This vignette gives a minimal example of how to use
poissonsuperlearner to fit piecewise-constant hazard models
and combine them in a Poisson super learner. The package works by
expanding subject-level time-to-event data to a long Poisson format on a
grid of time intervals, fitting one or more base learners to the
corresponding cause-specific hazards, and optionally combining them
through a meta-learner.
The same workflow can be used both for a single fitted learner via
fit_learner() and for an ensemble via
Superlearner().
We first simulate a small synthetic data set with
simulateStenoT1(). The simulator is inspired by the Steno
Type-1 risk engine and generates correlated baseline covariates such as
age, diabetes duration, LDL, systolic blood pressure, HbA1c,
albuminuria, eGFR, smoking, and physical activity. Event times are then
generated from Weibull proportional hazards models. In scenario
"alpha", the event mechanism is linear in the covariates,
and with competing_risks = TRUE the data contain two
causes: cardiovascular disease (CVD) and death without prior CVD. In
scenario "beta", the CVD hazard instead includes additional
nonlinear hinge-squared effects of age and LDL.
Here we use the "alpha" scenario with competing risks to
illustrate the cause-specific output of the package on a subset of the
covariates.
set.seed(42)
d <- simulateStenoT1(
n = 150,
scenario = "alpha",
competing_risks = TRUE
)
head(d[, .(id, time, event, age, diabetes_duration, value_LDL, sex)])
#> id time event age diabetes_duration value_LDL sex
#> <int> <num> <num> <num> <num> <num> <fctr>
#> 1: 1 0.3390123 1 48.56530 22.40288 3.7434636 0
#> 2: 2 0.3576302 1 57.73618 25.73266 2.2979531 1
#> 3: 3 0.4172349 1 39.41783 16.74741 1.1053140 0
#> 4: 4 0.5742121 1 47.49727 23.21574 2.5886356 0
#> 5: 5 0.9066543 1 49.60508 23.06710 0.1462869 0
#> 6: 6 1.0303493 1 43.25154 17.76698 1.2642894 0The observed follow-up time is stored in time and the
event indicator in event. With competing risks,
event = 0 denotes censoring, event = 1
corresponds to CVD, and event = 2 to death without prior
CVD.
Each learner models the piecewise-constant hazard on a grid of time
intervals. In the examples below, the grid is controlled by
number_of_nodes = 5, which creates a quantile-based time
grid used internally for the long Poisson representation.
We define two simple learners based on glmnet: one
unpenalized Poisson model and one lasso-type learner.
lglmnet0 <- Learner_glmnet(
covariates = c("sex", "diabetes_duration"),
cross_validation = FALSE,
lambda = 0,
intercept = TRUE
)
lglmnet1 <- Learner_glmnet(
covariates = c("value_Smoking", "value_LDL"),
cross_validation = TRUE,
alpha = 1,
intercept = TRUE
)
learners_list <- list(
glm = lglmnet0,
lasso = lglmnet1
)The package currently provides three learner classes:
Learner_glmnet(), Learner_gam(), and
Learner_hal(). The code below is shown for illustration
only and is therefore not run. It highlights how learners are defined by
initializing Reference Class objects and then passing them to
fit_learner() or Superlearner().
l_glmnet <- Learner_glmnet(
covariates = c("sex", "diabetes_duration", "value_LDL"),
add_nodes = TRUE,
cross_validation = TRUE,
alpha = 1
)
l_gam <- Learner_gam(
covariates = c("s(age)", "s(value_LDL)", "sex"),
add_nodes = TRUE,
method = "fREML",
discrete = TRUE
)
l_hal <- Learner_hal(
covariates = c("age", "diabetes_duration", "value_LDL"),
add_nodes = TRUE,
cross_validation = TRUE,
num_knots = c(10L, 5L),
max_degree = 2L
)Some learner arguments are specific to the piecewise-constant hazard
workflow implemented in poissonsuperlearner. In particular,
covariates defines the terms used in the long-format hazard
model, and add_nodes = TRUE adds the interval-specific node
effects that represent the baseline hazard across the time grid. For the
HAL learner, num_knots, max_degree, and
maxit_prefit also control the package-specific basis
construction and screening step used before the final penalized fit.
A useful feature of the package is that, except for HAL, the learners
mainly wrap existing fitting routines. Learner_glmnet()
forwards additional arguments in ... to
glmnet::glmnet() or glmnet::cv.glmnet(), and
Learner_gam() forwards them to mgcv::bam().
This means that most tuning can be done with the familiar arguments from
the underlying packages rather than through a separate package-specific
interface.
For GAMs, smooth terms are specified directly in the
covariates vector using standard mgcv syntax.
For example, if a spline effect of age is desired, one simply writes
"s(age)"; tensor products or other mgcv terms
can be passed in the same way.
The HAL learner is more specialized. It includes several parameters
that are specific to the piecewise-constant hazard implementation, such
as the number of basis cutpoints per interaction order
(num_knots) and the maximum interaction degree
(max_degree), while still relying on the underlying
glmnet fitting routine for the penalized Poisson regression
step. In other words, HAL combines a package-specific basis expansion
with the familiar glmnet optimization controls available
through ....
Superlearner() fits all learners, obtains
cross-validated predictions for the meta-learning step, and then fits
one cause-specific meta-learner per cause. For comparison,
fit_learner() fits a single learner directly.
Superlearner() and fit_learner() can be
supplied either with an explicit grid of time nodes (nodes)
or with the number of nodes selected from the observed quantiles
(number_of_nodes) used to construct that grid;
nfolds specifies the number of folds used for internal
cross-validation; if an id variable is provided it
identifies which rows belong to the same individual, whereas if
id is omitted each row is treated as a separate
observation; the status variable must be coded as
0 for censoring and positive integers for the competing
event types; and time_event is the event or follow-up time
variable on which the model is built.
sl_model <- Superlearner(
d,
id = "id",
status = "event",
event_time = "time",
learners = learners_list,
number_of_nodes = 5,
nfold = 3
)
l0_model <- fit_learner(
d,
id = "id",
learner = lglmnet0,
status = "event",
event_time = "time",
number_of_nodes = 5
)
l1_model <- fit_learner(
d,
id = "id",
learner = lglmnet1,
status = "event",
event_time = "time",
number_of_nodes = 5
)The fitted objects have print(), summary(),
and coef() methods.
print() is useful for a quick look at the stored fitted
object. For a fitted super learner, you can print the stacked
meta-learner or one of the stored base learners.
print(sl_model, cause = 1)
#>
#> Call: (function (x, y, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), weights = NULL, offset = NULL, alpha = 1, nlambda = 100, lambda.min.ratio = ifelse(nobs < nvars, 0.01, 1e-04), lambda = NULL, standardize = TRUE, intercept = TRUE, thresh = 1e-07, dfmax = nvars + 1, pmax = min(dfmax * 2 + 20, nvars), exclude = NULL, penalty.factor = rep(1, nvars), lower.limits = -Inf, upper.limits = Inf, maxit = 1e+05, type.gaussian = ifelse(nvars < 500, "covariance", "naive"), type.logistic = c("Newton", "modified.Newton"), standardize.response = FALSE, type.multinomial = c("ungrouped", "grouped"), relax = FALSE, trace.it = 0, ...) { this.call = match.call() np = dim(x) if (is.null(np) | (np[2] <= 1)) stop("x should be a matrix with 2 or more columns") nobs = as.integer(np[1]) nvars = as.integer(np[2]) if (any(is.na(x))) stop("x has missing values; consider using makeX() to impute them") if (is.null(weights)) weights = rep(1, nobs) else if (length(weights) != nobs) stop(paste("number of elements in weights (", length(weights), ") not equal to the number of rows of x (", nobs, ")", sep = "")) if (is.function(exclude)) exclude <- check.exclude(exclude(x = x, y = y, weights = weights), nvars) if (length(penalty.factor) != nvars) stop("the length of penalty.factor does not match the number of variables") if (!is.character(family)) { fit = glmnet.path(x, y, weights, lambda, nlambda, lambda.min.ratio, alpha, offset, family, standardize, intercept, thresh = thresh, maxit, penalty.factor, exclude, lower.limits, upper.limits, trace.it = trace.it) fit$call = this.call } else { family = match.arg(family) if (family == "cox" && use.cox.path(x, y)) { fit <- cox.path(x, y, weights, offset, alpha, nlambda, lambda.min.ratio, lambda, standardize, thresh, exclude, penalty.factor, lower.limits, upper.limits, maxit, trace.it, ...) fit$call <- this.call } else { if (alpha > 1) { warning("alpha >1; set to 1") alpha = 1 } if (alpha < 0) { warning("alpha<0; set to 0") alpha = 0 } alpha = as.double(alpha) nlam = as.integer(nlambda) y = drop(y) dimy = dim(y) nrowy = ifelse(is.null(dimy), length(y), dimy[1]) if (nrowy != nobs) stop(paste("number of observations in y (", nrowy, ") not equal to the number of rows of x (", nobs, ")", sep = "")) vnames = colnames(x) if (is.null(vnames)) vnames = paste("V", seq(nvars), sep = "") ne = as.integer(dfmax) nx = as.integer(pmax) if (is.null(exclude)) exclude = integer(0) if (any(penalty.factor == Inf)) { exclude = c(exclude, seq(nvars)[penalty.factor == Inf]) exclude = sort(unique(exclude)) } if (length(exclude) > 0) { jd = match(exclude, seq(nvars), 0) if (!all(jd > 0)) stop("Some excluded variables out of range") penalty.factor[jd] = 1 jd = as.integer(c(length(jd), jd)) } else jd = as.integer(0) vp = as.double(penalty.factor) internal.parms = glmnet.control() if (internal.parms$itrace) trace.it = 1 else { if (trace.it) { glmnet.control(itrace = 1) on.exit(glmnet.control(itrace = 0)) } } if (any(lower.limits > 0)) { stop("Lower limits should be non-positive") } if (any(upper.limits < 0)) { stop("Upper limits should be non-negative") } lower.limits[lower.limits == -Inf] = -internal.parms$big upper.limits[upper.limits == Inf] = internal.parms$big if (length(lower.limits) < nvars) { if (length(lower.limits) == 1) lower.limits = rep(lower.limits, nvars) else stop("Require length 1 or nvars lower.limits") } else lower.limits = lower.limits[seq(nvars)] if (length(upper.limits) < nvars) { if (length(upper.limits) == 1) upper.limits = rep(upper.limits, nvars) else stop("Require length 1 or nvars upper.limits") } else upper.limits = upper.limits[seq(nvars)] cl = rbind(lower.limits, upper.limits) if (any(cl == 0)) { fdev = glmnet.control()$fdev if (fdev != 0) { glmnet.control(fdev = 0) on.exit(glmnet.control(fdev = fdev)) } } storage.mode(cl) = "double" isd = as.integer(standardize) intr = as.integer(intercept) if (!missing(intercept) && family == "cox") warning("Cox model has no intercept") jsd = as.integer(standardize.response) thresh = as.double(thresh) if (is.null(lambda)) { if (lambda.min.ratio >= 1) stop("lambda.min.ratio should be less than 1") flmin = as.double(lambda.min.ratio) ulam = double(1) } else { flmin = as.double(1) if (any(lambda < 0)) stop("lambdas should be non-negative") ulam = as.double(rev(sort(lambda))) nlam = as.integer(length(lambda)) } is.sparse = FALSE if (inherits(x, "sparseMatrix")) { is.sparse = TRUE if (!inherits(x, "dgCMatrix")) x = as(as(as(x, "generalMatrix"), "CsparseMatrix"), "dMatrix") } else if (!inherits(x, "matrix")) { x <- data.matrix(x) } else { x <- x } if (!inherits(x, "sparseMatrix")) { storage.mode(x) <- "double" } if (trace.it) { if (relax) cat("Training Fit\n") pb <- createPB(min = 0, max = nlam, initial = 0, style = 3) } else { pb <- NULL } kopt = switch(match.arg(type.logistic), Newton = 0, modified.Newton = 1) if (family == "multinomial") { type.multinomial = match.arg(type.multinomial) if (type.multinomial == "grouped") kopt = 2 } kopt = as.integer(kopt) fit = switch(family, gaussian = elnet(x, is.sparse, y, weights, offset, type.gaussian, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, pb), poisson = fishnet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, pb), binomial = lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, kopt, family, pb), multinomial = lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, kopt, family, pb), cox = coxnet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, vnames, maxit), mgaussian = mrelnet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, jsd, intr, vnames, maxit, pb)) if (trace.it) { utils::setTxtProgressBar(pb, nlam) close(pb) } if (is.null(lambda)) fit$lambda = fix.lam(fit$lambda) fit$call = this.call fit$nobs = nobs class(fit) = c(class(fit), "glmnet") } } if (relax) relax.glmnet(fit, x = x, y = y, weights = weights, offset = offset, lower.limits = lower.limits, upper.limits = upper.limits, penalty.factor = penalty.factor, check.args = FALSE, ...) else fit })(x = new("dgCMatrix", i = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L, 465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L, 542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L, 550L, 551L, 552L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L, 562L, 563L, 564L, 565L, 566L, 567L, 568L, 569L, 570L, 571L, 572L, 573L, 574L, 575L, 576L, 577L, 578L, 579L, 580L, 581L, 582L, 583L, 584L, 585L, 586L, 587L, 588L, 589L, 590L, 591L, 592L, 593L, 594L, 595L, 596L, 597L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L, 465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L, 532L, 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1.26204845982929, 1.90061226295242, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.95753338447636, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 2.10018312549547, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 2.27238716888237, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 2.44250995253019), lambda = 0, intercept = FALSE, penalise_nodes = TRUE)
#>
#> Df %Dev Lambda
#> 1 2 81.25 0
print(sl_model, cause = 1, model = "glm")
#>
#> Call: (function (x, y, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), weights = NULL, offset = NULL, alpha = 1, nlambda = 100, lambda.min.ratio = ifelse(nobs < nvars, 0.01, 1e-04), lambda = NULL, standardize = TRUE, intercept = TRUE, thresh = 1e-07, dfmax = nvars + 1, pmax = min(dfmax * 2 + 20, nvars), exclude = NULL, penalty.factor = rep(1, nvars), lower.limits = -Inf, upper.limits = Inf, maxit = 1e+05, type.gaussian = ifelse(nvars < 500, "covariance", "naive"), type.logistic = c("Newton", "modified.Newton"), standardize.response = FALSE, type.multinomial = c("ungrouped", "grouped"), relax = FALSE, trace.it = 0, ...) { this.call = match.call() np = dim(x) if (is.null(np) | (np[2] <= 1)) stop("x should be a matrix with 2 or more columns") nobs = as.integer(np[1]) nvars = as.integer(np[2]) if (any(is.na(x))) stop("x has missing values; consider using makeX() to impute them") if (is.null(weights)) weights = rep(1, nobs) else if (length(weights) != nobs) stop(paste("number of elements in weights (", length(weights), ") not equal to the number of rows of x (", nobs, ")", sep = "")) if (is.function(exclude)) exclude <- check.exclude(exclude(x = x, y = y, weights = weights), nvars) if (length(penalty.factor) != nvars) stop("the length of penalty.factor does not match the number of variables") if (!is.character(family)) { fit = glmnet.path(x, y, weights, lambda, nlambda, lambda.min.ratio, alpha, offset, family, standardize, intercept, thresh = thresh, maxit, penalty.factor, exclude, lower.limits, upper.limits, trace.it = trace.it) fit$call = this.call } else { family = match.arg(family) if (family == "cox" && use.cox.path(x, y)) { fit <- cox.path(x, y, weights, offset, alpha, nlambda, lambda.min.ratio, lambda, standardize, thresh, exclude, penalty.factor, lower.limits, upper.limits, maxit, trace.it, ...) fit$call <- this.call } else { if (alpha > 1) { warning("alpha >1; set to 1") alpha = 1 } if (alpha < 0) { warning("alpha<0; set to 0") alpha = 0 } alpha = as.double(alpha) nlam = as.integer(nlambda) y = drop(y) dimy = dim(y) nrowy = ifelse(is.null(dimy), length(y), dimy[1]) if (nrowy != nobs) stop(paste("number of observations in y (", nrowy, ") not equal to the number of rows of x (", nobs, ")", sep = "")) vnames = colnames(x) if (is.null(vnames)) vnames = paste("V", seq(nvars), sep = "") ne = as.integer(dfmax) nx = as.integer(pmax) if (is.null(exclude)) exclude = integer(0) if (any(penalty.factor == Inf)) { exclude = c(exclude, seq(nvars)[penalty.factor == Inf]) exclude = sort(unique(exclude)) } if (length(exclude) > 0) { jd = match(exclude, seq(nvars), 0) if (!all(jd > 0)) stop("Some excluded variables out of range") penalty.factor[jd] = 1 jd = as.integer(c(length(jd), jd)) } else jd = as.integer(0) vp = as.double(penalty.factor) internal.parms = glmnet.control() if (internal.parms$itrace) trace.it = 1 else { if (trace.it) { glmnet.control(itrace = 1) on.exit(glmnet.control(itrace = 0)) } } if (any(lower.limits > 0)) { stop("Lower limits should be non-positive") } if (any(upper.limits < 0)) { stop("Upper limits should be non-negative") } lower.limits[lower.limits == -Inf] = -internal.parms$big upper.limits[upper.limits == Inf] = internal.parms$big if (length(lower.limits) < nvars) { if (length(lower.limits) == 1) lower.limits = rep(lower.limits, nvars) else stop("Require length 1 or nvars lower.limits") } else lower.limits = lower.limits[seq(nvars)] if (length(upper.limits) < nvars) { if (length(upper.limits) == 1) upper.limits = rep(upper.limits, nvars) else stop("Require length 1 or nvars upper.limits") } else upper.limits = upper.limits[seq(nvars)] cl = rbind(lower.limits, upper.limits) if (any(cl == 0)) { fdev = glmnet.control()$fdev if (fdev != 0) { glmnet.control(fdev = 0) on.exit(glmnet.control(fdev = fdev)) } } storage.mode(cl) = "double" isd = as.integer(standardize) intr = as.integer(intercept) if (!missing(intercept) && family == "cox") warning("Cox model has no intercept") jsd = as.integer(standardize.response) thresh = as.double(thresh) if (is.null(lambda)) { if (lambda.min.ratio >= 1) stop("lambda.min.ratio should be less than 1") flmin = as.double(lambda.min.ratio) ulam = double(1) } else { flmin = as.double(1) if (any(lambda < 0)) stop("lambdas should be non-negative") ulam = as.double(rev(sort(lambda))) nlam = as.integer(length(lambda)) } is.sparse = FALSE if (inherits(x, "sparseMatrix")) { is.sparse = TRUE if (!inherits(x, "dgCMatrix")) x = as(as(as(x, "generalMatrix"), "CsparseMatrix"), "dMatrix") } else if (!inherits(x, "matrix")) { x <- data.matrix(x) } else { x <- x } if (!inherits(x, "sparseMatrix")) { storage.mode(x) <- "double" } if (trace.it) { if (relax) cat("Training Fit\n") pb <- createPB(min = 0, max = nlam, initial = 0, style = 3) } else { pb <- NULL } kopt = switch(match.arg(type.logistic), Newton = 0, modified.Newton = 1) if (family == "multinomial") { type.multinomial = match.arg(type.multinomial) if (type.multinomial == "grouped") kopt = 2 } kopt = as.integer(kopt) fit = switch(family, gaussian = elnet(x, is.sparse, y, weights, offset, type.gaussian, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, pb), poisson = fishnet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, pb), binomial = lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, kopt, family, pb), multinomial = lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, kopt, family, pb), cox = coxnet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, vnames, maxit), mgaussian = mrelnet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, jsd, intr, vnames, maxit, pb)) if (trace.it) { utils::setTxtProgressBar(pb, nlam) close(pb) } if (is.null(lambda)) fit$lambda = fix.lam(fit$lambda) fit$call = this.call fit$nobs = nobs class(fit) = c(class(fit), "glmnet") } } if (relax) relax.glmnet(fit, x = x, y = y, weights = weights, offset = offset, lower.limits = lower.limits, upper.limits = upper.limits, penalty.factor = penalty.factor, check.args = FALSE, ...) else fit })(x = new("dgCMatrix", i = c(1L, 2L, 15L, 16L, 23L, 24L, 27L, 28L, 29L, 30L, 33L, 34L, 35L, 36L, 37L, 38L, 62L, 63L, 64L, 74L, 75L, 76L, 83L, 84L, 85L, 89L, 90L, 91L, 101L, 102L, 103L, 110L, 111L, 112L, 113L, 114L, 115L, 131L, 132L, 133L, 140L, 141L, 142L, 149L, 150L, 151L, 152L, 161L, 162L, 163L, 164L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 189L, 190L, 191L, 192L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 261L, 262L, 263L, 264L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L, 302L, 313L, 314L, 315L, 316L, 317L, 323L, 324L, 325L, 326L, 327L, 333L, 334L, 335L, 336L, 337L, 343L, 344L, 345L, 346L, 347L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 393L, 394L, 395L, 396L, 397L, 408L, 409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 460L, 461L, 462L, 463L, 464L, 465L, 472L, 473L, 474L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 498L, 499L, 500L, 501L, 508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L, 542L, 543L, 550L, 551L, 552L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L, 568L, 569L, 570L, 571L, 572L, 573L, 580L, 581L, 582L, 583L, 584L, 585L, 586L, 587L, 588L, 589L, 590L, 591L, 592L, 593L, 594L, 595L, 596L, 597L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L, 465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L, 542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L, 550L, 551L, 552L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L, 562L, 563L, 564L, 565L, 566L, 567L, 568L, 569L, 570L, 571L, 572L, 573L, 574L, 575L, 576L, 577L, 578L, 579L, 580L, 581L, 582L, 583L, 584L, 585L, 586L, 587L, 588L, 589L, 590L, 591L, 592L, 593L, 594L, 595L, 596L, 597L, 0L, 1L, 3L, 5L, 7L, 9L, 11L, 13L, 15L, 17L, 19L, 21L, 23L, 25L, 27L, 29L, 31L, 33L, 35L, 37L, 39L, 41L, 43L, 45L, 47L, 49L, 51L, 53L, 55L, 57L, 59L, 62L, 65L, 68L, 71L, 74L, 77L, 80L, 83L, 86L, 89L, 92L, 95L, 98L, 101L, 104L, 107L, 110L, 113L, 116L, 119L, 122L, 125L, 128L, 131L, 134L, 137L, 140L, 143L, 146L, 149L, 153L, 157L, 161L, 165L, 169L, 173L, 177L, 181L, 185L, 189L, 193L, 197L, 201L, 205L, 209L, 213L, 217L, 221L, 225L, 229L, 233L, 237L, 241L, 245L, 249L, 253L, 257L, 261L, 265L, 269L, 273L, 278L, 283L, 288L, 293L, 298L, 303L, 308L, 313L, 318L, 323L, 328L, 333L, 338L, 343L, 348L, 353L, 358L, 363L, 368L, 373L, 378L, 383L, 388L, 393L, 398L, 403L, 408L, 413L, 418L, 424L, 430L, 436L, 442L, 448L, 454L, 460L, 466L, 472L, 478L, 484L, 490L, 496L, 502L, 508L, 514L, 520L, 526L, 532L, 538L, 544L, 550L, 556L, 562L, 568L, 574L, 580L, 586L, 592L, 2L, 4L, 6L, 8L, 10L, 12L, 14L, 16L, 18L, 20L, 22L, 24L, 26L, 28L, 30L, 32L, 34L, 36L, 38L, 40L, 42L, 44L, 46L, 48L, 50L, 52L, 54L, 56L, 58L, 60L, 63L, 66L, 69L, 72L, 75L, 78L, 81L, 84L, 87L, 90L, 93L, 96L, 99L, 102L, 105L, 108L, 111L, 114L, 117L, 120L, 123L, 126L, 129L, 132L, 135L, 138L, 141L, 144L, 147L, 150L, 154L, 158L, 162L, 166L, 170L, 174L, 178L, 182L, 186L, 190L, 194L, 198L, 202L, 206L, 210L, 214L, 218L, 222L, 226L, 230L, 234L, 238L, 242L, 246L, 250L, 254L, 258L, 262L, 266L, 270L, 274L, 279L, 284L, 289L, 294L, 299L, 304L, 309L, 314L, 319L, 324L, 329L, 334L, 339L, 344L, 349L, 354L, 359L, 364L, 369L, 374L, 379L, 384L, 389L, 394L, 399L, 404L, 409L, 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224L, 228L, 232L, 236L, 240L, 244L, 248L, 252L, 256L, 260L, 264L, 268L, 272L, 276L, 281L, 286L, 291L, 296L, 301L, 306L, 311L, 316L, 321L, 326L, 331L, 336L, 341L, 346L, 351L, 356L, 361L, 366L, 371L, 376L, 381L, 386L, 391L, 396L, 401L, 406L, 411L, 416L, 421L, 427L, 433L, 439L, 445L, 451L, 457L, 463L, 469L, 475L, 481L, 487L, 493L, 499L, 505L, 511L, 517L, 523L, 529L, 535L, 541L, 547L, 553L, 559L, 565L, 571L, 577L, 583L, 589L, 595L, 277L, 282L, 287L, 292L, 297L, 302L, 307L, 312L, 317L, 322L, 327L, 332L, 337L, 342L, 347L, 352L, 357L, 362L, 367L, 372L, 377L, 382L, 387L, 392L, 397L, 402L, 407L, 412L, 417L, 422L, 428L, 434L, 440L, 446L, 452L, 458L, 464L, 470L, 476L, 482L, 488L, 494L, 500L, 506L, 512L, 518L, 524L, 530L, 536L, 542L, 548L, 554L, 560L, 566L, 572L, 578L, 584L, 590L, 596L), p = c(0L, 268L, 866L, 1016L, 1165L, 1285L, 1375L, 1434L), Dim = c(598L, 7L), Dimnames = list( c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", 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0.986638937859699, 0.97534120439782, 1.26204845982929, 0.85910237328832, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 0.891199114751497, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.0777340923896, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.27624304344664, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.30195447252204, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.42050901179438, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.44358739848253, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.44837738103435, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.48043338335665, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.69904441056629, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.90061226295242, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 1.95753338447636, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 2.10018312549547, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 2.27238716888237, -1.08171900457779, 0.705061850648697, 0.986638937859699, 0.97534120439782, 1.26204845982929, 2.44250995253019), lambda = 0, intercept = TRUE)
#>
#> Df %Dev Lambda
#> 1 7 10.8 0
print(l0_model, cause = 1)
#>
#> Call: (function (x, y, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), weights = NULL, offset = NULL, alpha = 1, nlambda = 100, lambda.min.ratio = ifelse(nobs < nvars, 0.01, 1e-04), lambda = NULL, standardize = TRUE, intercept = TRUE, thresh = 1e-07, dfmax = nvars + 1, pmax = min(dfmax * 2 + 20, nvars), exclude = NULL, penalty.factor = rep(1, nvars), lower.limits = -Inf, upper.limits = Inf, maxit = 1e+05, type.gaussian = ifelse(nvars < 500, "covariance", "naive"), type.logistic = c("Newton", "modified.Newton"), standardize.response = FALSE, type.multinomial = c("ungrouped", "grouped"), relax = FALSE, trace.it = 0, ...) { this.call = match.call() np = dim(x) if (is.null(np) | (np[2] <= 1)) stop("x should be a matrix with 2 or more columns") nobs = as.integer(np[1]) nvars = as.integer(np[2]) if (any(is.na(x))) stop("x has missing values; consider using makeX() to impute them") if (is.null(weights)) weights = rep(1, nobs) else if (length(weights) != nobs) stop(paste("number of elements in weights (", length(weights), ") not equal to the number of rows of x (", nobs, ")", sep = "")) if (is.function(exclude)) exclude <- check.exclude(exclude(x = x, y = y, weights = weights), nvars) if (length(penalty.factor) != nvars) stop("the length of penalty.factor does not match the number of variables") if (!is.character(family)) { fit = glmnet.path(x, y, weights, lambda, nlambda, lambda.min.ratio, alpha, offset, family, standardize, intercept, thresh = thresh, maxit, penalty.factor, exclude, lower.limits, upper.limits, trace.it = trace.it) fit$call = this.call } else { family = match.arg(family) if (family == "cox" && use.cox.path(x, y)) { fit <- cox.path(x, y, weights, offset, alpha, nlambda, lambda.min.ratio, lambda, standardize, thresh, exclude, penalty.factor, lower.limits, upper.limits, maxit, trace.it, ...) fit$call <- this.call } else { if (alpha > 1) { warning("alpha >1; set to 1") alpha = 1 } if (alpha < 0) { warning("alpha<0; set to 0") alpha = 0 } alpha = as.double(alpha) nlam = as.integer(nlambda) y = drop(y) dimy = dim(y) nrowy = ifelse(is.null(dimy), length(y), dimy[1]) if (nrowy != nobs) stop(paste("number of observations in y (", nrowy, ") not equal to the number of rows of x (", nobs, ")", sep = "")) vnames = colnames(x) if (is.null(vnames)) vnames = paste("V", seq(nvars), sep = "") ne = as.integer(dfmax) nx = as.integer(pmax) if (is.null(exclude)) exclude = integer(0) if (any(penalty.factor == Inf)) { exclude = c(exclude, seq(nvars)[penalty.factor == Inf]) exclude = sort(unique(exclude)) } if (length(exclude) > 0) { jd = match(exclude, seq(nvars), 0) if (!all(jd > 0)) stop("Some excluded variables out of range") penalty.factor[jd] = 1 jd = as.integer(c(length(jd), jd)) } else jd = as.integer(0) vp = as.double(penalty.factor) internal.parms = glmnet.control() if (internal.parms$itrace) trace.it = 1 else { if (trace.it) { glmnet.control(itrace = 1) on.exit(glmnet.control(itrace = 0)) } } if (any(lower.limits > 0)) { stop("Lower limits should be non-positive") } if (any(upper.limits < 0)) { stop("Upper limits should be non-negative") } lower.limits[lower.limits == -Inf] = -internal.parms$big upper.limits[upper.limits == Inf] = internal.parms$big if (length(lower.limits) < nvars) { if (length(lower.limits) == 1) lower.limits = rep(lower.limits, nvars) else stop("Require length 1 or nvars lower.limits") } else lower.limits = lower.limits[seq(nvars)] if (length(upper.limits) < nvars) { if (length(upper.limits) == 1) upper.limits = rep(upper.limits, nvars) else stop("Require length 1 or nvars upper.limits") } else upper.limits = upper.limits[seq(nvars)] cl = rbind(lower.limits, upper.limits) if (any(cl == 0)) { fdev = glmnet.control()$fdev if (fdev != 0) { glmnet.control(fdev = 0) on.exit(glmnet.control(fdev = fdev)) } } storage.mode(cl) = "double" isd = as.integer(standardize) intr = as.integer(intercept) if (!missing(intercept) && family == "cox") warning("Cox model has no intercept") jsd = as.integer(standardize.response) thresh = as.double(thresh) if (is.null(lambda)) { if (lambda.min.ratio >= 1) stop("lambda.min.ratio should be less than 1") flmin = as.double(lambda.min.ratio) ulam = double(1) } else { flmin = as.double(1) if (any(lambda < 0)) stop("lambdas should be non-negative") ulam = as.double(rev(sort(lambda))) nlam = as.integer(length(lambda)) } is.sparse = FALSE if (inherits(x, "sparseMatrix")) { is.sparse = TRUE if (!inherits(x, "dgCMatrix")) x = as(as(as(x, "generalMatrix"), "CsparseMatrix"), "dMatrix") } else if (!inherits(x, "matrix")) { x <- data.matrix(x) } else { x <- x } if (!inherits(x, "sparseMatrix")) { storage.mode(x) <- "double" } if (trace.it) { if (relax) cat("Training Fit\n") pb <- createPB(min = 0, max = nlam, initial = 0, style = 3) } else { pb <- NULL } kopt = switch(match.arg(type.logistic), Newton = 0, modified.Newton = 1) if (family == "multinomial") { type.multinomial = match.arg(type.multinomial) if (type.multinomial == "grouped") kopt = 2 } kopt = as.integer(kopt) fit = switch(family, gaussian = elnet(x, is.sparse, y, weights, offset, type.gaussian, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, pb), poisson = fishnet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, pb), binomial = lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, kopt, family, pb), multinomial = lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit, kopt, family, pb), cox = coxnet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, vnames, maxit), mgaussian = mrelnet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, jsd, intr, vnames, maxit, pb)) if (trace.it) { utils::setTxtProgressBar(pb, nlam) close(pb) } if (is.null(lambda)) fit$lambda = fix.lam(fit$lambda) fit$call = this.call fit$nobs = nobs class(fit) = c(class(fit), "glmnet") } } if (relax) relax.glmnet(fit, x = x, y = y, weights = weights, offset = offset, lower.limits = lower.limits, upper.limits = upper.limits, penalty.factor = penalty.factor, check.args = FALSE, ...) else fit })(x = new("dgCMatrix", i = c(1L, 2L, 15L, 16L, 23L, 24L, 27L, 28L, 29L, 30L, 33L, 34L, 35L, 36L, 37L, 38L, 62L, 63L, 64L, 74L, 75L, 76L, 83L, 84L, 85L, 89L, 90L, 91L, 101L, 102L, 103L, 110L, 111L, 112L, 113L, 114L, 115L, 131L, 132L, 133L, 140L, 141L, 142L, 149L, 150L, 151L, 152L, 161L, 162L, 163L, 164L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 189L, 190L, 191L, 192L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 261L, 262L, 263L, 264L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L, 302L, 313L, 314L, 315L, 316L, 317L, 323L, 324L, 325L, 326L, 327L, 333L, 334L, 335L, 336L, 337L, 343L, 344L, 345L, 346L, 347L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 393L, 394L, 395L, 396L, 397L, 408L, 409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 460L, 461L, 462L, 463L, 464L, 465L, 472L, 473L, 474L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 498L, 499L, 500L, 501L, 508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L, 542L, 543L, 550L, 551L, 552L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L, 568L, 569L, 570L, 571L, 572L, 573L, 580L, 581L, 582L, 583L, 584L, 585L, 586L, 587L, 588L, 589L, 590L, 591L, 592L, 593L, 594L, 595L, 596L, 597L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L, 465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L, 542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L, 550L, 551L, 552L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L, 562L, 563L, 564L, 565L, 566L, 567L, 568L, 569L, 570L, 571L, 572L, 573L, 574L, 575L, 576L, 577L, 578L, 579L, 580L, 581L, 582L, 583L, 584L, 585L, 586L, 587L, 588L, 589L, 590L, 591L, 592L, 593L, 594L, 595L, 596L, 597L, 0L, 1L, 3L, 5L, 7L, 9L, 11L, 13L, 15L, 17L, 19L, 21L, 23L, 25L, 27L, 29L, 31L, 33L, 35L, 37L, 39L, 41L, 43L, 45L, 47L, 49L, 51L, 53L, 55L, 57L, 59L, 62L, 65L, 68L, 71L, 74L, 77L, 80L, 83L, 86L, 89L, 92L, 95L, 98L, 101L, 104L, 107L, 110L, 113L, 116L, 119L, 122L, 125L, 128L, 131L, 134L, 137L, 140L, 143L, 146L, 149L, 153L, 157L, 161L, 165L, 169L, 173L, 177L, 181L, 185L, 189L, 193L, 197L, 201L, 205L, 209L, 213L, 217L, 221L, 225L, 229L, 233L, 237L, 241L, 245L, 249L, 253L, 257L, 261L, 265L, 269L, 273L, 278L, 283L, 288L, 293L, 298L, 303L, 308L, 313L, 318L, 323L, 328L, 333L, 338L, 343L, 348L, 353L, 358L, 363L, 368L, 373L, 378L, 383L, 388L, 393L, 398L, 403L, 408L, 413L, 418L, 424L, 430L, 436L, 442L, 448L, 454L, 460L, 466L, 472L, 478L, 484L, 490L, 496L, 502L, 508L, 514L, 520L, 526L, 532L, 538L, 544L, 550L, 556L, 562L, 568L, 574L, 580L, 586L, 592L, 2L, 4L, 6L, 8L, 10L, 12L, 14L, 16L, 18L, 20L, 22L, 24L, 26L, 28L, 30L, 32L, 34L, 36L, 38L, 40L, 42L, 44L, 46L, 48L, 50L, 52L, 54L, 56L, 58L, 60L, 63L, 66L, 69L, 72L, 75L, 78L, 81L, 84L, 87L, 90L, 93L, 96L, 99L, 102L, 105L, 108L, 111L, 114L, 117L, 120L, 123L, 126L, 129L, 132L, 135L, 138L, 141L, 144L, 147L, 150L, 154L, 158L, 162L, 166L, 170L, 174L, 178L, 182L, 186L, 190L, 194L, 198L, 202L, 206L, 210L, 214L, 218L, 222L, 226L, 230L, 234L, 238L, 242L, 246L, 250L, 254L, 258L, 262L, 266L, 270L, 274L, 279L, 284L, 289L, 294L, 299L, 304L, 309L, 314L, 319L, 324L, 329L, 334L, 339L, 344L, 349L, 354L, 359L, 364L, 369L, 374L, 379L, 384L, 389L, 394L, 399L, 404L, 409L, 414L, 419L, 425L, 431L, 437L, 443L, 449L, 455L, 461L, 467L, 473L, 479L, 485L, 491L, 497L, 503L, 509L, 515L, 521L, 527L, 533L, 539L, 545L, 551L, 557L, 563L, 569L, 575L, 581L, 587L, 593L, 61L, 64L, 67L, 70L, 73L, 76L, 79L, 82L, 85L, 88L, 91L, 94L, 97L, 100L, 103L, 106L, 109L, 112L, 115L, 118L, 121L, 124L, 127L, 130L, 133L, 136L, 139L, 142L, 145L, 148L, 151L, 155L, 159L, 163L, 167L, 171L, 175L, 179L, 183L, 187L, 191L, 195L, 199L, 203L, 207L, 211L, 215L, 219L, 223L, 227L, 231L, 235L, 239L, 243L, 247L, 251L, 255L, 259L, 263L, 267L, 271L, 275L, 280L, 285L, 290L, 295L, 300L, 305L, 310L, 315L, 320L, 325L, 330L, 335L, 340L, 345L, 350L, 355L, 360L, 365L, 370L, 375L, 380L, 385L, 390L, 395L, 400L, 405L, 410L, 415L, 420L, 426L, 432L, 438L, 444L, 450L, 456L, 462L, 468L, 474L, 480L, 486L, 492L, 498L, 504L, 510L, 516L, 522L, 528L, 534L, 540L, 546L, 552L, 558L, 564L, 570L, 576L, 582L, 588L, 594L, 152L, 156L, 160L, 164L, 168L, 172L, 176L, 180L, 184L, 188L, 192L, 196L, 200L, 204L, 208L, 212L, 216L, 220L, 224L, 228L, 232L, 236L, 240L, 244L, 248L, 252L, 256L, 260L, 264L, 268L, 272L, 276L, 281L, 286L, 291L, 296L, 301L, 306L, 311L, 316L, 321L, 326L, 331L, 336L, 341L, 346L, 351L, 356L, 361L, 366L, 371L, 376L, 381L, 386L, 391L, 396L, 401L, 406L, 411L, 416L, 421L, 427L, 433L, 439L, 445L, 451L, 457L, 463L, 469L, 475L, 481L, 487L, 493L, 499L, 505L, 511L, 517L, 523L, 529L, 535L, 541L, 547L, 553L, 559L, 565L, 571L, 577L, 583L, 589L, 595L, 277L, 282L, 287L, 292L, 297L, 302L, 307L, 312L, 317L, 322L, 327L, 332L, 337L, 342L, 347L, 352L, 357L, 362L, 367L, 372L, 377L, 382L, 387L, 392L, 397L, 402L, 407L, 412L, 417L, 422L, 428L, 434L, 440L, 446L, 452L, 458L, 464L, 470L, 476L, 482L, 488L, 494L, 500L, 506L, 512L, 518L, 524L, 530L, 536L, 542L, 548L, 554L, 560L, 566L, 572L, 578L, 584L, 590L, 596L), p = c(0L, 268L, 866L, 1016L, 1165L, 1285L, 1375L, 1434L), Dim = c(598L, 7L), Dimnames = list( c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", 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"node0.339012260842819", "node2.36298412586669", "node5.04518837599029", "node7.69726033122722" )), x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 22.4028780552881, 25.7326597154358, 25.7326597154358, 16.7474100468688, 16.7474100468688, 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#>
#> Df %Dev Lambda
#> 1 7 10.8 0summary() provides a more informative overview. For a
fitted super learner, it prints a compact description of the ensemble,
the number of causes, learners, folds and nodes, the cross-validated
Poisson deviance used to compare the base learners, and the
cause-specific meta-learner coefficients. These coefficients show how
the ensemble combines the learners for each cause.
summary(sl_model)
#> Call:
#> Superlearner(..., learners = c(glm, lasso), metalearner = Learner_glmnet)
#>
#> Fitted object:
#> Class: poisson_superlearner
#> Number of competing risks: 2
#> Number of learners: 2
#> Learners: glm, lasso
#> Number of folds: 3
#> Maximum follow-up: 22.73178
#> Number of nodes: 6
#>
#> Cross-validation deviance (Average across V-Folds):
#> learner deviance
#> <char> <num>
#> 1: glm 171.4138
#> 2: lasso 178.9889
#>
#> Meta-learner coefficients:
#> k = 1:
#> (Intercept) glm lasso
#> 0.0000000 0.7280179 0.2479293
#>
#> k = 2:
#> (Intercept) glm lasso
#> 0.0000000 0.5989187 0.3458754You can also summarize one of the base learners stored inside the
ensemble, or a learner fitted directly with
fit_learner().
summary(sl_model, cause = 1, model = "glm")
#> Length Class Mode
#> a0 1 -none- numeric
#> beta 7 dgCMatrix S4
#> df 1 -none- numeric
#> dim 2 -none- numeric
#> lambda 1 -none- numeric
#> dev.ratio 1 -none- numeric
#> nulldev 1 -none- numeric
#> npasses 1 -none- numeric
#> jerr 1 -none- numeric
#> offset 1 -none- logical
#> call 7 -none- call
#> nobs 1 -none- numeric
summary(l0_model, cause = 1)
#> Length Class Mode
#> a0 1 -none- numeric
#> beta 7 dgCMatrix S4
#> df 1 -none- numeric
#> dim 2 -none- numeric
#> lambda 1 -none- numeric
#> dev.ratio 1 -none- numeric
#> nulldev 1 -none- numeric
#> npasses 1 -none- numeric
#> jerr 1 -none- numeric
#> offset 1 -none- logical
#> call 7 -none- call
#> nobs 1 -none- numericcoef() extracts model coefficients. For a fitted super
learner, coef() returns the meta-learner coefficients by
default, while the model argument can be used to extract
coefficients from one of the stored base learners.
coef(sl_model, cause = 1)
#> 3 x 1 sparse Matrix of class "dgCMatrix"
#> s0
#> (Intercept) .
#> Z1 0.7280179
#> Z2 0.2479293
coef(sl_model, cause = 1, model = "glm")
#> 8 x 1 sparse Matrix of class "dgCMatrix"
#> s0
#> (Intercept) -7.36035683
#> sex1 -1.05713940
#> diabetes_duration 0.26272645
#> node0 -1.61251128
#> node0.339012260842819 -0.04862647
#> node2.36298412586669 -0.09245152
#> node5.04518837599029 0.06596544
#> node7.69726033122722 -0.45621644
coef(l0_model, cause = 1)
#> 8 x 1 sparse Matrix of class "dgCMatrix"
#> s0
#> (Intercept) -7.36035683
#> sex1 -1.05713940
#> diabetes_duration 0.26272645
#> node0 -1.61251128
#> node0.339012260842819 -0.04862647
#> node2.36298412586669 -0.09245152
#> node5.04518837599029 0.06596544
#> node7.69726033122722 -0.45621644The low-level predict() method returns a data set with
one row per requested prediction time. In addition to the original
covariates, it includes the predicted cause-specific piecewise hazards
(pwch_1, pwch_2, …), the predicted survival
function, and the absolute risk for the chosen cause.
pred_sl <- predict(
sl_model,
newdata = d[1:2],
times = c(2, 5),
cause = 1
)
pred_sl
#> sex age diabetes_duration value_SBP value_LDL value_HBA1C
#> <fctr> <num> <num> <num> <num> <num>
#> 1: 0 48.56530 22.40288 137.5896 3.743464 67.12621
#> 2: 0 48.56530 22.40288 137.5896 3.743464 67.12621
#> 3: 1 57.73618 25.73266 136.8310 2.297953 64.23921
#> 4: 1 57.73618 25.73266 136.8310 2.297953 64.23921
#> value_Smoking value_Motion value_Albuminuria eGFR time.event.1
#> <fctr> <fctr> <fctr> <num> <num>
#> 1: 0 1 Normal 3.893651 0.3390123
#> 2: 0 1 Normal 3.893651 0.3390123
#> 3: 0 1 Normal 0.356103 0.3576302
#> 4: 0 1 Normal 0.356103 0.3576302
#> time.event.0 time.event.2 time_cvd status_cvd uncensored_time_cvd
#> <num> <num> <num> <num> <num>
#> 1: 10.484410 10.40121 0.3390123 1 0.3390123
#> 2: 10.484410 10.40121 0.3390123 1 0.3390123
#> 3: 3.945603 42.93017 0.3576302 1 0.3576302
#> 4: 3.945603 42.93017 0.3576302 1 0.3576302
#> uncensored_status_cvd time event uncensored_time uncensored_event pwch_1
#> <num> <num> <num> <num> <num> <num>
#> 1: 1 2 0 0.3390123 1 0.1787839
#> 2: 1 5 0 0.3390123 1 0.1647861
#> 3: 1 2 0 0.3576302 1 0.1565612
#> 4: 1 5 0 0.3576302 1 0.1443033
#> pwch_2 survival_function absolute_risk id
#> <num> <num> <num> <int>
#> 1: 0.02441779 0.7039082 0.2620830 1
#> 2: 0.03291557 0.3882101 0.5275388 1
#> 3: 0.01832687 0.7390550 0.2348009 2
#> 4: 0.02470491 0.4441727 0.4884598 2For interoperability with riskRegression, the package
also provides predictRisk(). This returns a matrix with one
column per requested time.
Risk predictions can be obtained directly from a learner fitted with
fit_learner():
cbind(
glm_direct = predictRisk(l0_model, newdata = d[1, ], times = 5, cause = 1),
lasso_direct = predictRisk(l1_model, newdata = d[1, ], times = 5, cause = 1)
)
#> [,1] [,2]
#> [1,] 0.5996443 0.2952072The same is possible from the fitted super learner object. Setting
model = "sl" uses the ensemble prediction, while setting
model equal to a learner label such as "glm"
or "lasso" uses the corresponding stored base learner from
the fitted ensemble.
cbind(
sl = predictRisk(sl_model, newdata = d[1, ], times = 5, cause = 1, model = "sl"),
glm_from_ensemble = predictRisk(sl_model, newdata = d[1, ], times = 5, cause = 1, model = "glm"),
lasso_from_ensemble = predictRisk(sl_model, newdata = d[1, ], times = 5, cause = 1, model = "lasso")
)
#> [,1] [,2] [,3]
#> [1,] 0.5275388 0.5996443 0.2952072This illustrates the two main prediction workflows supported by the
package: using a single fitted learner with fit_learner()
or using the fitted ensemble returned by
Superlearner().