Nothing
inst/extdata/micem1.for.R
In forsearch: Diagnostic Analysis Using Forward Search Procedure for Various Models
micem1.for <-
list(`Rows in stage` = list(NULL, NULL, NULL, NULL, c(1L, 12L,
7L, 2L, 13L), c(2L, 3L, 7L, 8L, 12L, 13L), c(1L, 2L, 3L, 7L,
8L, 12L, 13L), c(1L, 2L, 3L, 6L, 7L, 8L, 12L, 13L), c(1L, 2L,
3L, 5L, 6L, 7L, 8L, 12L, 13L), c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 12L, 13L), c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 12L, 13L
), c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L), 1:13),
Family = structure(list(family = "binomial", link = "logit",
linkfun = function (mu)
.Call(C_logit_link, mu), linkinv = function (eta)
.Call(C_logit_linkinv, eta), variance = function (mu)
mu * (1 - mu), dev.resids = function (y, mu, wt)
.Call(C_binomial_dev_resids, y, mu, wt), aic = function (y,
n, mu, wt, dev)
{
m <- if (any(n > 1))
n
else wt
-2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m *
y), round(m), mu, log = TRUE))
}, mu.eta = function (eta)
.Call(C_logit_mu_eta, eta), initialize = quote({
if (NCOL(y) == 1) {
if (is.factor(y))
y <- y != levels(y)[1L]
n <- rep.int(1, nobs)
y[weights == 0] <- 0
if (any(y < 0 | y > 1))
stop("y values must be 0 <= y <= 1")
mustart <- (weights * y + 0.5)/(weights + 1)
m <- weights * y
if ("binomial" == "binomial" && any(abs(m - round(m)) >
0.001))
warning(gettextf("non-integer #successes in a %s glm!",
"binomial"), domain = NA)
}
else if (NCOL(y) == 2) {
if ("binomial" == "binomial" && any(abs(y - round(y)) >
0.001))
warning(gettextf("non-integer counts in a %s glm!",
"binomial"), domain = NA)
n <- (y1 <- y[, 1L]) + y[, 2L]
y <- y1/n
if (any(n0 <- n == 0))
y[n0] <- 0
weights <- weights * n
mustart <- (n * y + 0.5)/(n + 1)
}
else stop(gettextf("for the '%s' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures",
"binomial"), domain = NA)
}), validmu = function (mu)
all(is.finite(mu)) && all(mu > 0 & mu < 1), valideta = function (eta)
TRUE, simulate = function (object, nsim)
{
ftd <- fitted(object)
n <- length(ftd)
ntot <- n * nsim
wts <- object$prior.weights
if (any(wts%%1 != 0))
stop("cannot simulate from non-integer prior.weights")
if (!is.null(m <- object$model)) {
y <- model.response(m)
if (is.factor(y)) {
yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd),
labels = levels(y))
split(yy, rep(seq_len(nsim), each = n))
}
else if (is.matrix(y) && ncol(y) == 2) {
yy <- vector("list", nsim)
for (i in seq_len(nsim)) {
Y <- rbinom(n, size = wts, prob = ftd)
YY <- cbind(Y, wts - Y)
colnames(YY) <- colnames(y)
yy[[i]] <- YY
}
yy
}
else rbinom(ntot, size = wts, prob = ftd)/wts
}
else rbinom(ntot, size = wts, prob = ftd)/wts
}), class = "family"), `Number of model parameters` = 3L,
`Fixed parameter estimates` = structure(list(m = 1:13, `(Intercept)` = c(0,
0, 0, 0, -5.2170560238124279, -5.1543128082172132, -5.2633607247872733,
-5.2188440239571374, -5.2137569221022115, -5.2537897902650101,
-5.5629000834780111, -5.4020575127524477, -5.1994786508089019
), logdose = c(0, 0, 0, 0, 2.1931788281792155, 2.1822518551479178,
2.2211900899285091, 2.1956838399497705, 2.1818029621545185,
2.189420205844463, 2.3167232511312115, 2.2504654161378723,
2.1670668900941292), prep = c(0, 0, 0, 0, -1.1379165290656961,
-1.1656336277601194, -1.1813970334165849, -1.1441581488728725,
-1.1047505801442437, -1.0891347024272062, -1.2567297771557207,
-1.055757452657704, -0.92580231352391595)), class = "data.frame", row.names = c(NA,
-13L)), `Studentized deviance residuals` = structure(list(
c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L), c(1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L), c(-0.011556587243762295,
0.010453513128521441, 0.0063461179931202372, -0.034987115873445884,
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0.11508897119712225, 0.076845536985569785, -0.0098619715938985664,
-0.024290119079168337), c(0, 0, 0, 0, 0.17019028542189174,
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0.31415267082938964, 0.45971985136818999, 0.53359537356161657,
0, 0, 0, 0, 0.17019028542189174, 0.10861046172355526,
0.13248317294766421, 0.14663364744554477, 0.18168784274295152,
0.20703977429406278, 0.31415267082938964, 0.45971985136818999,
0.53359537356161657, 0, 0, 0, 0, 0.17019028542189174,
0.10861046172355526, 0.13248317294766421, 0.14663364744554477,
0.18168784274295152, 0.20703977429406278, 0.31415267082938964,
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0.14663364744554477, 0.18168784274295152, 0.20703977429406278,
0.31415267082938964, 0.45971985136818999, 0.53359537356161657,
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0.13248317294766421, 0.14663364744554477, 0.18168784274295152,
0.20703977429406278, 0.31415267082938964, 0.45971985136818999,
0.53359537356161657, 0, 0, 0, 0, 0.17019028542189174,
0.10861046172355526, 0.13248317294766421, 0.14663364744554477,
0.18168784274295152, 0.20703977429406278, 0.31415267082938964,
0.45971985136818999, 0.53359537356161657, 0, 0, 0, 0,
0.17019028542189174, 0.10861046172355526, 0.13248317294766421,
0.14663364744554477, 0.18168784274295152, 0.20703977429406278,
0.31415267082938964, 0.45971985136818999, 0.53359537356161657
), c(-Inf, Inf, Inf, -Inf, -0.19461388559398021, -0.11446935753446984,
-0.0055943274078398024, 0.067981372627813286, -0.24970750996386787,
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0.016302384804952851, -0.013286135047951255, Inf, Inf,
Inf, -Inf, -0.15368352432760413, -0.13359960473785662,
-0.040230530737237709, 0.025155295995205513, -0.14885756509659898,
0.71815506286765307, 0.38020507692292077, 0.061873910434689351,
0.0043691940133545387, Inf, Inf, Inf, -Inf, -0.14076719449644429,
-0.081170714903316746, 0.0066722437489523841, 0.064059498939255569,
-0.25179423676354601, 0.55587855806710385, 0.2446120759778711,
-0.021452133434194699, -0.045521607350224619)), .Names = c("m",
"obs", "devresid", NA, NA), row.names = c("2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "21",
"31", "41", "51", "61", "71", "81", "91", "101", "111", "121",
"131", "141", "22", "32", "42", "52", "62", "72", "82", "92",
"102", "112", "122", "132", "142", "23", "33", "43", "53",
"63", "73", "83", "93", "103", "113", "123", "133", "143",
"24", "34", "44", "54", "64", "74", "84", "94", "104", "114",
"124", "134", "144", "25", "35", "45", "55", "65", "75",
"85", "95", "105", "115", "125", "135", "145", "26", "36",
"46", "56", "66", "76", "86", "96", "106", "116", "126",
"136", "146", "27", "37", "47", "57", "67", "77", "87", "97",
"107", "117", "127", "137", "147"), class = "data.frame"),
`Residual deviance` = c(0, 0, 0, 0, 0.070094554936652786,
0.042878808629687827, 0.090565696053336794, 0.1326363083101868,
0.27326811475926771, 0.3838321728132022, 1.1086845415844579,
2.6666248039042637, 4.277235090456224), `Null deviance` = c(0,
0, 0, 0, 48.04360640548586, 46.202138803250769, 70.446276616427113,
74.853924359677933, 74.854990170358946, 76.912161088831127,
120.97154757114782, 121.41282675353065, 124.59527356116526
), PhiHat = c(0, 0, 0, 0, 0.035047277468326393, 0.014292936209895942,
0.022641424013334199, 0.026527261662037359, 0.045544685793211283,
0.054833167544743169, 0.13858556769805724, 0.29629164487825155,
0.4277235090456224), `Deviance residuals and augments` = structure(list(
m = c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13,
13, 13, 13, 13, 13, 13), AorD = c("D", "D", "A", "A",
"A", "A", "D", "A", "A", "A", "A", "D", "D", "A", "D",
"D", "A", "A", "A", "D", "D", "A", "A", "A", "D", "D",
"D", "D", "D", "A", "A", "A", "D", "D", "A", "A", "A",
"D", "D", "D", "D", "D", "A", "A", "D", "D", "D", "A",
"A", "A", "D", "D", "D", "D", "D", "A", "D", "D", "D",
"D", "A", "A", "A", "D", "D", "D", "D", "D", "D", "D",
"D", "D", "D", "A", "A", "A", "D", "D", "D", "D", "D",
"D", "D", "D", "D", "D", "D", "A", "A", "D", "D", "D",
"D", "D", "D", "D", "D", "D", "D", "D", "A", "D", "D",
"D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D",
"D", "D", "D"), obs = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L), deviance = c(-0.17659526947778575,
0.14313446840492103, 0.079772680639640353, -0.44324836468507378,
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1.4949670578220211, 1.1235990109379994, 0.087461575457079513,
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1.5676798320293035, 0.34110631076934317, 0.031875175550001057,
0.011103678445603563, 0.34608957229143628, 0.26296165073363859,
-0.28450845790145213, -0.29562871719054268, -0.1130330157087719,
0.013712237233897169, 0.16698167828660976, -1.0733627393738838,
1.449300200016437, 0.99336916554214516, -0.11943464175512958,
-0.33945753405655388, -0.12340653411743029, 0.23514389500412033,
0.19233341814428628, -0.30494243019196776, -0.26163344293664081,
-0.019715193044618374, 0.13679380141253469, 0.29537240410127402,
-1.382340643024121, 1.1108374441997528, 0.64412984453105293,
-0.340361292755196, -0.47577152231491771)), row.names = c(NA,
-117L), class = "data.frame"), AIC = c(0, 0, 0, 0, 24.716528470268173,
28.280899869437896, 31.640256474815942, 35.323844999922201,
39.522393945527448, 43.776036790890217, 47.063520787964009,
52.764539837592665, 58.129585591831031), Leverage = structure(c(5,
5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8,
8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 1, 12,
7, 2, 13, 2, 3, 7, 8, 12, 13, 1, 2, 3, 7, 8, 12, 13, 1, 2,
3, 6, 7, 8, 12, 13, 1, 2, 3, 5, 6, 7, 8, 12, 13, 1, 2, 3,
4, 5, 6, 7, 8, 12, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13,
1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 0.10886614910742531, 0.067034107759986652,
0.16518188465792955, 0.068418465160361375, 0.068210715337121086,
0.070501348980679696, 0.050643813994478551, 0.093498055366094679,
0.15384774550982133, 0.066562524141026466, 0.067553901943371686,
0.079016179621108587, 0.047526510254170276, 0.036999010638492494,
0.09252610923608201, 0.14627946065799785, 0.066022215306246496,
0.066806690708611172, 0.078791560447791398, 0.046950276097985674,
0.034599617951119231, 0.049610427233057156, 0.065038237690089068,
0.10508372710109837, 0.06567737087235441, 0.066327090774016995,
0.077066635580671616, 0.043937558045836933, 0.030543294278274229,
0.023960232922437485, 0.040017442261669534, 0.054011892068824563,
0.091112669167176807, 0.065620787649752654, 0.066249490348339879,
0.070922300245426409, 0.038602603074198424, 0.025697381851501451,
0.019020214813594869, 0.020075296680177542, 0.036767131926425972,
0.051031500621730785, 0.088583226745163501, 0.065622011773858346,
0.066250666575689732, 0.058436879519785753, 0.034040928117684,
0.024325087192754183, 0.019335472503124732, 0.020207143650798204,
0.032968118807644331, 0.043831506979185919, 0.072390149051130975,
0.1368339891646973, 0.053598017198614047, 0.061945924755586981,
0.054550585080105796, 0.032433736014520306, 0.023613561265023633,
0.019066245518981562, 0.019820977153474865, 0.03131364126790602,
0.041116780427369992, 0.066906764505952593, 0.092142782722317046,
0.038329627070115967, 0.038501471577377981, 0.051471165970210703,
0.050452561204707508, 0.030711407125837087, 0.022825170167743929,
0.018739517767700567, 0.019373275705291525, 0.029545852953270571,
0.038244985956485414, 0.061151585032149347, 0.069096932739463796,
0.03953195241357161, 0.029990551692407341, 0.03506241699561026,
0.05003901052814453), .Dim = c(81L, 3L), .Dimnames = list(
c("", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", ""), c("m",
"Observation", "leverage"))), `t statistics` = structure(list(
m = 1:13, `(Intercept)` = c(0, 0, 0, 0, 0, -5.634526210069783,
-6.8448689453698899, -7.1057094288081215, -7.107946675185957,
-7.2435226703460431, -8.7645012776914299, -8.924588655438626,
-9.0739701220364815), logdose = c(0, 0, 0, 0, 0, 5.9125932591339305,
6.8470187271276455, 7.3792346123270622, 7.4220674733641285,
7.4659408090021548, 9.0743249490594238, 9.2677107444207518,
9.458707344287582), prep = c(0, 0, 0, 0, 0, -2.6849020322240427,
-2.7508068180088303, -2.9578039706337056, -2.9847228352685344,
-2.9721509333720375, -4.0994489536816188, -4.1436018010995044,
-4.0237101835736748)), class = "data.frame", row.names = c(NA,
-13L)), Call = quote(temp(initial.sample = 100, cobs = 5,
response.cols = c(4, 8), indep.cols = c(9, 3), family = binomial("logit"),
data = micem1)))
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micem1.for <-
list(`Rows in stage` = list(NULL, NULL, NULL, NULL, c(1L, 12L,
7L, 2L, 13L), c(2L, 3L, 7L, 8L, 12L, 13L), c(1L, 2L, 3L, 7L,
8L, 12L, 13L), c(1L, 2L, 3L, 6L, 7L, 8L, 12L, 13L), c(1L, 2L,
3L, 5L, 6L, 7L, 8L, 12L, 13L), c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 12L, 13L), c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 12L, 13L
), c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L), 1:13),
Family = structure(list(family = "binomial", link = "logit",
linkfun = function (mu)
.Call(C_logit_link, mu), linkinv = function (eta)
.Call(C_logit_linkinv, eta), variance = function (mu)
mu * (1 - mu), dev.resids = function (y, mu, wt)
.Call(C_binomial_dev_resids, y, mu, wt), aic = function (y,
n, mu, wt, dev)
{
m <- if (any(n > 1))
n
else wt
-2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m *
y), round(m), mu, log = TRUE))
}, mu.eta = function (eta)
.Call(C_logit_mu_eta, eta), initialize = quote({
if (NCOL(y) == 1) {
if (is.factor(y))
y <- y != levels(y)[1L]
n <- rep.int(1, nobs)
y[weights == 0] <- 0
if (any(y < 0 | y > 1))
stop("y values must be 0 <= y <= 1")
mustart <- (weights * y + 0.5)/(weights + 1)
m <- weights * y
if ("binomial" == "binomial" && any(abs(m - round(m)) >
0.001))
warning(gettextf("non-integer #successes in a %s glm!",
"binomial"), domain = NA)
}
else if (NCOL(y) == 2) {
if ("binomial" == "binomial" && any(abs(y - round(y)) >
0.001))
warning(gettextf("non-integer counts in a %s glm!",
"binomial"), domain = NA)
n <- (y1 <- y[, 1L]) + y[, 2L]
y <- y1/n
if (any(n0 <- n == 0))
y[n0] <- 0
weights <- weights * n
mustart <- (n * y + 0.5)/(n + 1)
}
else stop(gettextf("for the '%s' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures",
"binomial"), domain = NA)
}), validmu = function (mu)
all(is.finite(mu)) && all(mu > 0 & mu < 1), valideta = function (eta)
TRUE, simulate = function (object, nsim)
{
ftd <- fitted(object)
n <- length(ftd)
ntot <- n * nsim
wts <- object$prior.weights
if (any(wts%%1 != 0))
stop("cannot simulate from non-integer prior.weights")
if (!is.null(m <- object$model)) {
y <- model.response(m)
if (is.factor(y)) {
yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd),
labels = levels(y))
split(yy, rep(seq_len(nsim), each = n))
}
else if (is.matrix(y) && ncol(y) == 2) {
yy <- vector("list", nsim)
for (i in seq_len(nsim)) {
Y <- rbinom(n, size = wts, prob = ftd)
YY <- cbind(Y, wts - Y)
colnames(YY) <- colnames(y)
yy[[i]] <- YY
}
yy
}
else rbinom(ntot, size = wts, prob = ftd)/wts
}
else rbinom(ntot, size = wts, prob = ftd)/wts
}), class = "family"), `Number of model parameters` = 3L,
`Fixed parameter estimates` = structure(list(m = 1:13, `(Intercept)` = c(0,
0, 0, 0, -5.2170560238124279, -5.1543128082172132, -5.2633607247872733,
-5.2188440239571374, -5.2137569221022115, -5.2537897902650101,
-5.5629000834780111, -5.4020575127524477, -5.1994786508089019
), logdose = c(0, 0, 0, 0, 2.1931788281792155, 2.1822518551479178,
2.2211900899285091, 2.1956838399497705, 2.1818029621545185,
2.189420205844463, 2.3167232511312115, 2.2504654161378723,
2.1670668900941292), prep = c(0, 0, 0, 0, -1.1379165290656961,
-1.1656336277601194, -1.1813970334165849, -1.1441581488728725,
-1.1047505801442437, -1.0891347024272062, -1.2567297771557207,
-1.055757452657704, -0.92580231352391595)), class = "data.frame", row.names = c(NA,
-13L)), `Studentized deviance residuals` = structure(list(
c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L), c(1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L), c(-0.011556587243762295,
0.010453513128521441, 0.0063461179931202372, -0.034987115873445884,
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0.11827844870771967, 0.08659237598193964, 0.0072417919791428753,
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0.031162504894427734, 0.025168414380954796, -0.02117839774564273,
-0.026155442869957191, -0.01451031475666309, -0.005329868361437784,
0.0036886128043492894, -0.027045609878369542, 0.14868666212425735,
0.1194424403782291, 0.028444664908604089, 0.0023313817117190938,
0.00071181997853358991, 0.025001839115046331, 0.020805141545657713,
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0.18168784274295152, 0.20703977429406278, 0.31415267082938964,
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0.17019028542189174, 0.10861046172355526, 0.13248317294766421,
0.14663364744554477, 0.18168784274295152, 0.20703977429406278,
0.31415267082938964, 0.45971985136818999, 0.53359537356161657,
0, 0, 0, 0, 0.17019028542189174, 0.10861046172355526,
0.13248317294766421, 0.14663364744554477, 0.18168784274295152,
0.20703977429406278, 0.31415267082938964, 0.45971985136818999,
0.53359537356161657, 0, 0, 0, 0, 0.17019028542189174,
0.10861046172355526, 0.13248317294766421, 0.14663364744554477,
0.18168784274295152, 0.20703977429406278, 0.31415267082938964,
0.45971985136818999, 0.53359537356161657, 0, 0, 0, 0,
0.17019028542189174, 0.10861046172355526, 0.13248317294766421,
0.14663364744554477, 0.18168784274295152, 0.20703977429406278,
0.31415267082938964, 0.45971985136818999, 0.53359537356161657,
0, 0, 0, 0, 0.17019028542189174, 0.10861046172355526,
0.13248317294766421, 0.14663364744554477, 0.18168784274295152,
0.20703977429406278, 0.31415267082938964, 0.45971985136818999,
0.53359537356161657, 0, 0, 0, 0, 0.17019028542189174,
0.10861046172355526, 0.13248317294766421, 0.14663364744554477,
0.18168784274295152, 0.20703977429406278, 0.31415267082938964,
0.45971985136818999, 0.53359537356161657, 0, 0, 0, 0,
0.17019028542189174, 0.10861046172355526, 0.13248317294766421,
0.14663364744554477, 0.18168784274295152, 0.20703977429406278,
0.31415267082938964, 0.45971985136818999, 0.53359537356161657
), c(-Inf, Inf, Inf, -Inf, -0.19461388559398021, -0.11446935753446984,
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-0.25666834517003645, 0.57161413391408256, 0.27582512459473352,
0.015785021427881227, -0.012864493688178895, -Inf, Inf,
Inf, -Inf, -0.2305188972688913, -0.17307618167108671,
-0.050042164814164805, 0.03715779656319388, -0.23223639792784023,
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0.016754361422934471, -0.013654487436687467, -Inf, Inf,
Inf, -Inf, -0.15804686608834559, -0.063622603483285606,
0.030931608371126285, 0.091395031113260111, -0.25695606930625864,
0.57128370194089562, 0.27563787935760165, 0.015752619682596475,
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-0.25209258159066145, 0.57688027514605578, 0.27881331633922446,
0.016302384804952851, -0.013286135047951255, Inf, Inf,
Inf, -Inf, -0.15368352432760413, -0.13359960473785662,
-0.040230530737237709, 0.025155295995205513, -0.14885756509659898,
0.71815506286765307, 0.38020507692292077, 0.061873910434689351,
0.0043691940133545387, Inf, Inf, Inf, -Inf, -0.14076719449644429,
-0.081170714903316746, 0.0066722437489523841, 0.064059498939255569,
-0.25179423676354601, 0.55587855806710385, 0.2446120759778711,
-0.021452133434194699, -0.045521607350224619)), .Names = c("m",
"obs", "devresid", NA, NA), row.names = c("2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "21",
"31", "41", "51", "61", "71", "81", "91", "101", "111", "121",
"131", "141", "22", "32", "42", "52", "62", "72", "82", "92",
"102", "112", "122", "132", "142", "23", "33", "43", "53",
"63", "73", "83", "93", "103", "113", "123", "133", "143",
"24", "34", "44", "54", "64", "74", "84", "94", "104", "114",
"124", "134", "144", "25", "35", "45", "55", "65", "75",
"85", "95", "105", "115", "125", "135", "145", "26", "36",
"46", "56", "66", "76", "86", "96", "106", "116", "126",
"136", "146", "27", "37", "47", "57", "67", "77", "87", "97",
"107", "117", "127", "137", "147"), class = "data.frame"),
`Residual deviance` = c(0, 0, 0, 0, 0.070094554936652786,
0.042878808629687827, 0.090565696053336794, 0.1326363083101868,
0.27326811475926771, 0.3838321728132022, 1.1086845415844579,
2.6666248039042637, 4.277235090456224), `Null deviance` = c(0,
0, 0, 0, 48.04360640548586, 46.202138803250769, 70.446276616427113,
74.853924359677933, 74.854990170358946, 76.912161088831127,
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