1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756
|
# Part of the rstanarm package for estimating model parameters
# Copyright (C) 2015, 2016, 2017 Trustees of Columbia University
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 3
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#' Logit and inverse logit
#'
#' @export
#' @param x Numeric vector.
#' @return A numeric vector the same length as \code{x}.
logit <- function(x) stats::qlogis(x)
#' @rdname logit
#' @export
invlogit <- function(x) stats::plogis(x)
# Set arguments for sampling
#
# Prepare a list of arguments to use with \code{rstan::sampling} via
# \code{do.call}.
#
# @param object The stanfit object to use for sampling.
# @param user_dots The contents of \code{...} from the user's call to
# the \code{stan_*} modeling function.
# @param user_adapt_delta The value for \code{adapt_delta} specified by the
# user.
# @param prior Prior distribution list (can be NULL).
# @param ... Other arguments to \code{\link[rstan]{sampling}} not coming from
# \code{user_dots} (e.g. \code{data}, \code{pars}, \code{init}, etc.)
# @return A list of arguments to use for the \code{args} argument for
# \code{do.call(sampling, args)}.
set_sampling_args <- function(object, prior, user_dots = list(),
user_adapt_delta = NULL, ...) {
args <- list(object = object, ...)
unms <- names(user_dots)
for (j in seq_along(user_dots)) {
args[[unms[j]]] <- user_dots[[j]]
}
defaults <- default_stan_control(prior = prior,
adapt_delta = user_adapt_delta)
if (!"control" %in% unms) {
# no user-specified 'control' argument
args$control <- defaults
} else {
# user specifies a 'control' argument
if (!is.null(user_adapt_delta)) {
# if user specified adapt_delta argument to stan_* then
# set control$adapt_delta to user-specified value
args$control$adapt_delta <- user_adapt_delta
} else {
# use default adapt_delta for the user's chosen prior
args$control$adapt_delta <- defaults$adapt_delta
}
if (is.null(args$control$max_treedepth)) {
# if user's 'control' has no max_treedepth set it to rstanarm default
args$control$max_treedepth <- defaults$max_treedepth
}
}
args$save_warmup <- FALSE
return(args)
}
# Default control arguments for sampling
#
# Called by set_sampling_args to set the default 'control' argument for
# \code{rstan::sampling} if none specified by user. This allows the value of
# \code{adapt_delta} to depend on the prior.
#
# @param prior Prior distribution list (can be NULL).
# @param adapt_delta User's \code{adapt_delta} argument.
# @param max_treedepth Default for \code{max_treedepth}.
# @return A list with \code{adapt_delta} and \code{max_treedepth}.
default_stan_control <- function(prior, adapt_delta = NULL,
max_treedepth = 15L) {
if (!length(prior)) {
if (is.null(adapt_delta)) adapt_delta <- 0.95
} else if (is.null(adapt_delta)) {
adapt_delta <- switch(prior$dist,
"R2" = 0.99,
"hs" = 0.99,
"hs_plus" = 0.99,
"lasso" = 0.99,
"product_normal" = 0.99,
0.95) # default
}
nlist(adapt_delta, max_treedepth)
}
# Test if an object is a stanreg object
#
# @param x The object to test.
is.stanreg <- function(x) inherits(x, "stanreg")
# Throw error if object isn't a stanreg object
#
# @param x The object to test.
validate_stanreg_object <- function(x, call. = FALSE) {
if (!is.stanreg(x))
stop("Object is not a stanreg object.", call. = call.)
}
# Test for a given family
#
# @param x A character vector (probably x = family(fit)$family)
is.binomial <- function(x) x == "binomial"
is.gaussian <- function(x) x == "gaussian"
is.gamma <- function(x) x == "Gamma"
is.ig <- function(x) x == "inverse.gaussian"
is.nb <- function(x) x == "neg_binomial_2"
is.poisson <- function(x) x == "poisson"
is.beta <- function(x) x == "beta" || x == "Beta regression"
# test if a stanreg object has class clogit
is_clogit <- function(object) {
is(object, "clogit")
}
# test if a stanreg object has class polr
is_polr <- function(object) {
is(object, "polr")
}
# test if a stanreg object is a scobit model
is_scobit <- function(object) {
validate_stanreg_object(object)
if (!is_polr(object))
return(FALSE)
return("alpha" %in% rownames(object$stan_summary))
}
# Test for a given estimation method
#
# @param x A stanreg object.
used.optimizing <- function(x) {
x$algorithm == "optimizing"
}
used.sampling <- function(x) {
x$algorithm == "sampling"
}
used.variational <- function(x) {
x$algorithm %in% c("meanfield", "fullrank")
}
# Test if stanreg object used stan_[gn]lmer
#
# @param x A stanreg object.
is.mer <- function(x) {
stopifnot(is.stanreg(x))
check1 <- inherits(x, "lmerMod")
check2 <- !is.null(x$glmod)
if (check1 && !check2) {
stop("Bug found. 'x' has class 'lmerMod' but no 'glmod' component.")
} else if (!check1 && check2) {
stop("Bug found. 'x' has 'glmod' component but not class 'lmerMod'.")
}
isTRUE(check1 && check2)
}
# Test if stanreg object used stan_nlmer
#
# @param x A stanreg object.
is.nlmer <- function(x) {
is.mer(x) && inherits(x, "nlmerMod")
}
# Consistent error message to use when something is only available for
# models fit using MCMC
#
# @param what An optional message to prepend to the default message.
STOP_sampling_only <- function(what) {
msg <- "only available for models fit using MCMC (algorithm='sampling')."
if (!missing(what))
msg <- paste(what, msg)
stop(msg, call. = FALSE)
}
# Consistent error message to use when something is only available for models
# fit using MCMC or VB but not optimization
#
# @param what An optional message to prepend to the default message.
STOP_not_optimizing <- function(what) {
msg <- "not available for models fit using algorithm='optimizing'."
if (!missing(what))
msg <- paste(what, msg)
stop(msg, call. = FALSE)
}
# Consistent error message to use when something is only available for models
# fit using MCMC or optimization but not VB
#
# @param what An optional message to prepend to the default message.
STOP_not_VB <- function(what) {
msg <- "not available for models fit using algorithm='meanfield|fullrank'."
if (!missing(what))
msg <- paste(what, msg)
stop(msg, call. = FALSE)
}
# Message to issue when fitting model with ADVI but 'QR=FALSE'.
recommend_QR_for_vb <- function() {
message(
"Setting 'QR' to TRUE can often be helpful when using ",
"one of the variational inference algorithms. ",
"See the documentation for the 'QR' argument."
)
}
# Issue warning if high rhat values
#
# @param rhats Vector of rhat values.
# @param threshold Threshold value. If any rhat values are above threshold a
# warning is issued.
check_rhats <- function(rhats, threshold = 1.1, check_lp = FALSE) {
if (!check_lp)
rhats <- rhats[!names(rhats) %in% c("lp__", "log-posterior")]
if (any(rhats > threshold, na.rm = TRUE))
warning("Markov chains did not converge! Do not analyze results!",
call. = FALSE, noBreaks. = TRUE)
}
# If y is a 1D array keep any names but convert to vector (used in stan_glm)
#
# @param y Result of calling model.response
array1D_check <- function(y) {
if (length(dim(y)) == 1L) {
nms <- rownames(y)
dim(y) <- NULL
if (!is.null(nms))
names(y) <- nms
}
return(y)
}
# Check for a binomial model with Y given as proportion of successes and weights
# given as total number of trials
#
binom_y_prop <- function(y, family, weights) {
if (!is.binomial(family$family))
return(FALSE)
yprop <- NCOL(y) == 1L &&
is.numeric(y) &&
any(y > 0 & y < 1) &&
!any(y < 0 | y > 1)
if (!yprop)
return(FALSE)
wtrials <- !identical(weights, double(0)) &&
all(weights > 0) &&
all(abs(weights - round(weights)) < .Machine$double.eps^0.5)
isTRUE(wtrials)
}
# Convert 2-level factor to 0/1
fac2bin <- function(y) {
if (!is.factor(y))
stop("Bug found: non-factor as input to fac2bin.",
call. = FALSE)
if (!identical(nlevels(y), 2L))
stop("Bug found: factor with nlevels != 2 as input to fac2bin.",
call. = FALSE)
as.integer(y != levels(y)[1L])
}
# Check weights argument
#
# @param w The \code{weights} argument specified by user or the result of
# calling \code{model.weights} on a model frame.
# @return If no error is thrown then \code{w} is returned.
validate_weights <- function(w) {
if (missing(w) || is.null(w)) {
w <- double(0)
} else {
if (!is.numeric(w))
stop("'weights' must be a numeric vector.",
call. = FALSE)
if (any(w < 0))
stop("Negative weights are not allowed.",
call. = FALSE)
}
return(w)
}
# Check offset argument
#
# @param o The \code{offset} argument specified by user or the result of calling
# \code{model.offset} on a model frame.
# @param y The result of calling \code{model.response} on a model frame.
# @return If no error is thrown then \code{o} is returned.
validate_offset <- function(o, y) {
if (is.null(o)) {
o <- double(0)
} else {
if (length(o) != NROW(y))
stop(gettextf("Number of offsets is %d but should be %d (number of observations)",
length(o), NROW(y)), domain = NA, call. = FALSE)
}
return(o)
}
# Check family argument
#
# @param f The \code{family} argument specified by user (or the default).
# @return If no error is thrown, then either \code{f} itself is returned (if
# already a family) or the family object created from \code{f} is returned (if
# \code{f} is a string or function).
validate_family <- function(f) {
if (is.character(f))
f <- get(f, mode = "function", envir = parent.frame(2))
if (is.function(f))
f <- f()
if (!is(f, "family"))
stop("'family' must be a family.", call. = FALSE)
return(f)
}
# Check for glmer syntax in formulas for non-glmer models
#
# @param f The model \code{formula}.
# @return Nothing is returned but an error might be thrown
validate_glm_formula <- function(f) {
if (any(grepl("\\|", f)))
stop("Using '|' in model formula not allowed. ",
"Maybe you meant to use 'stan_(g)lmer'?", call. = FALSE)
}
# Check if model formula has something on the LHS of ~
# @param f Model formula
# @return FALSE if there is no outcome on the LHS of the formula
has_outcome_variable <- function(f) {
tt <- terms(as.formula(f))
if (attr(tt, "response") == 0) {
return(FALSE)
} else {
return(TRUE)
}
}
# Check if any variables in a model frame are constants
#
# exceptions: constant variable of all 1's is allowed and outcomes with all 0s
# or 1s are allowed (e.g., for binomial models)
#
# @param mf A model frame or model matrix
# @return If no constant variables are found mf is returned, otherwise an error
# is thrown.
check_constant_vars <- function(mf) {
mf1 <- mf
if (NCOL(mf[, 1]) == 2 || all(mf[, 1] %in% c(0, 1))) {
mf1 <- mf[, -1, drop=FALSE]
}
lu1 <- function(x) !all(x == 1) && length(unique(x)) == 1
nocheck <- c("(weights)", "(offset)", "(Intercept)")
sel <- !colnames(mf1) %in% nocheck
is_constant <- apply(mf1[, sel, drop=FALSE], 2, lu1)
if (any(is_constant)) {
stop("Constant variable(s) found: ",
paste(names(is_constant)[is_constant], collapse = ", "),
call. = FALSE)
}
return(mf)
}
# Grep for "b" parameters (ranef)
#
# @param x Character vector (often rownames(fit$stan_summary))
# @param ... Passed to grep
b_names <- function(x, ...) {
grep("^b\\[", x, ...)
}
# Return names of the last dimension in a matrix/array (e.g. colnames if matrix)
#
# @param x A matrix or array
last_dimnames <- function(x) {
ndim <- length(dim(x))
dimnames(x)[[ndim]]
}
# Get the correct column name to use for selecting the median
#
# @param algorithm String naming the estimation algorithm (probably
# \code{fit$algorithm}).
# @return Either \code{"50%"} or \code{"Median"} depending on \code{algorithm}.
select_median <- function(algorithm) {
switch(algorithm,
sampling = "50%",
meanfield = "50%",
fullrank = "50%",
optimizing = "Median",
stop("Bug found (incorrect algorithm name passed to select_median)",
call. = FALSE))
}
# Regex parameter selection
#
# @param x stanreg object
# @param regex_pars Character vector of patterns
grep_for_pars <- function(x, regex_pars) {
validate_stanreg_object(x)
if (used.optimizing(x)) {
warning("'regex_pars' ignored for models fit using algorithm='optimizing'.",
call. = FALSE)
return(NULL)
}
stopifnot(is.character(regex_pars))
out <- unlist(lapply(seq_along(regex_pars), function(j) {
grep(regex_pars[j], rownames(x$stan_summary), value = TRUE)
}))
if (!length(out))
stop("No matches for 'regex_pars'.", call. = FALSE)
return(out)
}
# Combine pars and regex_pars
#
# @param x stanreg object
# @param pars Character vector of parameter names
# @param regex_pars Character vector of patterns
collect_pars <- function(x, pars = NULL, regex_pars = NULL) {
if (is.null(pars) && is.null(regex_pars))
return(NULL)
if (!is.null(pars))
pars[pars == "varying"] <- "b"
if (!is.null(regex_pars))
pars <- c(pars, grep_for_pars(x, regex_pars))
unique(pars)
}
# Get the posterior sample size
#
# @param x A stanreg object
# @return the posterior sample size (or size of sample from approximate posterior)
posterior_sample_size <- function(x) {
validate_stanreg_object(x)
if (used.optimizing(x)) {
return(NROW(x$asymptotic_sampling_dist))
}
pss <- x$stanfit@sim$n_save
if (used.variational(x))
return(pss)
sum(pss - x$stanfit@sim$warmup2)
}
# If a is NULL (and Inf, respectively) return b, otherwise just return a
# @param a,b Objects
`%ORifNULL%` <- function(a, b) {
if (is.null(a)) b else a
}
`%ORifINF%` <- function(a, b) {
if (a == Inf) b else a
}
# Maybe broadcast
#
# @param x A vector or scalar.
# @param n Number of replications to possibly make.
# @return If \code{x} has no length the \code{0} replicated \code{n} times is
# returned. If \code{x} has length 1, the \code{x} replicated \code{n} times
# is returned. Otherwise \code{x} itself is returned.
maybe_broadcast <- function(x, n) {
if (!length(x)) {
rep(0, times = n)
} else if (length(x) == 1L) {
rep(x, times = n)
} else {
x
}
}
# Create a named list using specified names or, if names are omitted, using the
# names of the objects in the list
#
# @param ... Objects to include in the list.
# @return A named list.
nlist <- function(...) {
m <- match.call()
out <- list(...)
no_names <- is.null(names(out))
has_name <- if (no_names) FALSE else nzchar(names(out))
if (all(has_name))
return(out)
nms <- as.character(m)[-1L]
if (no_names) {
names(out) <- nms
} else {
names(out)[!has_name] <- nms[!has_name]
}
return(out)
}
# Check and set scale parameters for priors
#
# @param scale Value of scale parameter (can be NULL).
# @param default Default value to use if \code{scale} is NULL.
# @param link String naming the link function or NULL.
# @return If a probit link is being used, \code{scale} (or \code{default} if
# \code{scale} is NULL) is scaled by \code{dnorm(0) / dlogis(0)}. Otherwise
# either \code{scale} or \code{default} is returned.
set_prior_scale <- function(scale, default, link) {
stopifnot(is.numeric(default), is.character(link) || is.null(link))
if (is.null(scale))
scale <- default
if (isTRUE(link == "probit"))
scale <- scale * dnorm(0) / dlogis(0)
return(scale)
}
# Methods for creating linear predictor
#
# Make linear predictor vector from x and point estimates for beta, or linear
# predictor matrix from x and full posterior sample of beta.
#
# @param beta A vector or matrix or parameter estimates.
# @param x Predictor matrix.
# @param offset Optional offset vector.
# @return A vector or matrix.
linear_predictor <- function(beta, x, offset = NULL) {
UseMethod("linear_predictor")
}
linear_predictor.default <- function(beta, x, offset = NULL) {
eta <- as.vector(if (NCOL(x) == 1L) x * beta else x %*% beta)
if (length(offset))
eta <- eta + offset
return(eta)
}
linear_predictor.matrix <- function(beta, x, offset = NULL) {
if (NCOL(beta) == 1L)
beta <- as.matrix(beta)
eta <- beta %*% t(x)
if (length(offset))
eta <- sweep(eta, 2L, offset, `+`)
return(eta)
}
#' Extract X, Y or Z from a stanreg object
#'
#' @keywords internal
#' @export
#' @templateVar stanregArg object
#' @template args-stanreg-object
#' @param ... Other arguments passed to methods. For a \code{stanmvreg} object
#' this can be an integer \code{m} specifying the submodel.
#' @return For \code{get_x} and \code{get_z}, a matrix. For \code{get_y}, either
#' a vector or a matrix, depending on how the response variable was specified.
get_y <- function(object, ...) UseMethod("get_y")
#' @rdname get_y
#' @export
get_x <- function(object, ...) UseMethod("get_x")
#' @rdname get_y
#' @export
get_z <- function(object, ...) UseMethod("get_z")
#' @export
get_y.default <- function(object, ...) {
object[["y"]] %ORifNULL% model.response(model.frame(object))
}
#' @export
get_x.default <- function(object, ...) {
object[["x"]] %ORifNULL% model.matrix(object)
}
#' @export
get_x.gamm4 <- function(object, ...) {
as.matrix(object[["x"]])
}
#' @export
get_x.lmerMod <- function(object, ...) {
object$glmod$X %ORifNULL% stop("X not found")
}
#' @export
get_z.lmerMod <- function(object, ...) {
Zt <- object$glmod$reTrms$Zt %ORifNULL% stop("Z not found")
t(Zt)
}
#' @export
get_y.stanmvreg <- function(object, m = NULL, ...) {
ret <- fetch(object$glmod, "y", "y") %ORifNULL% stop("y not found")
stub <- get_stub(object)
if (!is.null(m)) ret[[m]] else list_nms(ret, stub = stub)
}
#' @export
get_x.stanmvreg <- function(object, m = NULL, ...) {
ret <- fetch(object$glmod, "x", "x") %ORifNULL% stop("X not found")
stub <- get_stub(object)
if (!is.null(m)) ret[[m]] else list_nms(ret, stub = stub)
}
#' @export
get_z.stanmvreg <- function(object, m = NULL, ...) {
Zt <- fetch(object$glmod, "reTrms", "Zt") %ORifNULL% stop("Z not found")
ret <- lapply(Zt, t)
stub <- get_stub(object)
if (!is.null(m)) ret[[m]] else list_nms(ret, stub = stub)
}
# Get inverse link function
#
# @param x A stanreg object, family object, or string.
# @param ... Other arguments passed to methods. For a \code{stanmvreg} object
# this can be an integer \code{m} specifying the submodel.
# @return The inverse link function associated with x.
linkinv <- function(x, ...) UseMethod("linkinv")
linkinv.stanreg <- function(x, ...) {
if (is(x, "polr")) polr_linkinv(x) else family(x)$linkinv
}
linkinv.stanmvreg <- function(x, m = NULL, ...) {
ret <- lapply(family(x), `[[`, "linkinv")
stub <- get_stub(x)
if (!is.null(m)) ret[[m]] else list_nms(ret, stub = stub)
}
linkinv.family <- function(x, ...) {
x$linkinv
}
linkinv.character <- function(x, ...) {
stopifnot(length(x) == 1)
polr_linkinv(x)
}
# Make inverse link function for stan_polr models, neglecting any
# exponent in the scobit case
#
# @param x A stanreg object or character scalar giving the "method".
# @return The inverse link function associated with x.
polr_linkinv <- function(x) {
if (is.stanreg(x) && is(x, "polr")) {
method <- x$method
} else if (is.character(x) && length(x) == 1L) {
method <- x
} else {
stop("'x' should be a stanreg object created by stan_polr ",
"or a single string.")
}
if (is.null(method) || method == "logistic")
method <- "logit"
if (method == "loglog")
return(pgumbel)
make.link(method)$linkinv
}
# Wrapper for rstan::summary
# @param stanfit A stanfit object created using rstan::sampling or rstan::vb
# @return A matrix of summary stats
make_stan_summary <- function(stanfit) {
levs <- c(0.5, 0.8, 0.95)
qq <- (1 - levs) / 2
probs <- sort(c(0.5, qq, 1 - qq))
rstan::summary(stanfit, probs = probs, digits = 10)$summary
}
check_reTrms <- function(reTrms) {
stopifnot(is.list(reTrms))
nms <- names(reTrms$cnms)
dupes <- duplicated(nms)
for (i in which(dupes)) {
original <- reTrms$cnms[[nms[i]]]
dupe <- reTrms$cnms[[i]]
overlap <- dupe %in% original
if (any(overlap))
stop("rstanarm does not permit formulas with duplicate group-specific terms.\n",
"In this case ", nms[i], " is used as a grouping factor multiple times and\n",
dupe[overlap], " is included multiple times.\n",
"Consider using || or -1 in your formulas to prevent this from happening.")
}
return(invisible(NULL))
}
#' @importFrom lme4 glmerControl
# @param ignore_lhs ignore or throw error if LHS of formula is missing? (relevant if prior_PD is TRUE)
make_glmerControl <- function(..., ignore_lhs = FALSE, ignore_x_scale = FALSE) {
glmerControl(check.nlev.gtreq.5 = "ignore",
check.nlev.gtr.1 = "stop",
check.nobs.vs.rankZ = "ignore",
check.nobs.vs.nlev = "ignore",
check.nobs.vs.nRE = "ignore",
check.formula.LHS = if (ignore_lhs) "ignore" else "stop",
check.scaleX = if (ignore_x_scale) "ignore" else "warning",
...)
}
# Check if a fitted model (stanreg object) has weights
#
# @param x stanreg object
# @return Logical. Only TRUE if x$weights has positive length and the elements
# of x$weights are not all the same.
#
model_has_weights <- function(x) {
wts <- x[["weights"]]
if (!length(wts)) {
FALSE
} else if (all(wts == wts[1])) {
FALSE
} else {
TRUE
}
}
# Check that a stanfit object (or list returned by rstan::optimizing) is valid
#
check_stanfit <- function(x) {
if (is.list(x)) {
if (!all(c("par", "value") %in% names(x)))
stop("Invalid object produced please report bug")
}
else {
stopifnot(is(x, "stanfit"))
if (x@mode != 0)
stop("Invalid stanfit object produced please report bug")
}
return(TRUE)
}
# Validate data argument
#
# Make sure that, if specified, data is a data frame. If data is not missing
# then dimension reduction is also performed on variables (i.e., a one column
# matrix inside a data frame is converted to a vector).
#
# @param data User's data argument
# @param if_missing Object to return if data is missing/null
# @return If no error is thrown, data itself is returned if not missing/null,
# otherwise if_missing is returned.
#
drop_redundant_dims <- function(data) {
drop_dim <- sapply(data, function(v) is.matrix(v) && NCOL(v) == 1)
data[, drop_dim] <- lapply(data[, drop_dim, drop=FALSE], drop)
return(data)
}
validate_data <- function(data, if_missing = NULL) {
if (missing(data) || is.null(data)) {
warn_data_arg_missing()
return(if_missing)
}
if (!is.data.frame(data)) {
stop("'data' must be a data frame.", call. = FALSE)
}
# drop other classes (e.g. 'tbl_df', 'tbl', 'data.table')
data <- as.data.frame(data)
drop_redundant_dims(data)
}
# Throw a warning if 'data' argument to modeling function is missing
warn_data_arg_missing <- function() {
warning(
"Omitting the 'data' argument is not recommended ",
"and may not be allowed in future versions of rstanarm. ",
"Some post-estimation functions (in particular 'update', 'loo', 'kfold') ",
"are not guaranteed to work properly unless 'data' is specified as a data frame.",
call. = FALSE
)
}
# Validate newdata argument for posterior_predict, log_lik, etc.
#
# Checks for NAs in used variables only (but returns all variables),
# and also drops any unused dimensions in variables (e.g. a one column
# matrix inside a data frame is converted to a vector).
#
# @param object stanreg object
# @param newdata NULL or a data frame
# @pararm m For stanmvreg objects, the submodel (passed to formula())
# @return NULL or a data frame
#
validate_newdata <- function(object, newdata = NULL, m = NULL) {
if (is.null(newdata)) {
return(newdata)
}
if (!is.data.frame(newdata)) {
stop("If 'newdata' is specified it must be a data frame.", call. = FALSE)
}
# drop other classes (e.g. 'tbl_df', 'tbl')
newdata <- as.data.frame(newdata)
# only check for NAs in used variables
vars <- all.vars(formula(object, m = m))
newdata_check <- newdata[, colnames(newdata) %in% vars, drop=FALSE]
if (any(is.na(newdata_check))) {
stop("NAs are not allowed in 'newdata'.", call. = FALSE)
}
newdata <- drop_redundant_dims(newdata)
return(newdata)
}
#---------------------- for stan_{mvmer,jm} only -----------------------------
# Return a list (or vector if unlist = TRUE) which
# contains the embedded elements in list x named y
fetch <- function(x, y, z = NULL, zz = NULL, null_to_zero = FALSE,
pad_length = NULL, unlist = FALSE) {
ret <- lapply(x, `[[`, y)
if (!is.null(z))
ret <- lapply(ret, `[[`, z)
if (!is.null(zz))
ret <- lapply(ret, `[[`, zz)
if (null_to_zero)
ret <- lapply(ret, function(i) ifelse(is.null(i), 0L, i))
if (!is.null(pad_length)) {
padding <- rep(list(0L), pad_length - length(ret))
ret <- c(ret, padding)
}
if (unlist) unlist(ret) else ret
}
# Wrapper for using fetch with unlist = TRUE
fetch_ <- function(x, y, z = NULL, zz = NULL, null_to_zero = FALSE,
pad_length = NULL) {
fetch(x = x, y = y, z = z, zz = zz, null_to_zero = null_to_zero,
pad_length = pad_length, unlist = TRUE)
}
# Wrapper for using fetch with unlist = TRUE and
# returning array. Also converts logical to integer.
fetch_array <- function(x, y, z = NULL, zz = NULL, null_to_zero = FALSE,
pad_length = NULL) {
val <- fetch(x = x, y = y, z = z, zz = zz, null_to_zero = null_to_zero,
pad_length = pad_length, unlist = TRUE)
if (is.logical(val))
val <- as.integer(val)
as.array(val)
}
# Unlist the result from an lapply call
#
# @param X,FUN,... Same as lapply
uapply <- function(X, FUN, ...) {
unlist(lapply(X, FUN, ...))
}
# A refactored version of mapply with SIMPLIFY = FALSE
#
# @param FUN,... Same as mapply
# @param arg Passed to MoreArgs
xapply <- function(..., FUN, args = NULL) {
mapply(FUN, ..., MoreArgs = args, SIMPLIFY = FALSE)
}
# Test if family object corresponds to a linear mixed model
#
# @param x A family object
is.lmer <- function(x) {
if (!is(x, "family"))
stop("x should be a family object.", call. = FALSE)
isTRUE((x$family == "gaussian") && (x$link == "identity"))
}
# Split a 2D array into nsplits subarrays, returned as a list
#
# @param x A 2D array or matrix
# @param nsplits An integer, the number of subarrays or submatrices
# @param bycol A logical, if TRUE then the subarrays are generated by
# splitting the columns of x
# @return A list of nsplits arrays or matrices
array2list <- function(x, nsplits, bycol = TRUE) {
len <- if (bycol) ncol(x) else nrow(x)
len_k <- len %/% nsplits
if (!len == (len_k * nsplits))
stop("Dividing x by nsplits does not result in an integer.")
lapply(1:nsplits, function(k) {
if (bycol) x[, (k-1) * len_k + 1:len_k, drop = FALSE] else
x[(k-1) * len_k + 1:len_k, , drop = FALSE]})
}
# Convert a standardised quadrature node to an unstandardised value based on
# the specified integral limits
#
# @param x An unstandardised quadrature node
# @param a The lower limit(s) of the integral, possibly a vector
# @param b The upper limit(s) of the integral, possibly a vector
unstandardise_qpts <- function(x, a, b) {
if (!identical(length(x), 1L) || !is.numeric(x))
stop("'x' should be a single numeric value.", call. = FALSE)
if (!all(is.numeric(a), is.numeric(b)))
stop("'a' and 'b' should be numeric.", call. = FALSE)
if (!length(a) %in% c(1L, length(b)))
stop("'a' and 'b' should be vectors of length 1, or, be the same length.", call. = FALSE)
if (any((b - a) < 0))
stop("The upper limits for the integral ('b' values) should be greater than ",
"the corresponding lower limits for the integral ('a' values).", call. = FALSE)
((b - a) / 2) * x + ((b + a) / 2)
}
# Convert a standardised quadrature weight to an unstandardised value based on
# the specified integral limits
#
# @param x An unstandardised quadrature weight
# @param a The lower limit(s) of the integral, possibly a vector
# @param b The upper limit(s) of the integral, possibly a vector
unstandardise_qwts <- function(x, a, b) {
if (!identical(length(x), 1L) || !is.numeric(x))
stop("'x' should be a single numeric value.", call. = FALSE)
if (!all(is.numeric(a), is.numeric(b)))
stop("'a' and 'b' should be numeric.", call. = FALSE)
if (!length(a) %in% c(1L, length(b)))
stop("'a' and 'b' should be vectors of length 1, or, be the same length.", call. = FALSE)
if (any((b - a) < 0))
stop("The upper limits for the integral ('b' values) should be greater than ",
"the corresponding lower limits for the integral ('a' values).", call. = FALSE)
((b - a) / 2) * x
}
# Test if object is stanmvreg class
#
# @param x An object to be tested.
is.stanmvreg <- function(x) {
inherits(x, "stanmvreg")
}
# Test if object is stanjm class
#
# @param x An object to be tested.
is.stanjm <- function(x) {
inherits(x, "stanjm")
}
# Test if object is a joint longitudinal and survival model
#
# @param x An object to be tested.
is.jm <- function(x) {
isTRUE(x$stan_function == "stan_jm")
}
# Test if object contains a multivariate GLM
#
# @param x An object to be tested.
is.mvmer <- function(x) {
isTRUE(x$stan_function %in% c("stan_mvmer", "stan_jm"))
}
# Test if object contains a survival model
#
# @param x An object to be tested.
is.surv <- function(x) {
isTRUE(x$stan_function %in% c("stan_jm"))
}
# Throw error if object isn't a stanmvreg object
#
# @param x The object to test.
validate_stanmvreg_object <- function(x, call. = FALSE) {
if (!is.stanmvreg(x))
stop("Object is not a stanmvreg object.", call. = call.)
}
# Throw error if object isn't a stanjm object
#
# @param x The object to test.
validate_stanjm_object <- function(x, call. = FALSE) {
if (!is.stanjm(x))
stop("Object is not a stanjm object.", call. = call.)
}
# Throw error if parameter isn't a positive scalar
#
# @param x The object to test.
validate_positive_scalar <- function(x, not_greater_than = NULL) {
nm <- deparse(substitute(x))
if (is.null(x))
stop(nm, " cannot be NULL", call. = FALSE)
if (!is.numeric(x))
stop(nm, " should be numeric", call. = FALSE)
if (any(x <= 0))
stop(nm, " should be postive", call. = FALSE)
if (!is.null(not_greater_than)) {
if (!is.numeric(not_greater_than) || (not_greater_than <= 0))
stop("'not_greater_than' should be numeric and postive")
if (!all(x <= not_greater_than))
stop(nm, " should less than or equal to ", not_greater_than, call. = FALSE)
}
}
# Return a list with the median and prob% CrI bounds for each column of a
# matrix or 2D array
#
# @param x A matrix or 2D array
# @param prob Value between 0 and 1 indicating the desired width of the CrI
median_and_bounds <- function(x, prob, na.rm = FALSE) {
if (!any(is.matrix(x), is.array(x)))
stop("x should be a matrix or 2D array.")
med <- apply(x, 2, median, na.rm = na.rm)
lb <- apply(x, 2, quantile, (1 - prob)/2, na.rm = na.rm)
ub <- apply(x, 2, quantile, (1 + prob)/2, na.rm = na.rm)
nlist(med, lb, ub)
}
# Return the stub for variable names from one submodel of a stan_jm model
#
# @param m An integer specifying the number of the longitudinal submodel or
# a character string specifying the submodel (e.g. "Long1", "Event", etc)
# @param stub A character string to prefix to m, if m is supplied as an integer
get_m_stub <- function(m, stub = "Long") {
if (is.null(m)) {
return(NULL)
} else if (is.numeric(m)) {
return(paste0(stub, m, "|"))
} else if (is.character(m)) {
return(paste0(m, "|"))
}
}
# Return the appropriate stub for variable names
#
# @param object A stanmvreg object
get_stub <- function(object) {
if (is.jm(object)) "Long" else if (is.mvmer(object)) "y" else NULL
}
# Separates a names object into separate parts based on the longitudinal,
# event, or association parameters.
#
# @param x Character vector (often rownames(fit$stan_summary))
# @param M An integer specifying the number of longitudinal submodels.
# @param stub The character string used at the start of the names of variables
# in the longitudinal/GLM submodels
# @param ... Arguments passed to grep
# @return A list with x separated out into those names corresponding
# to parameters from the M longitudinal submodels, the event submodel
# or association parameters.
collect_nms <- function(x, M, stub = "Long", ...) {
ppd <- grep(paste0("^", stub, ".{1}\\|mean_PPD"), x, ...)
y <- lapply(1:M, function(m) grep(mod2rx(m, stub = stub), x, ...))
y_extra <- lapply(1:M, function(m)
c(grep(paste0("^", stub, m, "\\|sigma"), x, ...),
grep(paste0("^", stub, m, "\\|shape"), x, ...),
grep(paste0("^", stub, m, "\\|lambda"), x, ...),
grep(paste0("^", stub, m, "\\|reciprocal_dispersion"), x, ...)))
y <- lapply(1:M, function(m) setdiff(y[[m]], c(y_extra[[m]], ppd[m])))
e <- grep(mod2rx("^Event"), x, ...)
e_extra <- c(grep("^Event\\|weibull-shape|^Event\\|b-splines-coef|^Event\\|piecewise-coef", x, ...))
e <- setdiff(e, e_extra)
a <- grep(mod2rx("^Assoc"), x, ...)
b <- b_names(x, ...)
y_b <- lapply(1:M, function(m) b_names_M(x, m, stub = stub, ...))
alpha <- grep("^.{5}\\|\\(Intercept\\)", x, ...)
alpha <- c(alpha, grep(pattern=paste0("^", stub, ".{1}\\|\\(Intercept\\)"), x=x, ...))
beta <- setdiff(c(unlist(y), e, a), alpha)
nlist(y, y_extra, y_b, e, e_extra, a, b, alpha, beta, ppd)
}
# Grep for "b" parameters (ranef), can optionally be specified
# for a specific longitudinal submodel
#
# @param x Character vector (often rownames(fit$stan_summary))
# @param submodel Optional integer specifying which long submodel
# @param ... Passed to grep
b_names_M <- function(x, submodel = NULL, stub = "Long", ...) {
if (is.null(submodel)) {
grep("^b\\[", x, ...)
} else {
grep(paste0("^b\\[", stub, submodel, "\\|"), x, ...)
}
}
# Grep for regression coefs (fixef), can optionally be specified
# for a specific submodel
#
# @param x Character vector (often rownames(fit$stan_summary))
# @param submodel Character vector specifying which submodels
# to obtain the coef names for. Can be "Long", "Event", "Assoc", or
# an integer specifying a specific longitudinal submodel. Specifying
# NULL selects all submodels.
# @param ... Passed to grep
beta_names <- function(x, submodel = NULL, ...) {
if (is.null(submodel)) {
rxlist <- c(mod2rx("^Long"), mod2rx("^Event"), mod2rx("^Assoc"))
} else {
rxlist <- c()
if ("Long" %in% submodel) rxlist <- c(rxlist, mod2rx("^Long"))
if ("Event" %in% submodel) rxlist <- c(rxlist, mod2rx("^Event"))
if ("Assoc" %in% submodel) rxlist <- c(rxlist, mod2rx("^Assoc"))
miss <- setdiff(submodel, c("Long", "Event", "Assoc"))
if (length(miss)) rxlist <- c(rxlist, sapply(miss, mod2rx))
}
unlist(lapply(rxlist, function(y) grep(y, x, ...)))
}
# Converts "Long", "Event" or "Assoc" to the regular expression
# used at the start of variable names for the fitted joint model
#
# @param x The submodel for which the regular expression should be
# obtained. Can be "Long", "Event", "Assoc", or an integer specifying
# a specific longitudinal submodel.
mod2rx <- function(x, stub = "Long") {
if (x == "^Long") {
c("^Long[1-9]\\|")
} else if (x == "^Event") {
c("^Event\\|")
} else if (x == "^Assoc") {
c("^Assoc\\|")
} else if (x == "Long") {
c("Long[1-9]\\|")
} else if (x == "Event") {
c("Event\\|")
} else if (x == "Assoc") {
c("Assoc\\|")
} else if (x == "^y") {
c("^y[1-9]\\|")
} else if (x == "y") {
c("y[1-9]\\|")
} else {
paste0("^", stub, x, "\\|")
}
}
# Return the number of longitudinal submodels
#
# @param object A stanmvreg object
get_M <- function(object) {
validate_stanmvreg_object(object)
return(object$n_markers)
}
# Supplies names for the output list returned by most stanmvreg methods
#
# @param object The list object to which the names are to be applied
# @param M The number of longitudinal/GLM submodels. If NULL then the number of
# longitudinal/GLM submodels is assumed to be equal to the length of object.
# @param stub The character string to use at the start of the names for
# list items related to the longitudinal/GLM submodels
list_nms <- function(object, M = NULL, stub = "Long") {
ok_type <- is.null(object) || is.list(object) || is.vector(object)
if (!ok_type)
stop("'object' argument should be a list or vector.")
if (is.null(object))
return(object)
if (is.null(M))
M <- length(object)
nms <- paste0(stub, 1:M)
if (length(object) > M)
nms <- c(nms, "Event")
names(object) <- nms
object
}
# Removes the submodel identifying text (e.g. "Long1|", "Event|", etc
# from variable names
#
# @param x Character vector (often rownames(fit$stan_summary)) from which
# the stub should be removed
rm_stub <- function(x) {
x <- gsub(mod2rx("^y"), "", x)
x <- gsub(mod2rx("^Long"), "", x)
x <- gsub(mod2rx("^Event"), "", x)
}
# Removes a specified character string from the names of an
# object (for example, a matched call)
#
# @param x The matched call
# @param string The character string to be removed
strip_nms <- function(x, string) {
names(x) <- gsub(string, "", names(x))
x
}
# Check argument contains one of the allowed options
check_submodelopt2 <- function(x) {
if (!x %in% c("long", "event"))
stop("submodel option must be 'long' or 'event'")
}
check_submodelopt3 <- function(x) {
if (!x %in% c("long", "event", "both"))
stop("submodel option must be 'long', 'event' or 'both'")
}
# Error message when the argument contains an object of the incorrect type
STOP_arg <- function(arg_name, type) {
stop(paste0("'", arg_name, "' should be a ", paste0(type, collapse = " or "),
" object or a list of those objects."), call. = FALSE)
}
# Return error msg if both elements of the object are TRUE
STOP_combination_not_allowed <- function(object, x, y) {
if (object[[x]] && object[[y]])
stop("In ", deparse(substitute(object)), ", '", x, "' and '", y,
"' cannot be specified together", call. = FALSE)
}
# Error message when not specifying an argument required for stanmvreg objects
#
# @param arg The argument
STOP_arg_required_for_stanmvreg <- function(arg) {
nm <- deparse(substitute(arg))
msg <- paste0("Argument '", nm, "' required for stanmvreg objects.")
stop2(msg)
}
# Error message when a function is not yet implemented for stanmvreg objects
#
# @param what A character string naming the function not yet implemented
STOP_if_stanmvreg <- function(what) {
msg <- "not yet implemented for stanmvreg objects."
if (!missing(what))
msg <- paste(what, msg)
stop2(msg)
}
# Error message when a function is not yet implemented for stan_mvmer models
#
# @param what An optional message to prepend to the default message.
STOP_stan_mvmer <- function(what) {
msg <- "is not yet implemented for models fit using stan_mvmer."
if (!missing(what))
msg <- paste(what, msg)
stop2(msg)
}
# Consistent error message to use when something that is only available for
# models fit using stan_jm
#
# @param what An optional message to prepend to the default message.
STOP_jm_only <- function(what) {
msg <- "can only be used with stan_jm models."
if (!missing(what))
msg <- paste(what, msg)
stop2(msg)
}
# Consistent error message when binomial models with greater than
# one trial are not allowed
#
STOP_binomial <- function() {
stop2("Binomial models with number of trials greater than one ",
"are not allowed (i.e. only bernoulli models are allowed).")
}
# Error message when a required variable is missing from the data frame
#
# @param var The name of the variable that could not be found
STOP_no_var <- function(var) {
stop2("Variable '", var, "' cannot be found in the data frame.")
}
# Error message for dynamic predictions
#
# @param what A reason why the dynamic predictions are not allowed
STOP_dynpred <- function(what) {
stop2(paste("Dynamic predictions are not yet implemented for", what))
}
# Check if individuals in ids argument were also used in model estimation
#
# @param object A stanmvreg object
# @param ids A vector of ids appearing in the pp data
# @param m Integer specifying which submodel to get the estimation IDs from
# @return A logical. TRUE indicates their are new ids in the prediction data,
# while FALSE indicates all ids in the prediction data were used in fitting
# the model. This return is used to determine whether to draw new b pars.
check_pp_ids <- function(object, ids, m = 1) {
ids2 <- unique(model.frame(object, m = m)[[object$id_var]])
if (any(ids %in% ids2))
warning("Some of the IDs in the 'newdata' correspond to individuals in the ",
"estimation dataset. Please be sure you want to obtain subject-",
"specific predictions using the estimated random effects for those ",
"individuals. If you instead meant to marginalise over the distribution ",
"of the random effects (for posterior_predict or posterior_traj), or ",
"to draw new random effects conditional on outcome data provided in ",
"the 'newdata' arguments (for posterior_survfit), then please make ",
"sure the ID values do not correspond to individuals in the ",
"estimation dataset.", immediate. = TRUE)
if (!all(ids %in% ids2)) TRUE else FALSE
}
# Validate newdataLong and newdataEvent arguments
#
# @param object A stanmvreg object
# @param newdataLong A data frame, or a list of data frames
# @param newdataEvent A data frame
# @param duplicate_ok A logical. If FALSE then only one row per individual is
# allowed in the newdataEvent data frame
# @param response A logical specifying whether the longitudinal response
# variable must be included in the new data frame
# @return A list of validated data frames
validate_newdatas <- function(object, newdataLong = NULL, newdataEvent = NULL,
duplicate_ok = FALSE, response = TRUE) {
validate_stanmvreg_object(object)
id_var <- object$id_var
newdatas <- list()
if (!is.null(newdataLong)) {
if (!is(newdataLong, "list"))
newdataLong <- rep(list(newdataLong), get_M(object))
dfcheck <- sapply(newdataLong, is.data.frame)
if (!all(dfcheck))
stop("'newdataLong' must be a data frame or list of data frames.", call. = FALSE)
nacheck <- sapply(seq_along(newdataLong), function(m) {
if (response) { # newdataLong needs the reponse variable
fmL <- formula(object, m = m)
} else { # newdataLong only needs the covariates
fmL <- formula(object, m = m)[c(1,3)]
}
all(!is.na(get_all_vars(fmL, newdataLong[[m]])))
})
if (!all(nacheck))
stop("'newdataLong' cannot contain NAs.", call. = FALSE)
newdatas <- c(newdatas, newdataLong)
}
if (!is.null(newdataEvent)) {
if (!is.data.frame(newdataEvent))
stop("'newdataEvent' must be a data frame.", call. = FALSE)
if (response) { # newdataEvent needs the reponse variable
fmE <- formula(object, m = "Event")
} else { # newdataEvent only needs the covariates
fmE <- formula(object, m = "Event")[c(1,3)]
}
dat <- get_all_vars(fmE, newdataEvent)
dat[[id_var]] <- newdataEvent[[id_var]] # include ID variable in event data
if (any(is.na(dat)))
stop("'newdataEvent' cannot contain NAs.", call. = FALSE)
if (!duplicate_ok && any(duplicated(newdataEvent[[id_var]])))
stop("'newdataEvent' should only contain one row per individual, since ",
"time varying covariates are not allowed in the prediction data.")
newdatas <- c(newdatas, list(Event = newdataEvent))
}
if (length(newdatas)) {
idvar_check <- sapply(newdatas, function(x) id_var %in% colnames(x))
if (!all(idvar_check))
STOP_no_var(id_var)
ids <- lapply(newdatas, function(x) unique(x[[id_var]]))
sorted_ids <- lapply(ids, sort)
if (!length(unique(sorted_ids)) == 1L)
stop("The same subject ids should appear in each new data frame.")
if (!length(unique(ids)) == 1L)
stop("The subject ids should be ordered the same in each new data frame.")
return(newdatas)
} else return(NULL)
}
# Return data frames only including the specified subset of individuals
#
# @param object A stanmvreg object
# @param data A data frame, or a list of data frames
# @param ids A vector of ids indicating which individuals to keep
# @return A data frame, or a list of data frames, depending on the input
subset_ids <- function(object, data, ids) {
if (is.null(data))
return(NULL)
validate_stanmvreg_object(object)
id_var <- object$id_var
is_list <- is(data, "list")
if (!is_list) data <- list(data)
is_df <- sapply(data, is.data.frame)
if (!all(is_df)) stop("'data' should be a data frame, or list of data frames.")
data <- lapply(data, function(x) {
if (!id_var %in% colnames(x)) STOP_no_var(id_var)
sel <- which(!ids %in% x[[id_var]])
if (length(sel))
stop("The following 'ids' do not appear in the data: ",
paste(ids[[sel]], collapse = ", "))
x[x[[id_var]] %in% ids, , drop = FALSE]
})
if (is_list) return(data) else return(data[[1]])
}
# Return a data.table with a key set using the appropriate id/time/grp variables
#
# @param data A data frame.
# @param id_var The name of the ID variable.
# @param grp_var The name of the variable identifying groups clustered within
# individuals.
# @param time_var The name of the time variable.
# @return A data.table (which will be used in a rolling merge against the
# event times and/or quadrature times).
prepare_data_table <- function(data, id_var, time_var, grp_var = NULL) {
if (!requireNamespace("data.table"))
stop("the 'data.table' package must be installed to use this function")
if (!is.data.frame(data))
stop("'data' should be a data frame.")
# check required vars are in the data
if (!id_var %in% colnames(data))
STOP_no_var(id_var)
if (!time_var %in% colnames(data))
STOP_no_var(time_var)
if (!is.null(grp_var) && (!grp_var %in% colnames(data)))
STOP_no_var(grp_var)
# define and set the key for the data.table
key_vars <- if (!is.null(grp_var))
c(id_var, grp_var, time_var) else c(id_var, time_var)
dt <- data.table::data.table(data, key = key_vars)
dt[[time_var]] <- as.numeric(dt[[time_var]]) # ensures no rounding on merge
dt[[id_var]] <- factor(dt[[id_var]]) # ensures matching of ids
if (!is.null(grp_var))
dt[[grp_var]] <- factor(dt[[grp_var]]) # ensures matching of grps
dt
}
# Carry out a rolling merge
#
# @param data A data.table with a set key corresponding to ids, times (and
# possibly also grps).
# @param ids A vector of patient ids to merge against.
# @param times A vector of times to (rolling) merge against.
# @param grps An optional vector of groups clustered within patients to
# merge against. Only relevant when there is clustering within patient ids.
# @return A data.table formed by a merge of ids, (grps), times, and the closest
# preceding (in terms of times) rows in data.
rolling_merge <- function(data, ids, times, grps = NULL) {
if (!requireNamespace("data.table"))
stop("the 'data.table' package must be installed to use this function")
# check data.table is keyed
key_length <- length(data.table::key(data))
val_length <- if (is.null(grps)) 2L else 3L
if (key_length == 0L)
stop2("Bug found: data.table should have a key.")
if (!key_length == val_length)
stop2("Bug found: data.table key is not the same length as supplied keylist.")
# ensure data types are same as returned by the prepare_data_table function
ids <- factor(ids) # ensures matching of ids
times <- as.numeric(times) # ensures no rounding on merge
# carry out the rolling merge against the specified times
if (is.null(grps)) {
tmp <- data.table::data.table(ids, times)
val <- data[tmp, roll = TRUE, rollends = c(TRUE, TRUE)]
} else {
grps <- factor(grps)
tmp <- data.table::data.table(ids, grps, times)
val <- data[tmp, roll = TRUE, rollends = c(TRUE, TRUE)]
}
val
}
# Return an array or list with the time sequence used for posterior predictions
#
# @param increments An integer with the number of increments (time points) at
# which to predict the outcome for each individual
# @param t0,t1 Numeric vectors giving the start and end times across which to
# generate prediction times
# @param simplify Logical specifying whether to return each increment as a
# column of an array (TRUE) or as an element of a list (FALSE)
get_time_seq <- function(increments, t0, t1, simplify = TRUE) {
val <- sapply(0:(increments - 1), function(x, t0, t1) {
t0 + (t1 - t0) * (x / (increments - 1))
}, t0 = t0, t1 = t1, simplify = simplify)
if (simplify && is.vector(val)) {
# need to transform if there is only one individual
val <- t(val)
rownames(val) <- if (!is.null(names(t0))) names(t0) else
if (!is.null(names(t1))) names(t1) else NULL
}
return(val)
}
# Extract parameters from stanmat and return as a list
#
# @param object A stanmvreg object
# @param stanmat A matrix of posterior draws, may be provided if the desired
# stanmat is only a subset of the draws from as.matrix(object$stanfit)
# @return A named list
extract_pars <- function(object, stanmat = NULL, means = FALSE) {
validate_stanmvreg_object(object)
M <- get_M(object)
if (is.null(stanmat))
stanmat <- as.matrix(object$stanfit)
if (means)
stanmat <- t(colMeans(stanmat)) # return posterior means
nms <- collect_nms(colnames(stanmat), M, stub = get_stub(object))
beta <- lapply(1:M, function(m) stanmat[, nms$y[[m]], drop = FALSE])
ebeta <- stanmat[, nms$e, drop = FALSE]
abeta <- stanmat[, nms$a, drop = FALSE]
bhcoef <- stanmat[, nms$e_extra, drop = FALSE]
b <- lapply(1:M, function(m) stanmat[, nms$y_b[[m]], drop = FALSE])
nlist(beta, ebeta, abeta, bhcoef, b, stanmat)
}
# Promote a character variable to a factor
#
# @param x The variable to potentially promote
promote_to_factor <- function(x) {
if (is.character(x)) as.factor(x) else x
}
# Draw from a multivariate normal distribution
# @param mu A mean vector
# @param Sigma A variance-covariance matrix
# @param df A degrees of freedom
rmt <- function(mu, Sigma, df) {
y <- c(t(chol(Sigma)) %*% rnorm(length(mu)))
u <- rchisq(1, df = df)
return(mu + y / sqrt(u / df))
}
# Evaluate the multivariate t log-density
# @param x A realization
# @param mu A mean vector
# @param Sigma A variance-covariance matrix
# @param df A degrees of freedom
dmt <- function(x, mu, Sigma, df) {
x_mu <- x - mu
p <- length(x)
lgamma(0.5 * (df + p)) - lgamma(0.5 * df) -
0.5 * p * log(df) - 0.5 * p * log(pi) -
0.5 * c(determinant(Sigma, logarithm = TRUE)$modulus) -
0.5 * (df + p) * log1p((x_mu %*% chol2inv(chol(Sigma)) %*% x_mu)[1] / df)
}
# Count the number of unique values
#
# @param x A vector or list
n_distinct <- function(x) {
length(unique(x))
}
# Transpose function that can handle NULL objects
#
# @param x A matrix, a vector, or otherwise (e.g. NULL)
transpose <- function(x) {
if (is.matrix(x) || is.vector(x)) {
t(x)
} else {
x
}
}
# Translate group/factor IDs into integer values
#
# @param x A vector of group/factor IDs
groups <- function(x) {
if (!is.null(x)) {
as.integer(as.factor(x))
} else {
x
}
}
# Drop named attributes listed in ... from the object x
#
# @param x Any object with attributes
# @param ... The named attributes to drop
drop_attributes <- function(x, ...) {
dots <- list(...)
if (length(dots)) {
for (i in dots) {
attr(x, i) <- NULL
}
}
x
}
# Check if x and any objects in ... were all NULL or not
#
# @param x The first object to use in the comparison
# @param ... Any additional objects to include in the comparison
# @param error If TRUE then return an error if all objects aren't
# equal with regard to the 'is.null' test.
# @return If error = TRUE, then an error if all objects aren't
# equal with regard to the 'is.null' test. Otherwise, a logical
# specifying whether all objects were equal with regard to the
# 'is.null' test.
supplied_together <- function(x, ..., error = FALSE) {
dots <- list(...)
for (i in dots) {
if (!identical(is.null(x), is.null(i))) {
if (error) {
nm_x <- deparse(substitute(x))
nm_i <- deparse(substitute(i))
stop2(nm_x, " and ", nm_i, " must be supplied together.")
} else {
return(FALSE) # not supplied together, ie. one NULL and one not NULL
}
}
}
return(TRUE) # supplied together, ie. all NULL or all not NULL
}
# Check variables specified in ... are in the data frame
#
# @param data A data frame
# @param ... The names of the variables
check_vars_are_included <- function(data, ...) {
nms <- names(data)
vars <- list(...)
for (i in vars) {
if (!i %in% nms) {
arg_nm <- deparse(substitute(data))
stop2("Variable '", i, "' is not present in ", arg_nm, ".")
}
}
data
}
# Check whether a vector/matrix/array contains an "(Intercept)"
check_for_intercept <- function(x, logical = FALSE) {
nms <- if (is.matrix(x)) colnames(x) else names(x)
sel <- which("(Intercept)" %in% nms)
if (logical) as.logical(length(sel)) else sel
}
# Drop intercept from a vector/matrix/array of named coefficients
drop_intercept <- function(x) {
sel <- check_for_intercept(x)
if (length(sel) && is.matrix(x)) {
x[, -sel, drop = FALSE]
} else if (length(sel)) {
x[-sel]
} else {
x
}
}
# Return intercept from a vector/matrix/array of named coefficients
return_intercept <- function(x) {
sel <- which("(Intercept)" %in% names(x))
if (length(sel)) x[sel] else NULL
}
# Standardise a coefficient
standardise_coef <- function(x, location = 0, scale = 1)
(x - location) / scale
# Return a one-dimensional array or an empty numeric
array_else_double <- function(x)
if (!length(x)) double(0) else as.array(unlist(x))
# Return a matrix of uniform random variables or an empty matrix
matrix_of_uniforms <- function(nrow = 0, ncol = 0) {
if (nrow == 0 || ncol == 0) {
matrix(0,0,0)
} else {
matrix(runif(nrow * ncol), nrow, ncol)
}
}
# If x is NULL then return an empty object of the specified 'type'
#
# @param x An object to test whether it is null.
# @param type The type of empty object to return if x is null.
convert_null <- function(x, type = c("double", "integer", "matrix",
"arraydouble", "arrayinteger")) {
if (!is.null(x)) {
return(x)
} else if (type == "double") {
return(double(0))
} else if (type == "integer") {
return(integer(0))
} else if (type == "matrix") {
return(matrix(0,0,0))
} else if (type == "arraydouble") {
return(as.array(double(0)))
} else if (type == "arrayinteger") {
return(as.array(integer(0)))
} else {
stop("Input type not valid.")
}
}
# Expand/pad a matrix to the specified number of cols/rows
#
# @param x A matrix or 2D array
# @param cols,rows Integer specifying the desired number
# of columns/rows
# @param value The value to use for the padded cells
# @return A matrix
pad_matrix <- function(x, cols = NULL, rows = NULL,
value = 0L) {
nc <- ncol(x)
nr <- nrow(x)
if (!is.null(cols) && nc < cols) {
pad_mat <- matrix(value, nr, cols - nc)
x <- cbind(x, pad_mat)
nc <- ncol(x) # update nc to reflect new num cols
}
if (!is.null(rows) && nr < rows) {
pad_mat <- matrix(value, rows - nr, nc)
x <- rbind(x, pad_mat)
}
x
}
#------- helpers from brms package
stop2 <- function(...) {
stop(..., call. = FALSE)
}
warning2 <- function(...) {
warning(..., call. = FALSE)
}
SW <- function(expr) {
# just a short form for suppressWarnings
base::suppressWarnings(expr)
}
is_null <- function(x) {
# check if an object is NULL
is.null(x) || ifelse(is.vector(x), all(sapply(x, is.null)), FALSE)
}
rm_null <- function(x, recursive = TRUE) {
# recursively removes NULL entries from an object
x <- Filter(Negate(is_null), x)
if (recursive) {
x <- lapply(x, function(x) if (is.list(x)) rm_null(x) else x)
}
x
}
isFALSE <- function(x) {
identical(FALSE, x)
}
is_equal <- function(x, y, ...) {
isTRUE(all.equal(x, y, ...))
}
is_like_factor <- function(x) {
# check if x behaves like a factor in design matrices
is.factor(x) || is.character(x) || is.logical(x)
}
# @param x numeric vector
log_sum_exp <- function(x) {
max_x <- max(x)
max_x + log(sum(exp(x - max_x)))
}
|