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ripley
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add more examples
git-svn-id: https://svn.r-project.org/R/trunk@87482 00db46b3-68df-0310-9c12-caf00c1e9a41
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‎src/library/stats/tests/loglin.R‎

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@@ -10,3 +10,34 @@ loglin(x, list(1))
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## failed in R 4.4.2 as R code assumed >= 2 parameters
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loglin(x, NULL, param = TRUE)
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loglin(x, list(1), param = TRUE)
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## Versions of some MASS::loglm examples
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# loglm(~ Type + Origin, xtabs(~ Type + Origin, Cars93))
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if(require("MASS", quietly = TRUE))
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loglin(xtabs(~ Type + Origin, Cars93), list(Type=1, Origin=2))
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# fm <- loglm(~ 1 + 2 + 3 + 4, minn38a)
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mn <- structure(c(53L, 163L, 309L, 13L, 28L, 38L, 7L, 30L, 17L, 76L,
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118L, 89L, 36L, 116L, 225L, 11L, 53L, 68L, 16L, 41L,
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49L, 111L, 214L, 210L, 52L, 162L, 243L, 49L, 129L,
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284L, 28L, 64L, 79L, 521L, 708L, 448L, 48L, 130L,
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237L, 29L, 62L, 63L, 18L, 47L, 57L, 191L, 305L, 219L,
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12L, 35L, 72L, 10L, 37L, 21L, 5L, 11L, 20L, 101L,
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152L, 95L, 9L, 19L, 42L, 15L, 22L, 19L, 1L, 13L, 10L,
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130L, 174L, 105L, 3L, 25L, 36L, 6L, 15L, 19L, 1L, 9L,
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14L, 88L, 158L, 93L, 87L, 216L, 256L, 17L, 14L, 10L,
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3L, 4L, 2L, 105L, 118L, 53L, 72L, 159L, 176L, 18L,
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28L, 22L, 6L, 14L, 8L, 209L, 227L, 95L, 52L, 119L,
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119L, 14L, 44L, 33L, 17L, 13L, 10L, 541L, 578L, 257L,
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88L, 158L, 144L, 14L, 36L, 20L, 9L, 15L, 12L, 328L,
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304L, 115L, 32L, 43L, 42L, 12L, 7L, 7L, 1L, 5L, 2L,
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124L, 119L, 56L, 14L, 24L, 24L, 5L, 15L, 4L, 2L, 6L,
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2L, 148L, 131L, 61L, 20L, 41L, 32L, 4L, 13L, 4L, 3L,
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5L, 2L, 109L, 88L, 41L),
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dim = c(3L, 4L, 7L, 2L),
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dimnames = list(hs = c("L", "M", "U"),
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phs = c("C", "E", "N", "O"),
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fol = c("F1", "F2", "F3", "F4", "F5", "F6", "F7"),
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sex = c("F", "M")))
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loglin(mn, list(hs = 1, phs = 2, fol = 3, sex = 4), param = TRUE)

‎src/library/stats/tests/loglin.Rout.save‎

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R Under development (unstable) (2024-12-28 r87474) -- "Unsuffered Consequences"
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R Under development (unstable) (2024-12-29 r87480) -- "Unsuffered Consequences"
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Copyright (C) 2024 The R Foundation for Statistical Computing
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Platform: aarch64-apple-darwin24.2.0
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@@ -110,7 +110,103 @@ $param$Class
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-0.4115541 -0.5428901 0.3642359 0.5902083
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>
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>
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> ## Versions of some MASS::loglm examples
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> # loglm(~ Type + Origin, xtabs(~ Type + Origin, Cars93))
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> if(require("MASS", quietly = TRUE))
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+ loglin(xtabs(~ Type + Origin, Cars93), list(Type=1, Origin=2))
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2 iterations: deviation 0
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$lrt
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[1] 18.36179
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$pearson
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[1] 14.07985
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$df
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[1] 5
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$margin
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$margin$Type
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[1] "Type"
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$margin$Origin
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[1] "Origin"
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>
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> # fm <- loglm(~ 1 + 2 + 3 + 4, minn38a)
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> mn <- structure(c(53L, 163L, 309L, 13L, 28L, 38L, 7L, 30L, 17L, 76L,
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+ 118L, 89L, 36L, 116L, 225L, 11L, 53L, 68L, 16L, 41L,
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+ 49L, 111L, 214L, 210L, 52L, 162L, 243L, 49L, 129L,
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+ 284L, 28L, 64L, 79L, 521L, 708L, 448L, 48L, 130L,
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+ 237L, 29L, 62L, 63L, 18L, 47L, 57L, 191L, 305L, 219L,
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+ 12L, 35L, 72L, 10L, 37L, 21L, 5L, 11L, 20L, 101L,
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+ 152L, 95L, 9L, 19L, 42L, 15L, 22L, 19L, 1L, 13L, 10L,
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+ 130L, 174L, 105L, 3L, 25L, 36L, 6L, 15L, 19L, 1L, 9L,
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+ 14L, 88L, 158L, 93L, 87L, 216L, 256L, 17L, 14L, 10L,
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+ 3L, 4L, 2L, 105L, 118L, 53L, 72L, 159L, 176L, 18L,
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+ 28L, 22L, 6L, 14L, 8L, 209L, 227L, 95L, 52L, 119L,
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+ 119L, 14L, 44L, 33L, 17L, 13L, 10L, 541L, 578L, 257L,
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+ 88L, 158L, 144L, 14L, 36L, 20L, 9L, 15L, 12L, 328L,
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+ 304L, 115L, 32L, 43L, 42L, 12L, 7L, 7L, 1L, 5L, 2L,
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+ 124L, 119L, 56L, 14L, 24L, 24L, 5L, 15L, 4L, 2L, 6L,
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+ 2L, 148L, 131L, 61L, 20L, 41L, 32L, 4L, 13L, 4L, 3L,
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+ 5L, 2L, 109L, 88L, 41L),
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+ dim = c(3L, 4L, 7L, 2L),
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+ dimnames = list(hs = c("L", "M", "U"),
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+ phs = c("C", "E", "N", "O"),
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+ fol = c("F1", "F2", "F3", "F4", "F5", "F6", "F7"),
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+ sex = c("F", "M")))
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> loglin(mn, list(hs = 1, phs = 2, fol = 3, sex = 4), param = TRUE)
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2 iterations: deviation 1.818989e-12
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$lrt
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[1] 3711.914
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$pearson
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[1] 4161.597
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$df
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[1] 155
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$margin
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$margin$hs
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[1] "hs"
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$margin$phs
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[1] "phs"
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$margin$fol
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[1] "fol"
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$margin$sex
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[1] "sex"
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$param
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$param$`(Intercept)`
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[1] 3.821018
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$param$hs
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L M U
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-0.22433867 0.18885681 0.03548186
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$param$phs
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C E N O
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0.5314514 -0.5543160 -1.2296056 1.2524702
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$param$fol
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F1 F2 F3 F4 F5 F6
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0.07299887 0.25202915 0.98907052 0.44505330 -0.50834637 -0.53414145
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F7
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-0.71666403
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$param$sex
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F M
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0.1181181 -0.1181181
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>
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> proc.time()
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user system elapsed
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0.068 0.012 0.078
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0.074 0.014 0.085

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