deviance(setβ!(com05fe, β₀))
-
2491.15143905372542491.151439053725BenchmarkTools.Trial: 10000 samples with 1 evaluation.
- Range (min … max): 33.798 μs … 113.439 μs ┊ GC (min … max): 0.00% … 0.00%
- Time (median): 36.778 μs ┊ GC (median): 0.00%
- Time (mean ± σ): 36.707 μs ± 1.706 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
+ Range (min … max): 34.239 μs … 79.352 μs ┊ GC (min … max): 0.00% … 0.00%
+ Time (median): 35.081 μs ┊ GC (median): 0.00%
+ Time (mean ± σ): 35.674 μs ± 3.225 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
- ▂ ▁ ▁▃▆▂ ▄▇█▂ ▁▁ ▂
- ▆█▇▁▁▁▁▁▆▇█▅▄▁▃▄▃████▄▁▁▃▃▅████▇▅▄▆▅▄▆▆███▇▅▆▅▅▅▃▁▄▄▃▃▃▄▅▅▆▆ █
- 33.8 μs Histogram: log(frequency) by time 40 μs <
+ ▅▁█▅▂ ▁
+ █████▇▇▇███▇▇███▇▇▆▇▆▆▆▆▆▆▅▆▆▅▅▅▅▄▃▄▅▄▅▃▃▁▁▃▄▄▅▇▆▆▆▃▄▁▃▃▄▅▅ █
+ 34.2 μs Histogram: log(frequency) by time 53.1 μs <
Memory estimate: 16 bytes, allocs estimate: 1.fit!(com05fe, β₀);
[ Info: (0, 2491.1514390537254)
-[ Info: (1, 2411.165742459929)
+[ Info: (1, 2411.1657424599293)
[ Info: (2, 2409.382451337132)
-[ Info: (3, 2409.3774282835857)
+[ Info: (3, 2409.377428283585)
[ Info: (4, 2409.3774281600413)
@@ -867,14 +867,14 @@
@benchmark deviance(updateβ!(m)) seconds = 1 setup = (m = com05fe)
-BenchmarkTools.Trial: 10000 samples with 1 evaluation.
- Range (min … max): 67.528 μs … 161.118 μs ┊ GC (min … max): 0.00% … 0.00%
- Time (median): 76.638 μs ┊ GC (median): 0.00%
- Time (mean ± σ): 76.986 μs ± 2.640 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
+BenchmarkTools.Trial: 7372 samples with 1 evaluation.
+ Range (min … max): 126.577 μs … 294.077 μs ┊ GC (min … max): 0.00% … 0.00%
+ Time (median): 130.825 μs ┊ GC (median): 0.00%
+ Time (mean ± σ): 133.084 μs ± 12.228 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
- ▂▂▂▄▅▅▄▇██▆▅▄▄▅▅▄▃▂▂▂▂▂▁▁▁ ▂
- ▅▅▆▆▇▇▇▆▆▆▆▆▆▆▅▆▇▇▇██▇██████████████████████████████▇▇▇▇▇▇▇▇ █
- 67.5 μs Histogram: log(frequency) by time 84.5 μs <
+ ▇▇▇▆█▇▄▄▃▂▂▁ ▂
+ ██████████████▇█▇▇▇▇▇▇▆▆▅▅▆▆▆▆▂▅▄▅▄▅▆▅▆▆▅▅▆▅▇▅▅▆▆▅▅▅▆▆▆▄▅▄▄▅▄ █
+ 127 μs Histogram: log(frequency) by time 189 μs <
Memory estimate: 16.12 KiB, allocs estimate: 6.
@@ -1052,14 +1052,14 @@ m = BernoulliPIRLS(com05.X, com05.y, only(com05.reterms).refs)
pdeviance(m)
-2409.377428160041
+2409.3774281600413
As with IRLS, the first iteration of PIRLS reduces the objective, which is the penalized deviance in this case, substantially.
pdeviance(updateu!(m))
-2233.1209476357844
+2233.1209476357853
Create a pirls! method for this struct.
@@ -1094,9 +1094,9 @@
pirls!(m; verbose=true);
-[ Info: (0, 2409.377428160041)
-[ Info: (1, 2233.1209476357844)
-[ Info: (2, 2231.605935279706)
+[ Info: (0, 2409.3774281600413)
+[ Info: (1, 2233.1209476357853)
+[ Info: (2, 2231.6059352797065)
[ Info: (3, 2231.6002198321576)
[ Info: (4, 2231.600219406561)
@@ -1105,14 +1105,14 @@
@benchmark pirls!(mm) seconds = 1 setup = (mm = m)
-BenchmarkTools.Trial: 4402 samples with 1 evaluation.
- Range (min … max): 199.693 μs … 331.599 μs ┊ GC (min … max): 0.00% … 0.00%
- Time (median): 227.186 μs ┊ GC (median): 0.00%
- Time (mean ± σ): 226.091 μs ± 5.569 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
+BenchmarkTools.Trial: 3848 samples with 1 evaluation.
+ Range (min … max): 237.623 μs … 461.619 μs ┊ GC (min … max): 0.00% … 0.00%
+ Time (median): 243.844 μs ┊ GC (median): 0.00%
+ Time (mean ± σ): 257.466 μs ± 31.167 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
- ▁ ▇█
- ▂▂▂▂▁▂▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▁▂▂▁▁▂▂▂▂▁▂▁▁▁▁▄█▄▄▃▂▃▃▃▃▃██▇▆▃▃▅▄▃▂▂▂▂ ▃
- 200 μs Histogram: frequency by time 234 μs <
+ ▅▁█▆▃ ▁ ▁▁ ▁ ▁
+ ██████▇██▇████████████▇▇▆▇▆▆▅▆▆▅▅▅▅▆▅▆▅▅▆▅▅█▇▇█▆▅▂▄▃▅▅▄▄▄▃▅▄▄ █
+ 238 μs Histogram: log(frequency) by time 383 μs <
Memory estimate: 112 bytes, allocs estimate: 1.
@@ -1188,7 +1188,7 @@
fit!(m);
-[ Info: (ret = :ROUNDOFF_LIMITED, fevals = 550, minf = 2354.4744815688055)
+[ Info: (ret = :ROUNDOFF_LIMITED, fevals = 539, minf = 2354.4744815688023)
@@ -1202,7 +1202,7 @@ )
-Converged to θ = 0.5683043594028967 and β =[-0.3409777149845993, 0.3933796201906975, 0.6064857599227369, -0.012926172564277872, 0.03323478854784157, -0.005626184982660486]
+Converged to θ = 0.5683043828108765 and β =[-0.34097751800164144, 0.3933797121269934, 0.6064857490620532, -0.012926173063985653, 0.03323479562652116, -0.005626186061473733]
These estimates differ somewhat from those for model com05.
@@ -1219,7 +1219,7 @@ )
-Estimates for com05: θ = 0.5761507901895634, fmin = 2353.8241980539815, and β =[-0.3414913998306781, 0.3936080536502067, 0.6064861079468472, -0.012911714642169572, 0.03321662487439253, -0.005625046845040066]
+Estimates for com05: θ = 0.5761308007138215, fmin = 2353.8241975622855, and β =[-0.3414675499274024, 0.3936022945891755, 0.6064521635960723, -0.01291017393458232, 0.03321086562324279, -0.00562465124007457]
The discrepancy in the results is because the com05 results are based on a more accurate approximation to the integral called adaptive Gauss-Hermite Quadrature, which is discussed in Section C.6.
@@ -1448,7 +1448,7 @@
-
+
Figure C.3: Change in the penalized deviance contribution from that at the conditional mode, for each of the first 5 groups, in model com05, as a function of u.
@@ -1582,7 +1582,7 @@
-
+
Figure C.5: The difference between the contribution to the penalized deviance and its quadratic approximation for each of first 5 groups in model com05 as a function of z.
@@ -1616,7 +1616,7 @@
-
+
Figure C.6: Exponential of half the difference between the quadratic approximation and the contribution to the penalized deviance, for each of first 5 groups in model com05 as a function of z.
@@ -1667,11 +1667,11 @@
extrema(m.utbl.aGHQ)
-(-0.008514108854172236, -0.0004132698576107887)
+(-0.008514109511364184, -0.0004132697962170406)
As we see, these “correction terms” relative to Laplace’s approximation are relatively small, compared to the contributions to the objective from each component of \({\mathbf{u}}\). Also, the corrections are all negative, in this case. Close examination of the individual curves in Figure C.5 shows that these curves, which are \(-2\log(f_j(z))\), are more-or-less odd functions, in the sense that the value at \(-z\) is approximately the negative of the value at \(z\). If we were integrating \(\log(f_j(z_j))\phi(z_j)\) with a normalized Gauss-Hermite rule the negative and positive values would cancel out, for the most part, and some of the integrals would be positive while others would be negative.
@@ -1732,21 +1732,21 @@
7-element Vector{Float64}:
- 0.5761321679271924
- -0.3414655990254175
- 0.39359939391066806
- 0.6064447618771712
- -0.012909685721680265
- 0.03320994962034241
- -0.005624606329593786
+ 0.5761321422331179
+ -0.3414655647634521
+ 0.39359933036111444
+ 0.6064446768742858
+ -0.012909673161244401
+ 0.033209939096054776
+ -0.0056246062539383156
-
This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/datatables.html b/datatables.html
index db512e1..667b5ee 100644
--- a/datatables.html
+++ b/datatables.html
@@ -2,7 +2,7 @@
-
+
@@ -716,8 +716,8 @@ <
The JuliaData organization manages the development of several packages related to data science and data management, including DataFrames.jl, a comprehensive system for working with column-oriented data tables in Julia. Kamiński (2023), written by the primary author of that package, provides an in-depth introduction to data science facilities, in particular the DataFrames package, in Julia.
This package is particularly well-suited to more advanced data manipulation such as the split-apply-combine strategy (Wickham, 2011) and “joins” of data tables.
Bouchet-Valat & Kamiński (2023) compares the performance of DataFrames.jl to other data frame implementations in R and Python.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/glmmbernoulli.html b/glmmbernoulli.html
index 1bba742..e1712df 100644
--- a/glmmbernoulli.html
+++ b/glmmbernoulli.html
@@ -2,7 +2,7 @@
-
+
@@ -534,10 +534,10 @@
(Intercept)
--0.0126
+-0.0128
0.1058
-0.12
-0.9052
+0.9038
0.4787
@@ -545,12 +545,12 @@ 0.1532
0.0982
1.56
-0.1188
+0.1187
livch: 2
-0.2561
+0.2562
0.1045
2.45
0.0142
@@ -558,7 +558,7 @@
livch: 3+
-0.2590
+0.2591
0.1022
2.53
0.0113
@@ -568,15 +568,15 @@ age
0.0006
0.0095
-0.07
-0.9466
+0.06
+0.9483
abs2(age)
-0.0047
0.0008
--6.13
+-6.12
<1e-09
@@ -593,7 +593,7 @@ -0.0067
0.0069
-0.98
-0.3261
+0.3257
@@ -601,7 +601,7 @@ -0.0004
0.0007
-0.51
-0.6075
+0.6079
@@ -768,7 +768,7 @@ -0.2194
0.1155
-1.90
-0.0576
+0.0575
0.4773
@@ -784,12 +784,12 @@ 0.0035
0.0082
0.43
-0.6674
+0.6673
urban: Y
-0.3734
+0.3733
0.0801
4.66
<1e-05
@@ -808,7 +808,7 @@ -0.0068
0.0069
-0.99
-0.3231
+0.3227
@@ -816,7 +816,7 @@ -0.0004
0.0007
-0.49
-0.6261
+0.6272
@@ -880,7 +880,7 @@
(Intercept)
--0.2302
+-0.2303
0.1140
-2.02
0.0434
@@ -888,7 +888,7 @@
urban: Y
-0.3461
+0.3462
0.0599
5.78
<1e-08
@@ -907,7 +907,7 @@ 0.0063
0.0078
0.80
-0.4249
+0.4247
@@ -1034,7 +1034,7 @@ -0.0131
0.0110
-1.19
-0.2351
+0.2353
@@ -1131,7 +1131,7 @@
ch: true
-0.6065
+0.6064
0.1045
5.80
<1e-08
@@ -1142,7 +1142,7 @@ -0.0129
0.0112
-1.16
-0.2470
+0.2471
@@ -1182,21 +1182,21 @@ Table with 4 columns and 44 rows:
age ch urban η
┌─────────────────────────────
- 1 │ -10 false N -1.44276
+ 1 │ -10 false N -1.44278
2 │ -7 false N -1.29428
- 3 │ -4 false N -1.24704
- 4 │ -1 false N -1.30104
- 5 │ 2 false N -1.45629
- 6 │ 5 false N -1.71278
- 7 │ 8 false N -2.07051
- 8 │ 11 false N -2.52948
- 9 │ 14 false N -3.0897
- 10 │ 17 false N -3.75115
- 11 │ 20 false N -4.51385
- 12 │ -10 true N -0.894068
- 13 │ -7 true N -0.546316
- 14 │ -4 true N -0.299807
- 15 │ -1 true N -0.15454
- 16 │ 2 true N -0.110516
- 17 │ 5 true N -0.167734
+ 3 │ -4 false N -1.24703
+ 4 │ -1 false N -1.30102
+ 5 │ 2 false N -1.45626
+ 6 │ 5 false N -1.71273
+ 7 │ 8 false N -2.07045
+ 8 │ 11 false N -2.52941
+ 9 │ 14 false N -3.08962
+ 10 │ 17 false N -3.75107
+ 11 │ 20 false N -4.51376
+ 12 │ -10 true N -0.894085
+ 13 │ -7 true N -0.546331
+ 14 │ -4 true N -0.299819
+ 15 │ -1 true N -0.15455
+ 16 │ 2 true N -0.110524
+ 17 │ 5 true N -0.167741
⋮ │ ⋮ ⋮ ⋮ ⋮
@@ -1346,7 +1346,7 @@
-
+
Figure 6.3: Linear predictor versus centered age from model com05
@@ -1411,23 +1411,23 @@ <
Table with 5 columns and 44 rows:
age ch urban η μ
┌────────────────────────────────────────
- 1 │ -10 false N -1.44276 0.191118
+ 1 │ -10 false N -1.44278 0.191116
2 │ -7 false N -1.29428 0.215129
- 3 │ -4 false N -1.24704 0.223213
- 4 │ -1 false N -1.30104 0.213989
- 5 │ 2 false N -1.45629 0.189035
- 6 │ 5 false N -1.71278 0.152804
- 7 │ 8 false N -2.07051 0.111996
- 8 │ 11 false N -2.52948 0.0738171
- 9 │ 14 false N -3.0897 0.0435342
- 10 │ 17 false N -3.75115 0.0229515
- 11 │ 20 false N -4.51385 0.0108374
- 12 │ -10 true N -0.894068 0.290271
- 13 │ -7 true N -0.546316 0.366719
- 14 │ -4 true N -0.299807 0.425605
- 15 │ -1 true N -0.15454 0.461442
- 16 │ 2 true N -0.110516 0.472399
- 17 │ 5 true N -0.167734 0.458164
+ 3 │ -4 false N -1.24703 0.223215
+ 4 │ -1 false N -1.30102 0.213993
+ 5 │ 2 false N -1.45626 0.189041
+ 6 │ 5 false N -1.71273 0.15281
+ 7 │ 8 false N -2.07045 0.112002
+ 8 │ 11 false N -2.52941 0.0738216
+ 9 │ 14 false N -3.08962 0.0435374
+ 10 │ 17 false N -3.75107 0.0229534
+ 11 │ 20 false N -4.51376 0.0108384
+ 12 │ -10 true N -0.894085 0.290267
+ 13 │ -7 true N -0.546331 0.366716
+ 14 │ -4 true N -0.299819 0.425602
+ 15 │ -1 true N -0.15455 0.461439
+ 16 │ 2 true N -0.110524 0.472397
+ 17 │ 5 true N -0.167741 0.458163
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
@@ -1450,7 +1450,7 @@ <
-
+
Figure 6.5: Predicted probability of contraception use versus centered age from model com05.
@@ -1466,10 +1466,10 @@ <
Table with 5 columns and 4 rows:
age ch urban η μ
┌───────────────────────────────────────
- 1 │ 2 false N -1.45629 0.189035
- 2 │ 2 true N -0.110516 0.472399
- 3 │ 2 false Y -0.669101 0.338698
- 4 │ 2 true Y 0.676673 0.662996
+ 1 │ 2 false N -1.45626 0.189041
+ 2 │ 2 true N -0.110524 0.472397
+ 3 │ 2 false Y -0.669056 0.338708
+ 4 │ 2 true Y 0.676675 0.662996
The predicted probability of woman with centered age of 2, with children, living in an urban environment using artificial contraception is about 2/3, which is reasonably close to the smoothed frequency for that combination of covariates in Figure 6.2.
@@ -1487,7 +1487,7 @@
-
+
Figure 6.6: Caterpillar plot of the conditional modes of the random-effects for model com05
@@ -1506,12 +1506,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D01", "N") -0.957366
- 2 │ ("D11", "N") -0.93198
- 3 │ ("D24", "N") -0.603418
- 4 │ ("D01", "Y") -0.580203
- 5 │ ("D27", "N") -0.576647
- 6 │ ("D55", "Y") -0.548261
+ 1 │ ("D01", "N") -0.95737
+ 2 │ ("D11", "N") -0.931988
+ 3 │ ("D24", "N") -0.603424
+ 4 │ ("D01", "Y") -0.580204
+ 5 │ ("D27", "N") -0.57665
+ 6 │ ("D55", "Y") -0.548267
@@ -1520,12 +1520,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D43", "N") 0.64408
- 2 │ ("D04", "Y") 0.644669
- 3 │ ("D42", "N") 0.665114
- 4 │ ("D58", "N") 0.677418
- 5 │ ("D14", "Y") 0.695322
- 6 │ ("D34", "N") 1.08591
+ 1 │ ("D43", "N") 0.644084
+ 2 │ ("D04", "Y") 0.644675
+ 3 │ ("D42", "N") 0.665122
+ 4 │ ("D58", "N") 0.677423
+ 5 │ ("D14", "Y") 0.695323
+ 6 │ ("D34", "N") 1.08592
The largest random effect is for rural settings in D34. There were 26 women in the sample from rural D34
@@ -1565,12 +1565,12 @@
exp(last(first(srtdre)))
-0.38390287360272524
+0.3839013481790906
or about 40%.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/index.html b/index.html
index efb2049..56480db 100644
--- a/index.html
+++ b/index.html
@@ -2,14 +2,14 @@
-
+
-
+
Embrace Uncertainty
fit!(com05fe, β₀);[ Info: (0, 2491.1514390537254)
-[ Info: (1, 2411.165742459929)
+[ Info: (1, 2411.1657424599293)
[ Info: (2, 2409.382451337132)
-[ Info: (3, 2409.3774282835857)
+[ Info: (3, 2409.377428283585)
[ Info: (4, 2409.3774281600413)@benchmark deviance(updateβ!(m)) seconds = 1 setup = (m = com05fe)
-
@@ -1052,14 +1052,14 @@ BenchmarkTools.Trial: 10000 samples with 1 evaluation.
- Range (min … max): 67.528 μs … 161.118 μs ┊ GC (min … max): 0.00% … 0.00%
- Time (median): 76.638 μs ┊ GC (median): 0.00%
- Time (mean ± σ): 76.986 μs ± 2.640 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
+BenchmarkTools.Trial: 7372 samples with 1 evaluation.
+ Range (min … max): 126.577 μs … 294.077 μs ┊ GC (min … max): 0.00% … 0.00%
+ Time (median): 130.825 μs ┊ GC (median): 0.00%
+ Time (mean ± σ): 133.084 μs ± 12.228 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
- ▂▂▂▄▅▅▄▇██▆▅▄▄▅▅▄▃▂▂▂▂▂▁▁▁ ▂
- ▅▅▆▆▇▇▇▆▆▆▆▆▆▆▅▆▇▇▇██▇██████████████████████████████▇▇▇▇▇▇▇▇ █
- 67.5 μs Histogram: log(frequency) by time 84.5 μs <
+ ▇▇▇▆█▇▄▄▃▂▂▁ ▂
+ ██████████████▇█▇▇▇▇▇▇▆▆▅▅▆▆▆▆▂▅▄▅▄▅▆▅▆▆▅▅▆▅▇▅▅▆▆▅▅▅▆▆▆▄▅▄▄▅▄ █
+ 127 μs Histogram: log(frequency) by time 189 μs <
Memory estimate: 16.12 KiB, allocs estimate: 6.
m = BernoulliPIRLS(com05.X, com05.y, only(com05.reterms).refs)
pdeviance(m)
-2409.377428160041
+2409.3774281600413
As with IRLS, the first iteration of PIRLS reduces the objective, which is the penalized deviance in this case, substantially.
pdeviance(updateu!(m))
-2233.1209476357844
+2233.1209476357853
Create a pirls! method for this struct.
@@ -1094,9 +1094,9 @@
pirls!(m; verbose=true);
-[ Info: (0, 2409.377428160041)
-[ Info: (1, 2233.1209476357844)
-[ Info: (2, 2231.605935279706)
+[ Info: (0, 2409.3774281600413)
+[ Info: (1, 2233.1209476357853)
+[ Info: (2, 2231.6059352797065)
[ Info: (3, 2231.6002198321576)
[ Info: (4, 2231.600219406561)
@@ -1105,14 +1105,14 @@
@benchmark pirls!(mm) seconds = 1 setup = (mm = m)
-BenchmarkTools.Trial: 4402 samples with 1 evaluation.
- Range (min … max): 199.693 μs … 331.599 μs ┊ GC (min … max): 0.00% … 0.00%
- Time (median): 227.186 μs ┊ GC (median): 0.00%
- Time (mean ± σ): 226.091 μs ± 5.569 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
+BenchmarkTools.Trial: 3848 samples with 1 evaluation.
+ Range (min … max): 237.623 μs … 461.619 μs ┊ GC (min … max): 0.00% … 0.00%
+ Time (median): 243.844 μs ┊ GC (median): 0.00%
+ Time (mean ± σ): 257.466 μs ± 31.167 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
- ▁ ▇█
- ▂▂▂▂▁▂▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▁▂▂▁▁▂▂▂▂▁▂▁▁▁▁▄█▄▄▃▂▃▃▃▃▃██▇▆▃▃▅▄▃▂▂▂▂ ▃
- 200 μs Histogram: frequency by time 234 μs <
+ ▅▁█▆▃ ▁ ▁▁ ▁ ▁
+ ██████▇██▇████████████▇▇▆▇▆▆▅▆▆▅▅▅▅▆▅▆▅▅▆▅▅█▇▇█▆▅▂▄▃▅▅▄▄▄▃▅▄▄ █
+ 238 μs Histogram: log(frequency) by time 383 μs <
Memory estimate: 112 bytes, allocs estimate: 1.
@@ -1188,7 +1188,7 @@
fit!(m);
-[ Info: (ret = :ROUNDOFF_LIMITED, fevals = 550, minf = 2354.4744815688055)
+[ Info: (ret = :ROUNDOFF_LIMITED, fevals = 539, minf = 2354.4744815688023)
@@ -1202,7 +1202,7 @@ )
-Converged to θ = 0.5683043594028967 and β =[-0.3409777149845993, 0.3933796201906975, 0.6064857599227369, -0.012926172564277872, 0.03323478854784157, -0.005626184982660486]
+Converged to θ = 0.5683043828108765 and β =[-0.34097751800164144, 0.3933797121269934, 0.6064857490620532, -0.012926173063985653, 0.03323479562652116, -0.005626186061473733]
These estimates differ somewhat from those for model com05.
@@ -1219,7 +1219,7 @@ )
-Estimates for com05: θ = 0.5761507901895634, fmin = 2353.8241980539815, and β =[-0.3414913998306781, 0.3936080536502067, 0.6064861079468472, -0.012911714642169572, 0.03321662487439253, -0.005625046845040066]
+Estimates for com05: θ = 0.5761308007138215, fmin = 2353.8241975622855, and β =[-0.3414675499274024, 0.3936022945891755, 0.6064521635960723, -0.01291017393458232, 0.03321086562324279, -0.00562465124007457]
The discrepancy in the results is because the com05 results are based on a more accurate approximation to the integral called adaptive Gauss-Hermite Quadrature, which is discussed in Section C.6.
@@ -1448,7 +1448,7 @@
-
+
Figure C.3: Change in the penalized deviance contribution from that at the conditional mode, for each of the first 5 groups, in model com05, as a function of u.
@@ -1582,7 +1582,7 @@
-
+
Figure C.5: The difference between the contribution to the penalized deviance and its quadratic approximation for each of first 5 groups in model com05 as a function of z.
@@ -1616,7 +1616,7 @@
-
+
Figure C.6: Exponential of half the difference between the quadratic approximation and the contribution to the penalized deviance, for each of first 5 groups in model com05 as a function of z.
@@ -1667,11 +1667,11 @@
extrema(m.utbl.aGHQ)
-(-0.008514108854172236, -0.0004132698576107887)
+(-0.008514109511364184, -0.0004132697962170406)
As we see, these “correction terms” relative to Laplace’s approximation are relatively small, compared to the contributions to the objective from each component of \({\mathbf{u}}\). Also, the corrections are all negative, in this case. Close examination of the individual curves in Figure C.5 shows that these curves, which are \(-2\log(f_j(z))\), are more-or-less odd functions, in the sense that the value at \(-z\) is approximately the negative of the value at \(z\). If we were integrating \(\log(f_j(z_j))\phi(z_j)\) with a normalized Gauss-Hermite rule the negative and positive values would cancel out, for the most part, and some of the integrals would be positive while others would be negative.
@@ -1732,21 +1732,21 @@
7-element Vector{Float64}:
- 0.5761321679271924
- -0.3414655990254175
- 0.39359939391066806
- 0.6064447618771712
- -0.012909685721680265
- 0.03320994962034241
- -0.005624606329593786
+ 0.5761321422331179
+ -0.3414655647634521
+ 0.39359933036111444
+ 0.6064446768742858
+ -0.012909673161244401
+ 0.033209939096054776
+ -0.0056246062539383156
-
This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/datatables.html b/datatables.html
index db512e1..667b5ee 100644
--- a/datatables.html
+++ b/datatables.html
@@ -2,7 +2,7 @@
-
+
@@ -716,8 +716,8 @@ <
The JuliaData organization manages the development of several packages related to data science and data management, including DataFrames.jl, a comprehensive system for working with column-oriented data tables in Julia. Kamiński (2023), written by the primary author of that package, provides an in-depth introduction to data science facilities, in particular the DataFrames package, in Julia.
This package is particularly well-suited to more advanced data manipulation such as the split-apply-combine strategy (Wickham, 2011) and “joins” of data tables.
Bouchet-Valat & Kamiński (2023) compares the performance of DataFrames.jl to other data frame implementations in R and Python.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/glmmbernoulli.html b/glmmbernoulli.html
index 1bba742..e1712df 100644
--- a/glmmbernoulli.html
+++ b/glmmbernoulli.html
@@ -2,7 +2,7 @@
-
+
@@ -534,10 +534,10 @@
(Intercept)
--0.0126
+-0.0128
0.1058
-0.12
-0.9052
+0.9038
0.4787
@@ -545,12 +545,12 @@ 0.1532
0.0982
1.56
-0.1188
+0.1187
livch: 2
-0.2561
+0.2562
0.1045
2.45
0.0142
@@ -558,7 +558,7 @@
livch: 3+
-0.2590
+0.2591
0.1022
2.53
0.0113
@@ -568,15 +568,15 @@ age
0.0006
0.0095
-0.07
-0.9466
+0.06
+0.9483
abs2(age)
-0.0047
0.0008
--6.13
+-6.12
<1e-09
@@ -593,7 +593,7 @@ -0.0067
0.0069
-0.98
-0.3261
+0.3257
@@ -601,7 +601,7 @@ -0.0004
0.0007
-0.51
-0.6075
+0.6079
@@ -768,7 +768,7 @@ -0.2194
0.1155
-1.90
-0.0576
+0.0575
0.4773
@@ -784,12 +784,12 @@ 0.0035
0.0082
0.43
-0.6674
+0.6673
urban: Y
-0.3734
+0.3733
0.0801
4.66
<1e-05
@@ -808,7 +808,7 @@ -0.0068
0.0069
-0.99
-0.3231
+0.3227
@@ -816,7 +816,7 @@ -0.0004
0.0007
-0.49
-0.6261
+0.6272
@@ -880,7 +880,7 @@
(Intercept)
--0.2302
+-0.2303
0.1140
-2.02
0.0434
@@ -888,7 +888,7 @@
urban: Y
-0.3461
+0.3462
0.0599
5.78
<1e-08
@@ -907,7 +907,7 @@ 0.0063
0.0078
0.80
-0.4249
+0.4247
@@ -1034,7 +1034,7 @@ -0.0131
0.0110
-1.19
-0.2351
+0.2353
@@ -1131,7 +1131,7 @@
ch: true
-0.6065
+0.6064
0.1045
5.80
<1e-08
@@ -1142,7 +1142,7 @@ -0.0129
0.0112
-1.16
-0.2470
+0.2471
@@ -1182,21 +1182,21 @@ Table with 4 columns and 44 rows:
age ch urban η
┌─────────────────────────────
- 1 │ -10 false N -1.44276
+ 1 │ -10 false N -1.44278
2 │ -7 false N -1.29428
- 3 │ -4 false N -1.24704
- 4 │ -1 false N -1.30104
- 5 │ 2 false N -1.45629
- 6 │ 5 false N -1.71278
- 7 │ 8 false N -2.07051
- 8 │ 11 false N -2.52948
- 9 │ 14 false N -3.0897
- 10 │ 17 false N -3.75115
- 11 │ 20 false N -4.51385
- 12 │ -10 true N -0.894068
- 13 │ -7 true N -0.546316
- 14 │ -4 true N -0.299807
- 15 │ -1 true N -0.15454
- 16 │ 2 true N -0.110516
- 17 │ 5 true N -0.167734
+ 3 │ -4 false N -1.24703
+ 4 │ -1 false N -1.30102
+ 5 │ 2 false N -1.45626
+ 6 │ 5 false N -1.71273
+ 7 │ 8 false N -2.07045
+ 8 │ 11 false N -2.52941
+ 9 │ 14 false N -3.08962
+ 10 │ 17 false N -3.75107
+ 11 │ 20 false N -4.51376
+ 12 │ -10 true N -0.894085
+ 13 │ -7 true N -0.546331
+ 14 │ -4 true N -0.299819
+ 15 │ -1 true N -0.15455
+ 16 │ 2 true N -0.110524
+ 17 │ 5 true N -0.167741
⋮ │ ⋮ ⋮ ⋮ ⋮
@@ -1346,7 +1346,7 @@
-
+
Figure 6.3: Linear predictor versus centered age from model com05
@@ -1411,23 +1411,23 @@ <
Table with 5 columns and 44 rows:
age ch urban η μ
┌────────────────────────────────────────
- 1 │ -10 false N -1.44276 0.191118
+ 1 │ -10 false N -1.44278 0.191116
2 │ -7 false N -1.29428 0.215129
- 3 │ -4 false N -1.24704 0.223213
- 4 │ -1 false N -1.30104 0.213989
- 5 │ 2 false N -1.45629 0.189035
- 6 │ 5 false N -1.71278 0.152804
- 7 │ 8 false N -2.07051 0.111996
- 8 │ 11 false N -2.52948 0.0738171
- 9 │ 14 false N -3.0897 0.0435342
- 10 │ 17 false N -3.75115 0.0229515
- 11 │ 20 false N -4.51385 0.0108374
- 12 │ -10 true N -0.894068 0.290271
- 13 │ -7 true N -0.546316 0.366719
- 14 │ -4 true N -0.299807 0.425605
- 15 │ -1 true N -0.15454 0.461442
- 16 │ 2 true N -0.110516 0.472399
- 17 │ 5 true N -0.167734 0.458164
+ 3 │ -4 false N -1.24703 0.223215
+ 4 │ -1 false N -1.30102 0.213993
+ 5 │ 2 false N -1.45626 0.189041
+ 6 │ 5 false N -1.71273 0.15281
+ 7 │ 8 false N -2.07045 0.112002
+ 8 │ 11 false N -2.52941 0.0738216
+ 9 │ 14 false N -3.08962 0.0435374
+ 10 │ 17 false N -3.75107 0.0229534
+ 11 │ 20 false N -4.51376 0.0108384
+ 12 │ -10 true N -0.894085 0.290267
+ 13 │ -7 true N -0.546331 0.366716
+ 14 │ -4 true N -0.299819 0.425602
+ 15 │ -1 true N -0.15455 0.461439
+ 16 │ 2 true N -0.110524 0.472397
+ 17 │ 5 true N -0.167741 0.458163
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
@@ -1450,7 +1450,7 @@ <
-
+
Figure 6.5: Predicted probability of contraception use versus centered age from model com05.
@@ -1466,10 +1466,10 @@ <
Table with 5 columns and 4 rows:
age ch urban η μ
┌───────────────────────────────────────
- 1 │ 2 false N -1.45629 0.189035
- 2 │ 2 true N -0.110516 0.472399
- 3 │ 2 false Y -0.669101 0.338698
- 4 │ 2 true Y 0.676673 0.662996
+ 1 │ 2 false N -1.45626 0.189041
+ 2 │ 2 true N -0.110524 0.472397
+ 3 │ 2 false Y -0.669056 0.338708
+ 4 │ 2 true Y 0.676675 0.662996
The predicted probability of woman with centered age of 2, with children, living in an urban environment using artificial contraception is about 2/3, which is reasonably close to the smoothed frequency for that combination of covariates in Figure 6.2.
@@ -1487,7 +1487,7 @@
-
+
Figure 6.6: Caterpillar plot of the conditional modes of the random-effects for model com05
@@ -1506,12 +1506,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D01", "N") -0.957366
- 2 │ ("D11", "N") -0.93198
- 3 │ ("D24", "N") -0.603418
- 4 │ ("D01", "Y") -0.580203
- 5 │ ("D27", "N") -0.576647
- 6 │ ("D55", "Y") -0.548261
+ 1 │ ("D01", "N") -0.95737
+ 2 │ ("D11", "N") -0.931988
+ 3 │ ("D24", "N") -0.603424
+ 4 │ ("D01", "Y") -0.580204
+ 5 │ ("D27", "N") -0.57665
+ 6 │ ("D55", "Y") -0.548267
@@ -1520,12 +1520,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D43", "N") 0.64408
- 2 │ ("D04", "Y") 0.644669
- 3 │ ("D42", "N") 0.665114
- 4 │ ("D58", "N") 0.677418
- 5 │ ("D14", "Y") 0.695322
- 6 │ ("D34", "N") 1.08591
+ 1 │ ("D43", "N") 0.644084
+ 2 │ ("D04", "Y") 0.644675
+ 3 │ ("D42", "N") 0.665122
+ 4 │ ("D58", "N") 0.677423
+ 5 │ ("D14", "Y") 0.695323
+ 6 │ ("D34", "N") 1.08592
The largest random effect is for rural settings in D34. There were 26 women in the sample from rural D34
@@ -1565,12 +1565,12 @@
exp(last(first(srtdre)))
-0.38390287360272524
+0.3839013481790906
or about 40%.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/index.html b/index.html
index efb2049..56480db 100644
--- a/index.html
+++ b/index.html
@@ -2,14 +2,14 @@
-
+
-
+
Embrace Uncertainty
m = BernoulliPIRLS(com05.X, com05.y, only(com05.reterms).refs)
pdeviance(m)2409.3774281600412409.3774281600413pdeviance(updateu!(m))
-
2233.12094763578442233.1209476357853pirls! method for this struct.pirls!(m; verbose=true);
-
@@ -1105,14 +1105,14 @@ [ Info: (0, 2409.377428160041)
-[ Info: (1, 2233.1209476357844)
-[ Info: (2, 2231.605935279706)
+[ Info: (0, 2409.3774281600413)
+[ Info: (1, 2233.1209476357853)
+[ Info: (2, 2231.6059352797065)
[ Info: (3, 2231.6002198321576)
[ Info: (4, 2231.600219406561)
@benchmark pirls!(mm) seconds = 1 setup = (mm = m)
-BenchmarkTools.Trial: 4402 samples with 1 evaluation.
- Range (min … max): 199.693 μs … 331.599 μs ┊ GC (min … max): 0.00% … 0.00%
- Time (median): 227.186 μs ┊ GC (median): 0.00%
- Time (mean ± σ): 226.091 μs ± 5.569 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
+BenchmarkTools.Trial: 3848 samples with 1 evaluation.
+ Range (min … max): 237.623 μs … 461.619 μs ┊ GC (min … max): 0.00% … 0.00%
+ Time (median): 243.844 μs ┊ GC (median): 0.00%
+ Time (mean ± σ): 257.466 μs ± 31.167 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
- ▁ ▇█
- ▂▂▂▂▁▂▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▁▂▂▁▁▂▂▂▂▁▂▁▁▁▁▄█▄▄▃▂▃▃▃▃▃██▇▆▃▃▅▄▃▂▂▂▂ ▃
- 200 μs Histogram: frequency by time 234 μs <
+ ▅▁█▆▃ ▁ ▁▁ ▁ ▁
+ ██████▇██▇████████████▇▇▆▇▆▆▅▆▆▅▅▅▅▆▅▆▅▅▆▅▅█▇▇█▆▅▂▄▃▅▅▄▄▄▃▅▄▄ █
+ 238 μs Histogram: log(frequency) by time 383 μs <
Memory estimate: 112 bytes, allocs estimate: 1.
@@ -1188,7 +1188,7 @@
fit!(m);
-[ Info: (ret = :ROUNDOFF_LIMITED, fevals = 550, minf = 2354.4744815688055)
+[ Info: (ret = :ROUNDOFF_LIMITED, fevals = 539, minf = 2354.4744815688023)
@@ -1202,7 +1202,7 @@ )
-Converged to θ = 0.5683043594028967 and β =[-0.3409777149845993, 0.3933796201906975, 0.6064857599227369, -0.012926172564277872, 0.03323478854784157, -0.005626184982660486]
+Converged to θ = 0.5683043828108765 and β =[-0.34097751800164144, 0.3933797121269934, 0.6064857490620532, -0.012926173063985653, 0.03323479562652116, -0.005626186061473733]
These estimates differ somewhat from those for model com05.
@@ -1219,7 +1219,7 @@ )
-Estimates for com05: θ = 0.5761507901895634, fmin = 2353.8241980539815, and β =[-0.3414913998306781, 0.3936080536502067, 0.6064861079468472, -0.012911714642169572, 0.03321662487439253, -0.005625046845040066]
+Estimates for com05: θ = 0.5761308007138215, fmin = 2353.8241975622855, and β =[-0.3414675499274024, 0.3936022945891755, 0.6064521635960723, -0.01291017393458232, 0.03321086562324279, -0.00562465124007457]
The discrepancy in the results is because the com05 results are based on a more accurate approximation to the integral called adaptive Gauss-Hermite Quadrature, which is discussed in Section C.6.
@@ -1448,7 +1448,7 @@
-
+
Figure C.3: Change in the penalized deviance contribution from that at the conditional mode, for each of the first 5 groups, in model com05, as a function of u.
@@ -1582,7 +1582,7 @@
-
+
Figure C.5: The difference between the contribution to the penalized deviance and its quadratic approximation for each of first 5 groups in model com05 as a function of z.
@@ -1616,7 +1616,7 @@
-
+
Figure C.6: Exponential of half the difference between the quadratic approximation and the contribution to the penalized deviance, for each of first 5 groups in model com05 as a function of z.
@@ -1667,11 +1667,11 @@
extrema(m.utbl.aGHQ)
-(-0.008514108854172236, -0.0004132698576107887)
+(-0.008514109511364184, -0.0004132697962170406)
As we see, these “correction terms” relative to Laplace’s approximation are relatively small, compared to the contributions to the objective from each component of \({\mathbf{u}}\). Also, the corrections are all negative, in this case. Close examination of the individual curves in Figure C.5 shows that these curves, which are \(-2\log(f_j(z))\), are more-or-less odd functions, in the sense that the value at \(-z\) is approximately the negative of the value at \(z\). If we were integrating \(\log(f_j(z_j))\phi(z_j)\) with a normalized Gauss-Hermite rule the negative and positive values would cancel out, for the most part, and some of the integrals would be positive while others would be negative.
@@ -1732,21 +1732,21 @@
7-element Vector{Float64}:
- 0.5761321679271924
- -0.3414655990254175
- 0.39359939391066806
- 0.6064447618771712
- -0.012909685721680265
- 0.03320994962034241
- -0.005624606329593786
+ 0.5761321422331179
+ -0.3414655647634521
+ 0.39359933036111444
+ 0.6064446768742858
+ -0.012909673161244401
+ 0.033209939096054776
+ -0.0056246062539383156
-
This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/datatables.html b/datatables.html
index db512e1..667b5ee 100644
--- a/datatables.html
+++ b/datatables.html
@@ -2,7 +2,7 @@
-
+
@@ -716,8 +716,8 @@ <
The JuliaData organization manages the development of several packages related to data science and data management, including DataFrames.jl, a comprehensive system for working with column-oriented data tables in Julia. Kamiński (2023), written by the primary author of that package, provides an in-depth introduction to data science facilities, in particular the DataFrames package, in Julia.
This package is particularly well-suited to more advanced data manipulation such as the split-apply-combine strategy (Wickham, 2011) and “joins” of data tables.
Bouchet-Valat & Kamiński (2023) compares the performance of DataFrames.jl to other data frame implementations in R and Python.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/glmmbernoulli.html b/glmmbernoulli.html
index 1bba742..e1712df 100644
--- a/glmmbernoulli.html
+++ b/glmmbernoulli.html
@@ -2,7 +2,7 @@
-
+
@@ -534,10 +534,10 @@
(Intercept)
--0.0126
+-0.0128
0.1058
-0.12
-0.9052
+0.9038
0.4787
@@ -545,12 +545,12 @@ 0.1532
0.0982
1.56
-0.1188
+0.1187
livch: 2
-0.2561
+0.2562
0.1045
2.45
0.0142
@@ -558,7 +558,7 @@
livch: 3+
-0.2590
+0.2591
0.1022
2.53
0.0113
@@ -568,15 +568,15 @@ age
0.0006
0.0095
-0.07
-0.9466
+0.06
+0.9483
abs2(age)
-0.0047
0.0008
--6.13
+-6.12
<1e-09
@@ -593,7 +593,7 @@ -0.0067
0.0069
-0.98
-0.3261
+0.3257
@@ -601,7 +601,7 @@ -0.0004
0.0007
-0.51
-0.6075
+0.6079
@@ -768,7 +768,7 @@ -0.2194
0.1155
-1.90
-0.0576
+0.0575
0.4773
@@ -784,12 +784,12 @@ 0.0035
0.0082
0.43
-0.6674
+0.6673
urban: Y
-0.3734
+0.3733
0.0801
4.66
<1e-05
@@ -808,7 +808,7 @@ -0.0068
0.0069
-0.99
-0.3231
+0.3227
@@ -816,7 +816,7 @@ -0.0004
0.0007
-0.49
-0.6261
+0.6272
@@ -880,7 +880,7 @@
(Intercept)
--0.2302
+-0.2303
0.1140
-2.02
0.0434
@@ -888,7 +888,7 @@
urban: Y
-0.3461
+0.3462
0.0599
5.78
<1e-08
@@ -907,7 +907,7 @@ 0.0063
0.0078
0.80
-0.4249
+0.4247
@@ -1034,7 +1034,7 @@ -0.0131
0.0110
-1.19
-0.2351
+0.2353
@@ -1131,7 +1131,7 @@
ch: true
-0.6065
+0.6064
0.1045
5.80
<1e-08
@@ -1142,7 +1142,7 @@ -0.0129
0.0112
-1.16
-0.2470
+0.2471
@@ -1182,21 +1182,21 @@ Table with 4 columns and 44 rows:
age ch urban η
┌─────────────────────────────
- 1 │ -10 false N -1.44276
+ 1 │ -10 false N -1.44278
2 │ -7 false N -1.29428
- 3 │ -4 false N -1.24704
- 4 │ -1 false N -1.30104
- 5 │ 2 false N -1.45629
- 6 │ 5 false N -1.71278
- 7 │ 8 false N -2.07051
- 8 │ 11 false N -2.52948
- 9 │ 14 false N -3.0897
- 10 │ 17 false N -3.75115
- 11 │ 20 false N -4.51385
- 12 │ -10 true N -0.894068
- 13 │ -7 true N -0.546316
- 14 │ -4 true N -0.299807
- 15 │ -1 true N -0.15454
- 16 │ 2 true N -0.110516
- 17 │ 5 true N -0.167734
+ 3 │ -4 false N -1.24703
+ 4 │ -1 false N -1.30102
+ 5 │ 2 false N -1.45626
+ 6 │ 5 false N -1.71273
+ 7 │ 8 false N -2.07045
+ 8 │ 11 false N -2.52941
+ 9 │ 14 false N -3.08962
+ 10 │ 17 false N -3.75107
+ 11 │ 20 false N -4.51376
+ 12 │ -10 true N -0.894085
+ 13 │ -7 true N -0.546331
+ 14 │ -4 true N -0.299819
+ 15 │ -1 true N -0.15455
+ 16 │ 2 true N -0.110524
+ 17 │ 5 true N -0.167741
⋮ │ ⋮ ⋮ ⋮ ⋮
@@ -1346,7 +1346,7 @@
-
+
Figure 6.3: Linear predictor versus centered age from model com05
@@ -1411,23 +1411,23 @@ <
Table with 5 columns and 44 rows:
age ch urban η μ
┌────────────────────────────────────────
- 1 │ -10 false N -1.44276 0.191118
+ 1 │ -10 false N -1.44278 0.191116
2 │ -7 false N -1.29428 0.215129
- 3 │ -4 false N -1.24704 0.223213
- 4 │ -1 false N -1.30104 0.213989
- 5 │ 2 false N -1.45629 0.189035
- 6 │ 5 false N -1.71278 0.152804
- 7 │ 8 false N -2.07051 0.111996
- 8 │ 11 false N -2.52948 0.0738171
- 9 │ 14 false N -3.0897 0.0435342
- 10 │ 17 false N -3.75115 0.0229515
- 11 │ 20 false N -4.51385 0.0108374
- 12 │ -10 true N -0.894068 0.290271
- 13 │ -7 true N -0.546316 0.366719
- 14 │ -4 true N -0.299807 0.425605
- 15 │ -1 true N -0.15454 0.461442
- 16 │ 2 true N -0.110516 0.472399
- 17 │ 5 true N -0.167734 0.458164
+ 3 │ -4 false N -1.24703 0.223215
+ 4 │ -1 false N -1.30102 0.213993
+ 5 │ 2 false N -1.45626 0.189041
+ 6 │ 5 false N -1.71273 0.15281
+ 7 │ 8 false N -2.07045 0.112002
+ 8 │ 11 false N -2.52941 0.0738216
+ 9 │ 14 false N -3.08962 0.0435374
+ 10 │ 17 false N -3.75107 0.0229534
+ 11 │ 20 false N -4.51376 0.0108384
+ 12 │ -10 true N -0.894085 0.290267
+ 13 │ -7 true N -0.546331 0.366716
+ 14 │ -4 true N -0.299819 0.425602
+ 15 │ -1 true N -0.15455 0.461439
+ 16 │ 2 true N -0.110524 0.472397
+ 17 │ 5 true N -0.167741 0.458163
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
@@ -1450,7 +1450,7 @@ <
-
+
Figure 6.5: Predicted probability of contraception use versus centered age from model com05.
@@ -1466,10 +1466,10 @@ <
Table with 5 columns and 4 rows:
age ch urban η μ
┌───────────────────────────────────────
- 1 │ 2 false N -1.45629 0.189035
- 2 │ 2 true N -0.110516 0.472399
- 3 │ 2 false Y -0.669101 0.338698
- 4 │ 2 true Y 0.676673 0.662996
+ 1 │ 2 false N -1.45626 0.189041
+ 2 │ 2 true N -0.110524 0.472397
+ 3 │ 2 false Y -0.669056 0.338708
+ 4 │ 2 true Y 0.676675 0.662996
The predicted probability of woman with centered age of 2, with children, living in an urban environment using artificial contraception is about 2/3, which is reasonably close to the smoothed frequency for that combination of covariates in Figure 6.2.
@@ -1487,7 +1487,7 @@
-
+
Figure 6.6: Caterpillar plot of the conditional modes of the random-effects for model com05
@@ -1506,12 +1506,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D01", "N") -0.957366
- 2 │ ("D11", "N") -0.93198
- 3 │ ("D24", "N") -0.603418
- 4 │ ("D01", "Y") -0.580203
- 5 │ ("D27", "N") -0.576647
- 6 │ ("D55", "Y") -0.548261
+ 1 │ ("D01", "N") -0.95737
+ 2 │ ("D11", "N") -0.931988
+ 3 │ ("D24", "N") -0.603424
+ 4 │ ("D01", "Y") -0.580204
+ 5 │ ("D27", "N") -0.57665
+ 6 │ ("D55", "Y") -0.548267
@@ -1520,12 +1520,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D43", "N") 0.64408
- 2 │ ("D04", "Y") 0.644669
- 3 │ ("D42", "N") 0.665114
- 4 │ ("D58", "N") 0.677418
- 5 │ ("D14", "Y") 0.695322
- 6 │ ("D34", "N") 1.08591
+ 1 │ ("D43", "N") 0.644084
+ 2 │ ("D04", "Y") 0.644675
+ 3 │ ("D42", "N") 0.665122
+ 4 │ ("D58", "N") 0.677423
+ 5 │ ("D14", "Y") 0.695323
+ 6 │ ("D34", "N") 1.08592
The largest random effect is for rural settings in D34. There were 26 women in the sample from rural D34
@@ -1565,12 +1565,12 @@
exp(last(first(srtdre)))
-0.38390287360272524
+0.3839013481790906
or about 40%.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/index.html b/index.html
index efb2049..56480db 100644
--- a/index.html
+++ b/index.html
@@ -2,14 +2,14 @@
-
+
-
+
Embrace Uncertainty
@benchmark pirls!(mm) seconds = 1 setup = (mm = m)BenchmarkTools.Trial: 4402 samples with 1 evaluation.
- Range (min … max): 199.693 μs … 331.599 μs ┊ GC (min … max): 0.00% … 0.00%
- Time (median): 227.186 μs ┊ GC (median): 0.00%
- Time (mean ± σ): 226.091 μs ± 5.569 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
+BenchmarkTools.Trial: 3848 samples with 1 evaluation.
+ Range (min … max): 237.623 μs … 461.619 μs ┊ GC (min … max): 0.00% … 0.00%
+ Time (median): 243.844 μs ┊ GC (median): 0.00%
+ Time (mean ± σ): 257.466 μs ± 31.167 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
- ▁ ▇█
- ▂▂▂▂▁▂▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▁▂▂▁▁▂▂▂▂▁▂▁▁▁▁▄█▄▄▃▂▃▃▃▃▃██▇▆▃▃▅▄▃▂▂▂▂ ▃
- 200 μs Histogram: frequency by time 234 μs <
+ ▅▁█▆▃ ▁ ▁▁ ▁ ▁
+ ██████▇██▇████████████▇▇▆▇▆▆▅▆▆▅▅▅▅▆▅▆▅▅▆▅▅█▇▇█▆▅▂▄▃▅▅▄▄▄▃▅▄▄ █
+ 238 μs Histogram: log(frequency) by time 383 μs <
Memory estimate: 112 bytes, allocs estimate: 1.
fit!(m);
-
[ Info: (ret = :ROUNDOFF_LIMITED, fevals = 550, minf = 2354.4744815688055)[ Info: (ret = :ROUNDOFF_LIMITED, fevals = 539, minf = 2354.4744815688023)
@@ -1202,7 +1202,7 @@
)
-
Converged to θ = 0.5683043594028967 and β =[-0.3409777149845993, 0.3933796201906975, 0.6064857599227369, -0.012926172564277872, 0.03323478854784157, -0.005626184982660486]Converged to θ = 0.5683043828108765 and β =[-0.34097751800164144, 0.3933797121269934, 0.6064857490620532, -0.012926173063985653, 0.03323479562652116, -0.005626186061473733]These estimates differ somewhat from those for model com05.
)
-Estimates for com05: θ = 0.5761507901895634, fmin = 2353.8241980539815, and β =[-0.3414913998306781, 0.3936080536502067, 0.6064861079468472, -0.012911714642169572, 0.03321662487439253, -0.005625046845040066]
+Estimates for com05: θ = 0.5761308007138215, fmin = 2353.8241975622855, and β =[-0.3414675499274024, 0.3936022945891755, 0.6064521635960723, -0.01291017393458232, 0.03321086562324279, -0.00562465124007457]
Estimates for com05: θ = 0.5761507901895634, fmin = 2353.8241980539815, and β =[-0.3414913998306781, 0.3936080536502067, 0.6064861079468472, -0.012911714642169572, 0.03321662487439253, -0.005625046845040066]Estimates for com05: θ = 0.5761308007138215, fmin = 2353.8241975622855, and β =[-0.3414675499274024, 0.3936022945891755, 0.6064521635960723, -0.01291017393458232, 0.03321086562324279, -0.00562465124007457]The discrepancy in the results is because the com05 results are based on a more accurate approximation to the integral called adaptive Gauss-Hermite Quadrature, which is discussed in Section C.6.
-
+
Figure C.3: Change in the penalized deviance contribution from that at the conditional mode, for each of the first 5 groups, in model com05, as a function of u.
@@ -1582,7 +1582,7 @@
-
+
Figure C.5: The difference between the contribution to the penalized deviance and its quadratic approximation for each of first 5 groups in model com05 as a function of z.
@@ -1616,7 +1616,7 @@
-
+
Figure C.6: Exponential of half the difference between the quadratic approximation and the contribution to the penalized deviance, for each of first 5 groups in model com05 as a function of z.
@@ -1667,11 +1667,11 @@
extrema(m.utbl.aGHQ)
-(-0.008514108854172236, -0.0004132698576107887)
+(-0.008514109511364184, -0.0004132697962170406)
As we see, these “correction terms” relative to Laplace’s approximation are relatively small, compared to the contributions to the objective from each component of \({\mathbf{u}}\). Also, the corrections are all negative, in this case. Close examination of the individual curves in Figure C.5 shows that these curves, which are \(-2\log(f_j(z))\), are more-or-less odd functions, in the sense that the value at \(-z\) is approximately the negative of the value at \(z\). If we were integrating \(\log(f_j(z_j))\phi(z_j)\) with a normalized Gauss-Hermite rule the negative and positive values would cancel out, for the most part, and some of the integrals would be positive while others would be negative.
@@ -1732,21 +1732,21 @@
7-element Vector{Float64}:
- 0.5761321679271924
- -0.3414655990254175
- 0.39359939391066806
- 0.6064447618771712
- -0.012909685721680265
- 0.03320994962034241
- -0.005624606329593786
+ 0.5761321422331179
+ -0.3414655647634521
+ 0.39359933036111444
+ 0.6064446768742858
+ -0.012909673161244401
+ 0.033209939096054776
+ -0.0056246062539383156
-
This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/datatables.html b/datatables.html
index db512e1..667b5ee 100644
--- a/datatables.html
+++ b/datatables.html
@@ -2,7 +2,7 @@
-
+
@@ -716,8 +716,8 @@ <
The JuliaData organization manages the development of several packages related to data science and data management, including DataFrames.jl, a comprehensive system for working with column-oriented data tables in Julia. Kamiński (2023), written by the primary author of that package, provides an in-depth introduction to data science facilities, in particular the DataFrames package, in Julia.
This package is particularly well-suited to more advanced data manipulation such as the split-apply-combine strategy (Wickham, 2011) and “joins” of data tables.
Bouchet-Valat & Kamiński (2023) compares the performance of DataFrames.jl to other data frame implementations in R and Python.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/glmmbernoulli.html b/glmmbernoulli.html
index 1bba742..e1712df 100644
--- a/glmmbernoulli.html
+++ b/glmmbernoulli.html
@@ -2,7 +2,7 @@
-
+
@@ -534,10 +534,10 @@
(Intercept)
--0.0126
+-0.0128
0.1058
-0.12
-0.9052
+0.9038
0.4787
@@ -545,12 +545,12 @@ 0.1532
0.0982
1.56
-0.1188
+0.1187
livch: 2
-0.2561
+0.2562
0.1045
2.45
0.0142
@@ -558,7 +558,7 @@
livch: 3+
-0.2590
+0.2591
0.1022
2.53
0.0113
@@ -568,15 +568,15 @@ age
0.0006
0.0095
-0.07
-0.9466
+0.06
+0.9483
abs2(age)
-0.0047
0.0008
--6.13
+-6.12
<1e-09
@@ -593,7 +593,7 @@ -0.0067
0.0069
-0.98
-0.3261
+0.3257
@@ -601,7 +601,7 @@ -0.0004
0.0007
-0.51
-0.6075
+0.6079
@@ -768,7 +768,7 @@ -0.2194
0.1155
-1.90
-0.0576
+0.0575
0.4773
@@ -784,12 +784,12 @@ 0.0035
0.0082
0.43
-0.6674
+0.6673
urban: Y
-0.3734
+0.3733
0.0801
4.66
<1e-05
@@ -808,7 +808,7 @@ -0.0068
0.0069
-0.99
-0.3231
+0.3227
@@ -816,7 +816,7 @@ -0.0004
0.0007
-0.49
-0.6261
+0.6272
@@ -880,7 +880,7 @@
(Intercept)
--0.2302
+-0.2303
0.1140
-2.02
0.0434
@@ -888,7 +888,7 @@
urban: Y
-0.3461
+0.3462
0.0599
5.78
<1e-08
@@ -907,7 +907,7 @@ 0.0063
0.0078
0.80
-0.4249
+0.4247
@@ -1034,7 +1034,7 @@ -0.0131
0.0110
-1.19
-0.2351
+0.2353
@@ -1131,7 +1131,7 @@
ch: true
-0.6065
+0.6064
0.1045
5.80
<1e-08
@@ -1142,7 +1142,7 @@ -0.0129
0.0112
-1.16
-0.2470
+0.2471
@@ -1182,21 +1182,21 @@ Table with 4 columns and 44 rows:
age ch urban η
┌─────────────────────────────
- 1 │ -10 false N -1.44276
+ 1 │ -10 false N -1.44278
2 │ -7 false N -1.29428
- 3 │ -4 false N -1.24704
- 4 │ -1 false N -1.30104
- 5 │ 2 false N -1.45629
- 6 │ 5 false N -1.71278
- 7 │ 8 false N -2.07051
- 8 │ 11 false N -2.52948
- 9 │ 14 false N -3.0897
- 10 │ 17 false N -3.75115
- 11 │ 20 false N -4.51385
- 12 │ -10 true N -0.894068
- 13 │ -7 true N -0.546316
- 14 │ -4 true N -0.299807
- 15 │ -1 true N -0.15454
- 16 │ 2 true N -0.110516
- 17 │ 5 true N -0.167734
+ 3 │ -4 false N -1.24703
+ 4 │ -1 false N -1.30102
+ 5 │ 2 false N -1.45626
+ 6 │ 5 false N -1.71273
+ 7 │ 8 false N -2.07045
+ 8 │ 11 false N -2.52941
+ 9 │ 14 false N -3.08962
+ 10 │ 17 false N -3.75107
+ 11 │ 20 false N -4.51376
+ 12 │ -10 true N -0.894085
+ 13 │ -7 true N -0.546331
+ 14 │ -4 true N -0.299819
+ 15 │ -1 true N -0.15455
+ 16 │ 2 true N -0.110524
+ 17 │ 5 true N -0.167741
⋮ │ ⋮ ⋮ ⋮ ⋮
@@ -1346,7 +1346,7 @@
-
+
Figure 6.3: Linear predictor versus centered age from model com05
@@ -1411,23 +1411,23 @@ <
Table with 5 columns and 44 rows:
age ch urban η μ
┌────────────────────────────────────────
- 1 │ -10 false N -1.44276 0.191118
+ 1 │ -10 false N -1.44278 0.191116
2 │ -7 false N -1.29428 0.215129
- 3 │ -4 false N -1.24704 0.223213
- 4 │ -1 false N -1.30104 0.213989
- 5 │ 2 false N -1.45629 0.189035
- 6 │ 5 false N -1.71278 0.152804
- 7 │ 8 false N -2.07051 0.111996
- 8 │ 11 false N -2.52948 0.0738171
- 9 │ 14 false N -3.0897 0.0435342
- 10 │ 17 false N -3.75115 0.0229515
- 11 │ 20 false N -4.51385 0.0108374
- 12 │ -10 true N -0.894068 0.290271
- 13 │ -7 true N -0.546316 0.366719
- 14 │ -4 true N -0.299807 0.425605
- 15 │ -1 true N -0.15454 0.461442
- 16 │ 2 true N -0.110516 0.472399
- 17 │ 5 true N -0.167734 0.458164
+ 3 │ -4 false N -1.24703 0.223215
+ 4 │ -1 false N -1.30102 0.213993
+ 5 │ 2 false N -1.45626 0.189041
+ 6 │ 5 false N -1.71273 0.15281
+ 7 │ 8 false N -2.07045 0.112002
+ 8 │ 11 false N -2.52941 0.0738216
+ 9 │ 14 false N -3.08962 0.0435374
+ 10 │ 17 false N -3.75107 0.0229534
+ 11 │ 20 false N -4.51376 0.0108384
+ 12 │ -10 true N -0.894085 0.290267
+ 13 │ -7 true N -0.546331 0.366716
+ 14 │ -4 true N -0.299819 0.425602
+ 15 │ -1 true N -0.15455 0.461439
+ 16 │ 2 true N -0.110524 0.472397
+ 17 │ 5 true N -0.167741 0.458163
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
@@ -1450,7 +1450,7 @@ <
-
+
Figure 6.5: Predicted probability of contraception use versus centered age from model com05.
@@ -1466,10 +1466,10 @@ <
Table with 5 columns and 4 rows:
age ch urban η μ
┌───────────────────────────────────────
- 1 │ 2 false N -1.45629 0.189035
- 2 │ 2 true N -0.110516 0.472399
- 3 │ 2 false Y -0.669101 0.338698
- 4 │ 2 true Y 0.676673 0.662996
+ 1 │ 2 false N -1.45626 0.189041
+ 2 │ 2 true N -0.110524 0.472397
+ 3 │ 2 false Y -0.669056 0.338708
+ 4 │ 2 true Y 0.676675 0.662996
The predicted probability of woman with centered age of 2, with children, living in an urban environment using artificial contraception is about 2/3, which is reasonably close to the smoothed frequency for that combination of covariates in Figure 6.2.
@@ -1487,7 +1487,7 @@
-
+
Figure 6.6: Caterpillar plot of the conditional modes of the random-effects for model com05
@@ -1506,12 +1506,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D01", "N") -0.957366
- 2 │ ("D11", "N") -0.93198
- 3 │ ("D24", "N") -0.603418
- 4 │ ("D01", "Y") -0.580203
- 5 │ ("D27", "N") -0.576647
- 6 │ ("D55", "Y") -0.548261
+ 1 │ ("D01", "N") -0.95737
+ 2 │ ("D11", "N") -0.931988
+ 3 │ ("D24", "N") -0.603424
+ 4 │ ("D01", "Y") -0.580204
+ 5 │ ("D27", "N") -0.57665
+ 6 │ ("D55", "Y") -0.548267
@@ -1520,12 +1520,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D43", "N") 0.64408
- 2 │ ("D04", "Y") 0.644669
- 3 │ ("D42", "N") 0.665114
- 4 │ ("D58", "N") 0.677418
- 5 │ ("D14", "Y") 0.695322
- 6 │ ("D34", "N") 1.08591
+ 1 │ ("D43", "N") 0.644084
+ 2 │ ("D04", "Y") 0.644675
+ 3 │ ("D42", "N") 0.665122
+ 4 │ ("D58", "N") 0.677423
+ 5 │ ("D14", "Y") 0.695323
+ 6 │ ("D34", "N") 1.08592
The largest random effect is for rural settings in D34. There were 26 women in the sample from rural D34
@@ -1565,12 +1565,12 @@
exp(last(first(srtdre)))
-0.38390287360272524
+0.3839013481790906
or about 40%.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/index.html b/index.html
index efb2049..56480db 100644
--- a/index.html
+++ b/index.html
@@ -2,14 +2,14 @@
-
+
-
+
Embrace Uncertainty
-
+
+
-
+
Figure C.5: The difference between the contribution to the penalized deviance and its quadratic approximation for each of first 5 groups in model com05 as a function of z.
@@ -1616,7 +1616,7 @@
-
+
Figure C.6: Exponential of half the difference between the quadratic approximation and the contribution to the penalized deviance, for each of first 5 groups in model com05 as a function of z.
@@ -1667,11 +1667,11 @@
extrema(m.utbl.aGHQ)
-(-0.008514108854172236, -0.0004132698576107887)
+(-0.008514109511364184, -0.0004132697962170406)
As we see, these “correction terms” relative to Laplace’s approximation are relatively small, compared to the contributions to the objective from each component of \({\mathbf{u}}\). Also, the corrections are all negative, in this case. Close examination of the individual curves in Figure C.5 shows that these curves, which are \(-2\log(f_j(z))\), are more-or-less odd functions, in the sense that the value at \(-z\) is approximately the negative of the value at \(z\). If we were integrating \(\log(f_j(z_j))\phi(z_j)\) with a normalized Gauss-Hermite rule the negative and positive values would cancel out, for the most part, and some of the integrals would be positive while others would be negative.
@@ -1732,21 +1732,21 @@
7-element Vector{Float64}:
- 0.5761321679271924
- -0.3414655990254175
- 0.39359939391066806
- 0.6064447618771712
- -0.012909685721680265
- 0.03320994962034241
- -0.005624606329593786
+ 0.5761321422331179
+ -0.3414655647634521
+ 0.39359933036111444
+ 0.6064446768742858
+ -0.012909673161244401
+ 0.033209939096054776
+ -0.0056246062539383156
-
This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/datatables.html b/datatables.html
index db512e1..667b5ee 100644
--- a/datatables.html
+++ b/datatables.html
@@ -2,7 +2,7 @@
-
+
@@ -716,8 +716,8 @@ <
The JuliaData organization manages the development of several packages related to data science and data management, including DataFrames.jl, a comprehensive system for working with column-oriented data tables in Julia. Kamiński (2023), written by the primary author of that package, provides an in-depth introduction to data science facilities, in particular the DataFrames package, in Julia.
This package is particularly well-suited to more advanced data manipulation such as the split-apply-combine strategy (Wickham, 2011) and “joins” of data tables.
Bouchet-Valat & Kamiński (2023) compares the performance of DataFrames.jl to other data frame implementations in R and Python.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/glmmbernoulli.html b/glmmbernoulli.html
index 1bba742..e1712df 100644
--- a/glmmbernoulli.html
+++ b/glmmbernoulli.html
@@ -2,7 +2,7 @@
-
+
@@ -534,10 +534,10 @@
(Intercept)
--0.0126
+-0.0128
0.1058
-0.12
-0.9052
+0.9038
0.4787
@@ -545,12 +545,12 @@ 0.1532
0.0982
1.56
-0.1188
+0.1187
livch: 2
-0.2561
+0.2562
0.1045
2.45
0.0142
@@ -558,7 +558,7 @@
livch: 3+
-0.2590
+0.2591
0.1022
2.53
0.0113
@@ -568,15 +568,15 @@ age
0.0006
0.0095
-0.07
-0.9466
+0.06
+0.9483
abs2(age)
-0.0047
0.0008
--6.13
+-6.12
<1e-09
@@ -593,7 +593,7 @@ -0.0067
0.0069
-0.98
-0.3261
+0.3257
@@ -601,7 +601,7 @@ -0.0004
0.0007
-0.51
-0.6075
+0.6079
@@ -768,7 +768,7 @@ -0.2194
0.1155
-1.90
-0.0576
+0.0575
0.4773
@@ -784,12 +784,12 @@ 0.0035
0.0082
0.43
-0.6674
+0.6673
urban: Y
-0.3734
+0.3733
0.0801
4.66
<1e-05
@@ -808,7 +808,7 @@ -0.0068
0.0069
-0.99
-0.3231
+0.3227
@@ -816,7 +816,7 @@ -0.0004
0.0007
-0.49
-0.6261
+0.6272
@@ -880,7 +880,7 @@
(Intercept)
--0.2302
+-0.2303
0.1140
-2.02
0.0434
@@ -888,7 +888,7 @@
urban: Y
-0.3461
+0.3462
0.0599
5.78
<1e-08
@@ -907,7 +907,7 @@ 0.0063
0.0078
0.80
-0.4249
+0.4247
@@ -1034,7 +1034,7 @@ -0.0131
0.0110
-1.19
-0.2351
+0.2353
@@ -1131,7 +1131,7 @@
ch: true
-0.6065
+0.6064
0.1045
5.80
<1e-08
@@ -1142,7 +1142,7 @@ -0.0129
0.0112
-1.16
-0.2470
+0.2471
@@ -1182,21 +1182,21 @@ Table with 4 columns and 44 rows:
age ch urban η
┌─────────────────────────────
- 1 │ -10 false N -1.44276
+ 1 │ -10 false N -1.44278
2 │ -7 false N -1.29428
- 3 │ -4 false N -1.24704
- 4 │ -1 false N -1.30104
- 5 │ 2 false N -1.45629
- 6 │ 5 false N -1.71278
- 7 │ 8 false N -2.07051
- 8 │ 11 false N -2.52948
- 9 │ 14 false N -3.0897
- 10 │ 17 false N -3.75115
- 11 │ 20 false N -4.51385
- 12 │ -10 true N -0.894068
- 13 │ -7 true N -0.546316
- 14 │ -4 true N -0.299807
- 15 │ -1 true N -0.15454
- 16 │ 2 true N -0.110516
- 17 │ 5 true N -0.167734
+ 3 │ -4 false N -1.24703
+ 4 │ -1 false N -1.30102
+ 5 │ 2 false N -1.45626
+ 6 │ 5 false N -1.71273
+ 7 │ 8 false N -2.07045
+ 8 │ 11 false N -2.52941
+ 9 │ 14 false N -3.08962
+ 10 │ 17 false N -3.75107
+ 11 │ 20 false N -4.51376
+ 12 │ -10 true N -0.894085
+ 13 │ -7 true N -0.546331
+ 14 │ -4 true N -0.299819
+ 15 │ -1 true N -0.15455
+ 16 │ 2 true N -0.110524
+ 17 │ 5 true N -0.167741
⋮ │ ⋮ ⋮ ⋮ ⋮
@@ -1346,7 +1346,7 @@
-
+
Figure 6.3: Linear predictor versus centered age from model com05
@@ -1411,23 +1411,23 @@ <
Table with 5 columns and 44 rows:
age ch urban η μ
┌────────────────────────────────────────
- 1 │ -10 false N -1.44276 0.191118
+ 1 │ -10 false N -1.44278 0.191116
2 │ -7 false N -1.29428 0.215129
- 3 │ -4 false N -1.24704 0.223213
- 4 │ -1 false N -1.30104 0.213989
- 5 │ 2 false N -1.45629 0.189035
- 6 │ 5 false N -1.71278 0.152804
- 7 │ 8 false N -2.07051 0.111996
- 8 │ 11 false N -2.52948 0.0738171
- 9 │ 14 false N -3.0897 0.0435342
- 10 │ 17 false N -3.75115 0.0229515
- 11 │ 20 false N -4.51385 0.0108374
- 12 │ -10 true N -0.894068 0.290271
- 13 │ -7 true N -0.546316 0.366719
- 14 │ -4 true N -0.299807 0.425605
- 15 │ -1 true N -0.15454 0.461442
- 16 │ 2 true N -0.110516 0.472399
- 17 │ 5 true N -0.167734 0.458164
+ 3 │ -4 false N -1.24703 0.223215
+ 4 │ -1 false N -1.30102 0.213993
+ 5 │ 2 false N -1.45626 0.189041
+ 6 │ 5 false N -1.71273 0.15281
+ 7 │ 8 false N -2.07045 0.112002
+ 8 │ 11 false N -2.52941 0.0738216
+ 9 │ 14 false N -3.08962 0.0435374
+ 10 │ 17 false N -3.75107 0.0229534
+ 11 │ 20 false N -4.51376 0.0108384
+ 12 │ -10 true N -0.894085 0.290267
+ 13 │ -7 true N -0.546331 0.366716
+ 14 │ -4 true N -0.299819 0.425602
+ 15 │ -1 true N -0.15455 0.461439
+ 16 │ 2 true N -0.110524 0.472397
+ 17 │ 5 true N -0.167741 0.458163
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
@@ -1450,7 +1450,7 @@ <
-
+
Figure 6.5: Predicted probability of contraception use versus centered age from model com05.
@@ -1466,10 +1466,10 @@ <
Table with 5 columns and 4 rows:
age ch urban η μ
┌───────────────────────────────────────
- 1 │ 2 false N -1.45629 0.189035
- 2 │ 2 true N -0.110516 0.472399
- 3 │ 2 false Y -0.669101 0.338698
- 4 │ 2 true Y 0.676673 0.662996
+ 1 │ 2 false N -1.45626 0.189041
+ 2 │ 2 true N -0.110524 0.472397
+ 3 │ 2 false Y -0.669056 0.338708
+ 4 │ 2 true Y 0.676675 0.662996
The predicted probability of woman with centered age of 2, with children, living in an urban environment using artificial contraception is about 2/3, which is reasonably close to the smoothed frequency for that combination of covariates in Figure 6.2.
@@ -1487,7 +1487,7 @@
-
+
Figure 6.6: Caterpillar plot of the conditional modes of the random-effects for model com05
@@ -1506,12 +1506,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D01", "N") -0.957366
- 2 │ ("D11", "N") -0.93198
- 3 │ ("D24", "N") -0.603418
- 4 │ ("D01", "Y") -0.580203
- 5 │ ("D27", "N") -0.576647
- 6 │ ("D55", "Y") -0.548261
+ 1 │ ("D01", "N") -0.95737
+ 2 │ ("D11", "N") -0.931988
+ 3 │ ("D24", "N") -0.603424
+ 4 │ ("D01", "Y") -0.580204
+ 5 │ ("D27", "N") -0.57665
+ 6 │ ("D55", "Y") -0.548267
@@ -1520,12 +1520,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D43", "N") 0.64408
- 2 │ ("D04", "Y") 0.644669
- 3 │ ("D42", "N") 0.665114
- 4 │ ("D58", "N") 0.677418
- 5 │ ("D14", "Y") 0.695322
- 6 │ ("D34", "N") 1.08591
+ 1 │ ("D43", "N") 0.644084
+ 2 │ ("D04", "Y") 0.644675
+ 3 │ ("D42", "N") 0.665122
+ 4 │ ("D58", "N") 0.677423
+ 5 │ ("D14", "Y") 0.695323
+ 6 │ ("D34", "N") 1.08592
The largest random effect is for rural settings in D34. There were 26 women in the sample from rural D34
@@ -1565,12 +1565,12 @@
exp(last(first(srtdre)))
-0.38390287360272524
+0.3839013481790906
or about 40%.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/index.html b/index.html
index efb2049..56480db 100644
--- a/index.html
+++ b/index.html
@@ -2,14 +2,14 @@
-
+
-
+
Embrace Uncertainty
-
+
+
-
+
Figure C.6: Exponential of half the difference between the quadratic approximation and the contribution to the penalized deviance, for each of first 5 groups in model com05 as a function of z.
@@ -1667,11 +1667,11 @@
extrema(m.utbl.aGHQ)
-(-0.008514108854172236, -0.0004132698576107887)
+(-0.008514109511364184, -0.0004132697962170406)
As we see, these “correction terms” relative to Laplace’s approximation are relatively small, compared to the contributions to the objective from each component of \({\mathbf{u}}\). Also, the corrections are all negative, in this case. Close examination of the individual curves in Figure C.5 shows that these curves, which are \(-2\log(f_j(z))\), are more-or-less odd functions, in the sense that the value at \(-z\) is approximately the negative of the value at \(z\). If we were integrating \(\log(f_j(z_j))\phi(z_j)\) with a normalized Gauss-Hermite rule the negative and positive values would cancel out, for the most part, and some of the integrals would be positive while others would be negative.
@@ -1732,21 +1732,21 @@
7-element Vector{Float64}:
- 0.5761321679271924
- -0.3414655990254175
- 0.39359939391066806
- 0.6064447618771712
- -0.012909685721680265
- 0.03320994962034241
- -0.005624606329593786
+ 0.5761321422331179
+ -0.3414655647634521
+ 0.39359933036111444
+ 0.6064446768742858
+ -0.012909673161244401
+ 0.033209939096054776
+ -0.0056246062539383156
-
This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/datatables.html b/datatables.html
index db512e1..667b5ee 100644
--- a/datatables.html
+++ b/datatables.html
@@ -2,7 +2,7 @@
-
+
@@ -716,8 +716,8 @@ <
The JuliaData organization manages the development of several packages related to data science and data management, including DataFrames.jl, a comprehensive system for working with column-oriented data tables in Julia. Kamiński (2023), written by the primary author of that package, provides an in-depth introduction to data science facilities, in particular the DataFrames package, in Julia.
This package is particularly well-suited to more advanced data manipulation such as the split-apply-combine strategy (Wickham, 2011) and “joins” of data tables.
Bouchet-Valat & Kamiński (2023) compares the performance of DataFrames.jl to other data frame implementations in R and Python.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/glmmbernoulli.html b/glmmbernoulli.html
index 1bba742..e1712df 100644
--- a/glmmbernoulli.html
+++ b/glmmbernoulli.html
@@ -2,7 +2,7 @@
-
+
@@ -534,10 +534,10 @@
(Intercept)
--0.0126
+-0.0128
0.1058
-0.12
-0.9052
+0.9038
0.4787
@@ -545,12 +545,12 @@ 0.1532
0.0982
1.56
-0.1188
+0.1187
livch: 2
-0.2561
+0.2562
0.1045
2.45
0.0142
@@ -558,7 +558,7 @@
livch: 3+
-0.2590
+0.2591
0.1022
2.53
0.0113
@@ -568,15 +568,15 @@ age
0.0006
0.0095
-0.07
-0.9466
+0.06
+0.9483
abs2(age)
-0.0047
0.0008
--6.13
+-6.12
<1e-09
@@ -593,7 +593,7 @@ -0.0067
0.0069
-0.98
-0.3261
+0.3257
@@ -601,7 +601,7 @@ -0.0004
0.0007
-0.51
-0.6075
+0.6079
@@ -768,7 +768,7 @@ -0.2194
0.1155
-1.90
-0.0576
+0.0575
0.4773
@@ -784,12 +784,12 @@ 0.0035
0.0082
0.43
-0.6674
+0.6673
urban: Y
-0.3734
+0.3733
0.0801
4.66
<1e-05
@@ -808,7 +808,7 @@ -0.0068
0.0069
-0.99
-0.3231
+0.3227
@@ -816,7 +816,7 @@ -0.0004
0.0007
-0.49
-0.6261
+0.6272
@@ -880,7 +880,7 @@
(Intercept)
--0.2302
+-0.2303
0.1140
-2.02
0.0434
@@ -888,7 +888,7 @@
urban: Y
-0.3461
+0.3462
0.0599
5.78
<1e-08
@@ -907,7 +907,7 @@ 0.0063
0.0078
0.80
-0.4249
+0.4247
@@ -1034,7 +1034,7 @@ -0.0131
0.0110
-1.19
-0.2351
+0.2353
@@ -1131,7 +1131,7 @@
ch: true
-0.6065
+0.6064
0.1045
5.80
<1e-08
@@ -1142,7 +1142,7 @@ -0.0129
0.0112
-1.16
-0.2470
+0.2471
@@ -1182,21 +1182,21 @@ Table with 4 columns and 44 rows:
age ch urban η
┌─────────────────────────────
- 1 │ -10 false N -1.44276
+ 1 │ -10 false N -1.44278
2 │ -7 false N -1.29428
- 3 │ -4 false N -1.24704
- 4 │ -1 false N -1.30104
- 5 │ 2 false N -1.45629
- 6 │ 5 false N -1.71278
- 7 │ 8 false N -2.07051
- 8 │ 11 false N -2.52948
- 9 │ 14 false N -3.0897
- 10 │ 17 false N -3.75115
- 11 │ 20 false N -4.51385
- 12 │ -10 true N -0.894068
- 13 │ -7 true N -0.546316
- 14 │ -4 true N -0.299807
- 15 │ -1 true N -0.15454
- 16 │ 2 true N -0.110516
- 17 │ 5 true N -0.167734
+ 3 │ -4 false N -1.24703
+ 4 │ -1 false N -1.30102
+ 5 │ 2 false N -1.45626
+ 6 │ 5 false N -1.71273
+ 7 │ 8 false N -2.07045
+ 8 │ 11 false N -2.52941
+ 9 │ 14 false N -3.08962
+ 10 │ 17 false N -3.75107
+ 11 │ 20 false N -4.51376
+ 12 │ -10 true N -0.894085
+ 13 │ -7 true N -0.546331
+ 14 │ -4 true N -0.299819
+ 15 │ -1 true N -0.15455
+ 16 │ 2 true N -0.110524
+ 17 │ 5 true N -0.167741
⋮ │ ⋮ ⋮ ⋮ ⋮
@@ -1346,7 +1346,7 @@
-
+
Figure 6.3: Linear predictor versus centered age from model com05
@@ -1411,23 +1411,23 @@ <
Table with 5 columns and 44 rows:
age ch urban η μ
┌────────────────────────────────────────
- 1 │ -10 false N -1.44276 0.191118
+ 1 │ -10 false N -1.44278 0.191116
2 │ -7 false N -1.29428 0.215129
- 3 │ -4 false N -1.24704 0.223213
- 4 │ -1 false N -1.30104 0.213989
- 5 │ 2 false N -1.45629 0.189035
- 6 │ 5 false N -1.71278 0.152804
- 7 │ 8 false N -2.07051 0.111996
- 8 │ 11 false N -2.52948 0.0738171
- 9 │ 14 false N -3.0897 0.0435342
- 10 │ 17 false N -3.75115 0.0229515
- 11 │ 20 false N -4.51385 0.0108374
- 12 │ -10 true N -0.894068 0.290271
- 13 │ -7 true N -0.546316 0.366719
- 14 │ -4 true N -0.299807 0.425605
- 15 │ -1 true N -0.15454 0.461442
- 16 │ 2 true N -0.110516 0.472399
- 17 │ 5 true N -0.167734 0.458164
+ 3 │ -4 false N -1.24703 0.223215
+ 4 │ -1 false N -1.30102 0.213993
+ 5 │ 2 false N -1.45626 0.189041
+ 6 │ 5 false N -1.71273 0.15281
+ 7 │ 8 false N -2.07045 0.112002
+ 8 │ 11 false N -2.52941 0.0738216
+ 9 │ 14 false N -3.08962 0.0435374
+ 10 │ 17 false N -3.75107 0.0229534
+ 11 │ 20 false N -4.51376 0.0108384
+ 12 │ -10 true N -0.894085 0.290267
+ 13 │ -7 true N -0.546331 0.366716
+ 14 │ -4 true N -0.299819 0.425602
+ 15 │ -1 true N -0.15455 0.461439
+ 16 │ 2 true N -0.110524 0.472397
+ 17 │ 5 true N -0.167741 0.458163
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
@@ -1450,7 +1450,7 @@ <
-
+
Figure 6.5: Predicted probability of contraception use versus centered age from model com05.
@@ -1466,10 +1466,10 @@ <
Table with 5 columns and 4 rows:
age ch urban η μ
┌───────────────────────────────────────
- 1 │ 2 false N -1.45629 0.189035
- 2 │ 2 true N -0.110516 0.472399
- 3 │ 2 false Y -0.669101 0.338698
- 4 │ 2 true Y 0.676673 0.662996
+ 1 │ 2 false N -1.45626 0.189041
+ 2 │ 2 true N -0.110524 0.472397
+ 3 │ 2 false Y -0.669056 0.338708
+ 4 │ 2 true Y 0.676675 0.662996
The predicted probability of woman with centered age of 2, with children, living in an urban environment using artificial contraception is about 2/3, which is reasonably close to the smoothed frequency for that combination of covariates in Figure 6.2.
@@ -1487,7 +1487,7 @@
-
+
Figure 6.6: Caterpillar plot of the conditional modes of the random-effects for model com05
@@ -1506,12 +1506,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D01", "N") -0.957366
- 2 │ ("D11", "N") -0.93198
- 3 │ ("D24", "N") -0.603418
- 4 │ ("D01", "Y") -0.580203
- 5 │ ("D27", "N") -0.576647
- 6 │ ("D55", "Y") -0.548261
+ 1 │ ("D01", "N") -0.95737
+ 2 │ ("D11", "N") -0.931988
+ 3 │ ("D24", "N") -0.603424
+ 4 │ ("D01", "Y") -0.580204
+ 5 │ ("D27", "N") -0.57665
+ 6 │ ("D55", "Y") -0.548267
@@ -1520,12 +1520,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D43", "N") 0.64408
- 2 │ ("D04", "Y") 0.644669
- 3 │ ("D42", "N") 0.665114
- 4 │ ("D58", "N") 0.677418
- 5 │ ("D14", "Y") 0.695322
- 6 │ ("D34", "N") 1.08591
+ 1 │ ("D43", "N") 0.644084
+ 2 │ ("D04", "Y") 0.644675
+ 3 │ ("D42", "N") 0.665122
+ 4 │ ("D58", "N") 0.677423
+ 5 │ ("D14", "Y") 0.695323
+ 6 │ ("D34", "N") 1.08592
The largest random effect is for rural settings in D34. There were 26 women in the sample from rural D34
@@ -1565,12 +1565,12 @@
exp(last(first(srtdre)))
-0.38390287360272524
+0.3839013481790906
or about 40%.
-This page was rendered from git revision 05a171b
+This page was rendered from git revision a091925
.
diff --git a/index.html b/index.html
index efb2049..56480db 100644
--- a/index.html
+++ b/index.html
@@ -2,14 +2,14 @@
-
+
-
+
Embrace Uncertainty
-
+
+
extrema(m.utbl.aGHQ)
-(-0.008514108854172236, -0.0004132698576107887)
+(-0.008514109511364184, -0.0004132697962170406)
extrema(m.utbl.aGHQ)(-0.008514108854172236, -0.0004132698576107887)(-0.008514109511364184, -0.0004132697962170406)As we see, these “correction terms” relative to Laplace’s approximation are relatively small, compared to the contributions to the objective from each component of \({\mathbf{u}}\). Also, the corrections are all negative, in this case. Close examination of the individual curves in Figure C.5 shows that these curves, which are \(-2\log(f_j(z))\), are more-or-less odd functions, in the sense that the value at \(-z\) is approximately the negative of the value at \(z\). If we were integrating \(\log(f_j(z_j))\phi(z_j)\) with a normalized Gauss-Hermite rule the negative and positive values would cancel out, for the most part, and some of the integrals would be positive while others would be negative.
@@ -1732,21 +1732,21 @@
7-element Vector{Float64}:
- 0.5761321679271924
- -0.3414655990254175
- 0.39359939391066806
- 0.6064447618771712
- -0.012909685721680265
- 0.03320994962034241
- -0.005624606329593786
+ 0.5761321422331179
+ -0.3414655647634521
+ 0.39359933036111444
+ 0.6064446768742858
+ -0.012909673161244401
+ 0.033209939096054776
+ -0.0056246062539383156
-
7-element Vector{Float64}:
- 0.5761321679271924
- -0.3414655990254175
- 0.39359939391066806
- 0.6064447618771712
- -0.012909685721680265
- 0.03320994962034241
- -0.005624606329593786This page was rendered from git revision 05a171b
+ This page was rendered from git revision a091925
. The JuliaData organization manages the development of several packages related to data science and data management, including DataFrames.jl, a comprehensive system for working with column-oriented data tables in Julia. Kamiński (2023), written by the primary author of that package, provides an in-depth introduction to data science facilities, in particular the This package is particularly well-suited to more advanced data manipulation such as the Bouchet-Valat & Kamiński (2023) compares the performance of DataFrames.jl to other data frame implementations in R and Python. This page was rendered from git revision 05a171b
+ This page was rendered from git revision a091925
. The predicted probability of woman with centered age of 2, with children, living in an urban environment using artificial contraception is about 2/3, which is reasonably close to the smoothed frequency for that combination of covariates in Figure 6.2. The largest random effect is for rural settings in or about 40%. This page was rendered from git revision 05a171b
+ This page was rendered from git revision a091925
.<
DataFrames package, in Julia.split-apply-combine strategy (Wickham, 2011) and “joins” of data tables.
(Intercept)
--0.0126
+-0.0128
0.1058
-0.12
-0.9052
+0.9038
0.4787
@@ -545,12 +545,12 @@
0.1532
0.0982
1.56
-0.1188
+0.1187
livch: 2
-0.2561
+0.2562
0.1045
2.45
0.0142
@@ -558,7 +558,7 @@
livch: 3+
-0.2590
+0.2591
0.1022
2.53
0.0113
@@ -568,15 +568,15 @@ age
0.0006
0.0095
-0.07
-0.9466
+0.06
+0.9483
@@ -593,7 +593,7 @@ abs2(age)
-0.0047
0.0008
--6.13
+-6.12
<1e-09
-0.0067
0.0069
-0.98
-0.3261
+0.3257
@@ -601,7 +601,7 @@
@@ -768,7 +768,7 @@ -0.0004
0.0007
-0.51
-0.6075
+0.6079
-0.2194
0.1155
-1.90
-0.0576
+0.0575
0.4773
@@ -784,12 +784,12 @@
0.0035
0.0082
0.43
-0.6674
+0.6673
urban: Y
-0.3734
+0.3733
0.0801
4.66
<1e-05
@@ -808,7 +808,7 @@ -0.0068
0.0069
-0.99
-0.3231
+0.3227
@@ -816,7 +816,7 @@
@@ -880,7 +880,7 @@ -0.0004
0.0007
-0.49
-0.6261
+0.6272
(Intercept)
--0.2302
+-0.2303
0.1140
-2.02
0.0434
@@ -888,7 +888,7 @@
urban: Y
-0.3461
+0.3462
0.0599
5.78
<1e-08
@@ -907,7 +907,7 @@ 0.0063
0.0078
0.80
-0.4249
+0.4247
@@ -1034,7 +1034,7 @@
-0.0131
0.0110
-1.19
-0.2351
+0.2353
@@ -1131,7 +1131,7 @@
ch: true
-0.6065
+0.6064
0.1045
5.80
<1e-08
@@ -1142,7 +1142,7 @@ -0.0129
0.0112
-1.16
-0.2470
+0.2471
@@ -1182,21 +1182,21 @@ Table with 4 columns and 44 rows:
age ch urban η
┌─────────────────────────────
- 1 │ -10 false N -1.44276
+ 1 │ -10 false N -1.44278
2 │ -7 false N -1.29428
- 3 │ -4 false N -1.24704
- 4 │ -1 false N -1.30104
- 5 │ 2 false N -1.45629
- 6 │ 5 false N -1.71278
- 7 │ 8 false N -2.07051
- 8 │ 11 false N -2.52948
- 9 │ 14 false N -3.0897
- 10 │ 17 false N -3.75115
- 11 │ 20 false N -4.51385
- 12 │ -10 true N -0.894068
- 13 │ -7 true N -0.546316
- 14 │ -4 true N -0.299807
- 15 │ -1 true N -0.15454
- 16 │ 2 true N -0.110516
- 17 │ 5 true N -0.167734
+ 3 │ -4 false N -1.24703
+ 4 │ -1 false N -1.30102
+ 5 │ 2 false N -1.45626
+ 6 │ 5 false N -1.71273
+ 7 │ 8 false N -2.07045
+ 8 │ 11 false N -2.52941
+ 9 │ 14 false N -3.08962
+ 10 │ 17 false N -3.75107
+ 11 │ 20 false N -4.51376
+ 12 │ -10 true N -0.894085
+ 13 │ -7 true N -0.546331
+ 14 │ -4 true N -0.299819
+ 15 │ -1 true N -0.15455
+ 16 │ 2 true N -0.110524
+ 17 │ 5 true N -0.167741
⋮ │ ⋮ ⋮ ⋮ ⋮
@@ -1346,7 +1346,7 @@
-
+
<
@@ -1450,7 +1450,7 @@ Table with 5 columns and 44 rows:
age ch urban η μ
┌────────────────────────────────────────
- 1 │ -10 false N -1.44276 0.191118
+ 1 │ -10 false N -1.44278 0.191116
2 │ -7 false N -1.29428 0.215129
- 3 │ -4 false N -1.24704 0.223213
- 4 │ -1 false N -1.30104 0.213989
- 5 │ 2 false N -1.45629 0.189035
- 6 │ 5 false N -1.71278 0.152804
- 7 │ 8 false N -2.07051 0.111996
- 8 │ 11 false N -2.52948 0.0738171
- 9 │ 14 false N -3.0897 0.0435342
- 10 │ 17 false N -3.75115 0.0229515
- 11 │ 20 false N -4.51385 0.0108374
- 12 │ -10 true N -0.894068 0.290271
- 13 │ -7 true N -0.546316 0.366719
- 14 │ -4 true N -0.299807 0.425605
- 15 │ -1 true N -0.15454 0.461442
- 16 │ 2 true N -0.110516 0.472399
- 17 │ 5 true N -0.167734 0.458164
+ 3 │ -4 false N -1.24703 0.223215
+ 4 │ -1 false N -1.30102 0.213993
+ 5 │ 2 false N -1.45626 0.189041
+ 6 │ 5 false N -1.71273 0.15281
+ 7 │ 8 false N -2.07045 0.112002
+ 8 │ 11 false N -2.52941 0.0738216
+ 9 │ 14 false N -3.08962 0.0435374
+ 10 │ 17 false N -3.75107 0.0229534
+ 11 │ 20 false N -4.51376 0.0108384
+ 12 │ -10 true N -0.894085 0.290267
+ 13 │ -7 true N -0.546331 0.366716
+ 14 │ -4 true N -0.299819 0.425602
+ 15 │ -1 true N -0.15455 0.461439
+ 16 │ 2 true N -0.110524 0.472397
+ 17 │ 5 true N -0.167741 0.458163
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮<
-
+
<
+ 1 │ 2 false N -1.45626 0.189041
+ 2 │ 2 true N -0.110524 0.472397
+ 3 │ 2 false Y -0.669056 0.338708
+ 4 │ 2 true Y 0.676675 0.662996
Table with 5 columns and 4 rows:
age ch urban η μ
┌───────────────────────────────────────
- 1 │ 2 false N -1.45629 0.189035
- 2 │ 2 true N -0.110516 0.472399
- 3 │ 2 false Y -0.669101 0.338698
- 4 │ 2 true Y 0.676673 0.662996
-
+
com05
@@ -1506,12 +1506,12 @@ Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D01", "N") -0.957366
- 2 │ ("D11", "N") -0.93198
- 3 │ ("D24", "N") -0.603418
- 4 │ ("D01", "Y") -0.580203
- 5 │ ("D27", "N") -0.576647
- 6 │ ("D55", "Y") -0.548261
+ 1 │ ("D01", "N") -0.95737
+ 2 │ ("D11", "N") -0.931988
+ 3 │ ("D24", "N") -0.603424
+ 4 │ ("D01", "Y") -0.580204
+ 5 │ ("D27", "N") -0.57665
+ 6 │ ("D55", "Y") -0.548267
@@ -1520,12 +1520,12 @@
Table with 2 columns and 6 rows:
dist & urban (Intercept)
┌──────────────────────────
- 1 │ ("D43", "N") 0.64408
- 2 │ ("D04", "Y") 0.644669
- 3 │ ("D42", "N") 0.665114
- 4 │ ("D58", "N") 0.677418
- 5 │ ("D14", "Y") 0.695322
- 6 │ ("D34", "N") 1.08591
+ 1 │ ("D43", "N") 0.644084
+ 2 │ ("D04", "Y") 0.644675
+ 3 │ ("D42", "N") 0.665122
+ 4 │ ("D58", "N") 0.677423
+ 5 │ ("D14", "Y") 0.695323
+ 6 │ ("D34", "N") 1.08592
D34. There were 26 women in the sample from rural D34
exp(last(first(srtdre)))
-
+0.383902873602725240.3839013481790906