0 0
11 0.0 0.0 0.0 0
9 0.0 1.0 0.0 0
8 -1.0 0.0 0.0 -2
7 0.0 0.0 0.0 0
6 0.0 0.0 0.0 1
5 1.0 0.0 0.0 1
4 0.5 0.5 0.5 1
3 0.0 0.0 0.0 0
2 0.0 0.0 0.0 -1
1 -0.5 -0.5 -0.5 0
factor(sym.pair)6 factor(sym.pair)8
16 1 1
13 -1 -1
12 -1 -2
11 0 1
9 1 1
8 -1 0
7 1 0
6 0 0
5 0 0
4 1 1
3 -1 -1
2 0 0
1 0 0
$y
15 14 10 16 13 12 11 9 8 7 6 5 4
286 176 225 10192 63 270 17819 172 302 515 13677 87 124
3 2 1
366 100 11607
$ord
[1] 15 14 10 16 13 12 11 9 8 7 6 5 4 3 2 1
$glm.fit
Call: glm(formula = formula, family = poisson, data = data, x = TRUE,
y = TRUE)
Coefficients:
(Intercept) res.1985NE res.1985S res.1985W
1.6281 3.8411 0.5692 4.1120
res.1980NE res.1980S res.1980W factor(sym.pair)2
3.8901 -0.1752 3.4892 -0.9561
factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5 factor(sym.pair)6
-0.1728 -4.8118 7.8953 4.0206
factor(sym.pair)7 factor(sym.pair)8 factor(sym.pair)9 factor(sym.pair)10
NA 7.7658 NA NA
Degrees of Freedom: 15 Total (i.e. Null); 3 Residual
Null Deviance: 131000
Residual Deviance: 2.986 AIC: 159.2
$maxiter
[1] 1e+06
$startiter
[1] 10994
$sumdw
[1] 2721.215 2711.367
$sumdwsq
[1] 3875.555 3865.072
$sumw
[1] 7174.347
$sumwsq
[1] 10662.93
$impconst
[1] -466390.8
$phat
[1] 0.3792979 0.3779253
$mcse
[1] 0.004520627 0.004517877
$perpos
[1] 1
attr(,"class")
[1] "babSummary"
exactLoglinTest documentation built on May 1, 2019, 9:58 p.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
An update method for objects created by mcexact when
method = 'bab'.
Usage
1 2 3 |
Arguments
object |
Output from |
... |
Alternative arguments for the update |
args |
Output from |
nosim |
The desired number of simulations to be performed in the update |
maxiter |
The maximum number of iterations allowed. |
savechain |
Saves the chain of goodness-of-fit statistics and their importance weights |
Details
The method update.bab calls the function bab, which is the
engine for mcexact when method = 'bab'.
Value
A list of the form outputted from mcexact
Author(s)
Brian S. Caffo
See Also
Examples
1 2 3 4 5 |
Example output
$conde1
15 14 10
294.8800 166.8939 238.3175
$condv1
56.78443 -39.34468 36.79749
-39.34468 51.14382 -39.59345
36.79749 -39.59345 59.74533
$dens
function (y)
sum(-lgamma(y + 1))
<environment: namespace:exactLoglinTest>
$dobs
[1] 2.985962 2.981987
$mu.hat
15 14 10 16 13 12
294.87999 166.89392 238.31746 10192.00000 63.22609 261.12001
11 9 8 7 6 5
17819.00000 167.56253 311.10608 501.68254 13677.00000 91.21138
4 3 2 1
123.77391 370.43747 95.78862 11607.00000
$n
[1] 16
$n1
[1] 3
$nosim
[1] 1000
$s
[,1]
(Intercept) 55981
res.1985NE 11929
res.1985S 18986
res.1985W 10888
res.1980NE 12197
res.1980S 18486
res.1980W 10717
factor(sym.pair)2 187
factor(sym.pair)3 538
factor(sym.pair)4 187
factor(sym.pair)5 13677
factor(sym.pair)6 740
factor(sym.pair)8 17819
$stat
function (y = NULL, mu = NULL, rowlabels = FALSE)
{
if (rowlabels)
c("deviance", "Pearson")
else {
temp <- y != 0
c(2 * sum(y[temp] * log(y[temp]/mu[temp])), sum((y -
mu)^2/mu))
}
}
<environment: namespace:exactLoglinTest>
$tdf
[1] 3
$x
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
15 1 0 1 0 0 0 1
14 1 0 0 0 0 0 1
10 1 0 0 0 0 1 0
16 1 0 0 1 0 0 1
13 1 1 0 0 0 0 1
12 1 0 0 1 0 1 0
11 1 0 1 0 0 1 0
9 1 1 0 0 0 1 0
8 1 0 0 1 0 0 0
7 1 0 1 0 0 0 0
6 1 0 0 0 0 0 0
5 1 1 0 0 0 0 0
4 1 0 0 1 1 0 0
3 1 0 1 0 1 0 0
2 1 0 0 0 1 0 0
1 1 1 0 0 1 0 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
15 0 0 0 0
14 0 0 0 0
10 0 0 0 0
16 0 0 0 0
13 0 0 1 0
12 0 0 0 0
11 0 0 0 0
9 0 1 0 0
8 0 0 0 0
7 0 0 0 0
6 0 0 0 1
5 1 0 0 0
4 0 0 1 0
3 0 1 0 0
2 1 0 0 0
1 0 0 0 0
factor(sym.pair)6 factor(sym.pair)8
15 0 0
14 0 0
10 1 0
16 0 0
13 0 0
12 0 0
11 0 1
9 0 0
8 0 0
7 1 0
6 0 0
5 0 0
4 0 0
3 0 0
2 0 0
1 0 0
$x1
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
15 1 0 1 0 0 0 1
14 1 0 0 0 0 0 1
10 1 0 0 0 0 1 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
15 0 0 0 0
14 0 0 0 0
10 0 0 0 0
factor(sym.pair)6 factor(sym.pair)8
15 0 0
14 0 0
10 1 0
$x2invt
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
16 -1 0.5 0 1 0.5 0 1
13 1 -0.5 0 -1 -0.5 0 0
12 0 0.0 1 0 0.0 1 0
11 0 0.0 0 0 0.0 0 0
9 0 0.0 -1 0 0.0 0 0
8 2 -1.0 -1 -1 -1.0 -1 -1
7 0 0.0 0 0 0.0 0 0
6 0 0.0 0 0 0.0 0 0
5 -1 1.0 1 1 0.0 0 0
4 -1 0.5 0 1 0.5 0 0
3 0 0.0 1 0 0.0 0 0
2 1 -1.0 -1 -1 0.0 0 0
1 0 0.5 0 0 0.5 0 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
16 0.5 0.5 -0.5 1
13 -0.5 -0.5 0.5 -1
12 0.0 -1.0 0.0 0
11 0.0 0.0 0.0 0
9 0.0 1.0 0.0 0
8 -1.0 0.0 0.0 -2
7 0.0 0.0 0.0 0
6 0.0 0.0 0.0 1
5 1.0 0.0 0.0 1
4 0.5 0.5 0.5 1
3 0.0 0.0 0.0 0
2 0.0 0.0 0.0 -1
1 -0.5 -0.5 -0.5 0
factor(sym.pair)6 factor(sym.pair)8
16 1 1
13 -1 -1
12 -1 -2
11 0 1
9 1 1
8 -1 0
7 1 0
6 0 0
5 0 0
4 1 1
3 -1 -1
2 0 0
1 0 0
$y
15 14 10 16 13 12 11 9 8 7 6 5 4
286 176 225 10192 63 270 17819 172 302 515 13677 87 124
3 2 1
366 100 11607
$ord
[1] 15 14 10 16 13 12 11 9 8 7 6 5 4 3 2 1
$glm.fit
Call: glm(formula = formula, family = poisson, data = data, x = TRUE,
y = TRUE)
Coefficients:
(Intercept) res.1985NE res.1985S res.1985W
1.6281 3.8411 0.5692 4.1120
res.1980NE res.1980S res.1980W factor(sym.pair)2
3.8901 -0.1752 3.4892 -0.9561
factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5 factor(sym.pair)6
-0.1728 -4.8118 7.8953 4.0206
factor(sym.pair)7 factor(sym.pair)8 factor(sym.pair)9 factor(sym.pair)10
NA 7.7658 NA NA
Degrees of Freedom: 15 Total (i.e. Null); 3 Residual
Null Deviance: 131000
Residual Deviance: 2.986 AIC: 159.2
$maxiter
[1] 1000
$startiter
[1] 994
$sumdw
[1] 237.1418 235.6738
$sumdwsq
[1] 289.0064 286.8513
$sumw
[1] 616.3756
$sumwsq
[1] 865.8051
$impconst
[1] -466390.8
$phat
[1] 0.3847359 0.3823542
$mcse
[1] 0.01405482 0.01402910
$perpos
[1] 0.993
attr(,"class")
[1] "babSummary"
$conde1
15 14 10
294.8800 166.8939 238.3175
$condv1
56.78443 -39.34468 36.79749
-39.34468 51.14382 -39.59345
36.79749 -39.59345 59.74533
$dens
function (y)
sum(-lgamma(y + 1))
<environment: namespace:exactLoglinTest>
$dobs
[1] 2.985962 2.981987
$mu.hat
15 14 10 16 13 12
294.87999 166.89392 238.31746 10192.00000 63.22609 261.12001
11 9 8 7 6 5
17819.00000 167.56253 311.10608 501.68254 13677.00000 91.21138
4 3 2 1
123.77391 370.43747 95.78862 11607.00000
$n
[1] 16
$n1
[1] 3
$nosim
[1] 10000
$s
[,1]
(Intercept) 55981
res.1985NE 11929
res.1985S 18986
res.1985W 10888
res.1980NE 12197
res.1980S 18486
res.1980W 10717
factor(sym.pair)2 187
factor(sym.pair)3 538
factor(sym.pair)4 187
factor(sym.pair)5 13677
factor(sym.pair)6 740
factor(sym.pair)8 17819
$stat
function (y = NULL, mu = NULL, rowlabels = FALSE)
{
if (rowlabels)
c("deviance", "Pearson")
else {
temp <- y != 0
c(2 * sum(y[temp] * log(y[temp]/mu[temp])), sum((y -
mu)^2/mu))
}
}
<environment: namespace:exactLoglinTest>
$tdf
[1] 3
$x
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
15 1 0 1 0 0 0 1
14 1 0 0 0 0 0 1
10 1 0 0 0 0 1 0
16 1 0 0 1 0 0 1
13 1 1 0 0 0 0 1
12 1 0 0 1 0 1 0
11 1 0 1 0 0 1 0
9 1 1 0 0 0 1 0
8 1 0 0 1 0 0 0
7 1 0 1 0 0 0 0
6 1 0 0 0 0 0 0
5 1 1 0 0 0 0 0
4 1 0 0 1 1 0 0
3 1 0 1 0 1 0 0
2 1 0 0 0 1 0 0
1 1 1 0 0 1 0 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
15 0 0 0 0
14 0 0 0 0
10 0 0 0 0
16 0 0 0 0
13 0 0 1 0
12 0 0 0 0
11 0 0 0 0
9 0 1 0 0
8 0 0 0 0
7 0 0 0 0
6 0 0 0 1
5 1 0 0 0
4 0 0 1 0
3 0 1 0 0
2 1 0 0 0
1 0 0 0 0
factor(sym.pair)6 factor(sym.pair)8
15 0 0
14 0 0
10 1 0
16 0 0
13 0 0
12 0 0
11 0 1
9 0 0
8 0 0
7 1 0
6 0 0
5 0 0
4 0 0
3 0 0
2 0 0
1 0 0
$x1
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
15 1 0 1 0 0 0 1
14 1 0 0 0 0 0 1
10 1 0 0 0 0 1 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
15 0 0 0 0
14 0 0 0 0
10 0 0 0 0
factor(sym.pair)6 factor(sym.pair)8
15 0 0
14 0 0
10 1 0
$x2invt
(Intercept) res.1985NE res.1985S res.1985W res.1980NE res.1980S res.1980W
16 -1 0.5 0 1 0.5 0 1
13 1 -0.5 0 -1 -0.5 0 0
12 0 0.0 1 0 0.0 1 0
11 0 0.0 0 0 0.0 0 0
9 0 0.0 -1 0 0.0 0 0
8 2 -1.0 -1 -1 -1.0 -1 -1
7 0 0.0 0 0 0.0 0 0
6 0 0.0 0 0 0.0 0 0
5 -1 1.0 1 1 0.0 0 0
4 -1 0.5 0 1 0.5 0 0
3 0 0.0 1 0 0.0 0 0
2 1 -1.0 -1 -1 0.0 0 0
1 0 0.5 0 0 0.5 0 0
factor(sym.pair)2 factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5
16 0.5 0.5 -0.5 1
13 -0.5 -0.5 0.5 -1
12 0.0 -1.0 0.0 0
11 0.0 0.0 0.0 0
9 0.0 1.0 0.0 0
8 -1.0 0.0 0.0 -2
7 0.0 0.0 0.0 0
6 0.0 0.0 0.0 1
5 1.0 0.0 0.0 1
4 0.5 0.5 0.5 1
3 0.0 0.0 0.0 0
2 0.0 0.0 0.0 -1
1 -0.5 -0.5 -0.5 0
factor(sym.pair)6 factor(sym.pair)8
16 1 1
13 -1 -1
12 -1 -2
11 0 1
9 1 1
8 -1 0
7 1 0
6 0 0
5 0 0
4 1 1
3 -1 -1
2 0 0
1 0 0
$y
15 14 10 16 13 12 11 9 8 7 6 5 4
286 176 225 10192 63 270 17819 172 302 515 13677 87 124
3 2 1
366 100 11607
$ord
[1] 15 14 10 16 13 12 11 9 8 7 6 5 4 3 2 1
$glm.fit
Call: glm(formula = formula, family = poisson, data = data, x = TRUE,
y = TRUE)
Coefficients:
(Intercept) res.1985NE res.1985S res.1985W
1.6281 3.8411 0.5692 4.1120
res.1980NE res.1980S res.1980W factor(sym.pair)2
3.8901 -0.1752 3.4892 -0.9561
factor(sym.pair)3 factor(sym.pair)4 factor(sym.pair)5 factor(sym.pair)6
-0.1728 -4.8118 7.8953 4.0206
factor(sym.pair)7 factor(sym.pair)8 factor(sym.pair)9 factor(sym.pair)10
NA 7.7658 NA NA
Degrees of Freedom: 15 Total (i.e. Null); 3 Residual
Null Deviance: 131000
Residual Deviance: 2.986 AIC: 159.2
$maxiter
[1] 1e+06
$startiter
[1] 10994
$sumdw
[1] 2721.215 2711.367
$sumdwsq
[1] 3875.555 3865.072
$sumw
[1] 7174.347
$sumwsq
[1] 10662.93
$impconst
[1] -466390.8
$phat
[1] 0.3792979 0.3779253
$mcse
[1] 0.004520627 0.004517877
$perpos
[1] 1
attr(,"class")
[1] "babSummary"
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