Prints a compact description of the fitted base learner, including the learner type, the time-grid used, and (optionally) the fitted model object for a given cause.
Usage
# S3 method for class 'base_learner'
print(x, cause = 1, ...)Arguments
- x
base_learnerobject returned byfit_learner().- cause
numeric(1)orNULL. Which cause to print the fitted model for. IfNULL, prints one line per cause (classes only) instead of printing the full fitted objects.- ...
Passed to the underlying fitted object
print()method whencauseis a single integer.
Examples
d <- simulateStenoT1(30, competing_risks = TRUE)
lrn <- Learner_glmnet(
covariates = c("sex", "value_LDL"),
lambda = 0.01,
cross_validation = FALSE
)
bl <- fit_learner(
d,
learner = lrn,
id = "id",
status = "status_cvd",
event_time = "time_cvd",
number_of_nodes = 3
)
print(bl, cause = NULL)
#>
#> 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 = NULL, pmax = NULL, 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, cox.ties = c("breslow", "efron"), control = list(), ...) { this.call = match.call() cox.ties.user <- !missing(cox.ties) 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") rc <- .resolve_control(control = control, nvars = nvars, deprecated = list(thresh = if (!missing(thresh)) thresh, maxit = if (!missing(maxit)) maxit, dfmax = if (!missing(dfmax)) dfmax, pmax = if (!missing(pmax)) pmax, trace.it = if (!missing(trace.it)) trace.it)) resolved_control <- rc$control if (!is.null(rc$restore)) on.exit(rc$restore(), add = TRUE) thresh <- resolved_control$thresh maxit <- resolved_control$maxit dfmax <- resolved_control$dfmax pmax <- resolved_control$pmax trace.it <- resolved_control$trace.it 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 (is.function(penalty.factor)) penalty.factor <- penalty.factor(x = x, y = y, weights = weights) penalty.factor <- check.penalty.factor(penalty.factor, nvars) if (!is.character(family)) { fit = glmnet.path(x, y, weights = weights, lambda = lambda, nlambda = nlambda, lambda.min.ratio = lambda.min.ratio, alpha = alpha, offset = offset, family = family, standardize = standardize, intercept = intercept, penalty.factor = penalty.factor, exclude = exclude, lower.limits = lower.limits, upper.limits = upper.limits, control = resolved_control) fit$call = this.call } else { family = match.arg(family) cox.ties = match.arg(cox.ties) if (family == "cox" && !cox.ties.user) { warning("Starting in glmnet 5.1, the default Cox tie-handling ", "method will change from 'breslow' to 'efron' (matching ", "survival::coxph). To silence this message and lock in the ", "v5.0 default, pass cox.ties = 'breslow' explicitly. To ", "preview the v5.1 behavior, pass cox.ties = 'efron'.", call. = FALSE) } efron = (cox.ties == "efron") if (FALSE) { } else { if (alpha > 1) { warning("alpha >1; set to 1") alpha = 1 } if (alpha < 0) { warning("alpha<0; set to 0") alpha = 0 } alpha = as.double(alpha) nlam = as.integer(nlambda) y = drop(y) dimy = dim(y) nrowy = ifelse(is.null(dimy), length(y), dimy[1]) if (nrowy != nobs) stop(paste("number of observations in y (", nrowy, ") not equal to the number of rows of x (", nobs, ")", sep = "")) vnames = colnames(x) if (is.null(vnames)) vnames = paste("V", seq(nvars), sep = "") ne = as.integer(dfmax) nx = as.integer(pmax) if (is.null(exclude)) exclude = integer(0) if (any(penalty.factor == Inf)) { exclude = c(exclude, seq(nvars)[penalty.factor == Inf]) exclude = sort(unique(exclude)) } if (length(exclude) > 0) { jd = match(exclude, seq(nvars), 0) if (!all(jd > 0)) stop("Some excluded variables out of range") penalty.factor[jd] = 1 jd = as.integer(c(length(jd), jd)) } else jd = as.integer(0) vp = as.double(penalty.factor) if (trace.it && !resolved_control$itrace) { glmnet.control(itrace = 1) on.exit(glmnet.control(itrace = 0), add = TRUE) } 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] = -resolved_control$big upper.limits[upper.limits == Inf] = resolved_control$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 = resolved_control$fdev if (fdev != 0) { glmnet.control(fdev = 0) on.exit(glmnet.control(fdev = fdev), add = TRUE) } } 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, pb, efron), 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, 3L, 4L, 5L, 6L, 13L, 14L, 15L, 16L, 17L, 18L, 25L, 26L, 27L, 31L, 32L, 33L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 73L, 74L, 75L, 76L, 85L, 86L, 87L, 88L, 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, 2L, 4L, 6L, 8L, 10L, 12L, 14L, 16L, 18L, 20L, 23L, 26L, 29L, 32L, 35L, 38L, 41L, 44L, 47L, 50L, 54L, 58L, 62L, 66L, 70L, 74L, 78L, 82L, 86L, 21L, 24L, 27L, 30L, 33L, 36L, 39L, 42L, 45L, 48L, 51L, 55L, 59L, 63L, 67L, 71L, 75L, 79L, 83L, 87L, 52L, 56L, 60L, 64L, 68L, 72L, 76L, 80L, 84L, 88L), p = c(0L, 45L, 134L, 163L, 183L, 193L, 193L), Dim = c(89L, 6L), Dimnames = list(c("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"), c("sex1", "value_LDL", "noden2", "noden3", "noden4", "noden5")), 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, 3.75113702973314, 1.96896348671084, 1.96896348671084, 3.19444441475125, 3.19444441475125, 3.35338653089683, 3.35338653089683, 2.91838033829596, 2.91838033829596, 1.14800918111279, 1.14800918111279, 2.40052867415048, 2.40052867415048, 2.27470856391387, 2.27470856391387, 2.61945764272373, 2.61945764272373, 3.70333915671442, 3.70333915671442, 1.31146768642795, 1.31146768642795, 1.31146768642795, 1.24899053984835, 1.24899053984835, 1.24899053984835, 3.7000560710817, 3.7000560710817, 3.7000560710817, 3.37801485750764, 3.37801485750764, 3.37801485750764, 2.61063895986695, 2.61063895986695, 2.61063895986695, 2.24223853989282, 2.24223853989282, 2.24223853989282, 1.42267575624826, 1.42267575624826, 1.42267575624826, 1.97769203467776, 1.97769203467776, 1.97769203467776, 1.8713537355373, 1.8713537355373, 1.8713537355373, 1.22433572211247, 1.22433572211247, 1.22433572211247, 2.32654594208734, 2.32654594208734, 2.32654594208734, 2.32654594208734, 2.8350904586317, 2.8350904586317, 2.8350904586317, 2.8350904586317, 2.16879414036456, 2.16879414036456, 2.16879414036456, 2.16879414036456, 0.609800496031174, 0.609800496031174, 0.609800496031174, 0.609800496031174, 1.7660400611433, 1.7660400611433, 1.7660400611433, 1.7660400611433, 2.08790894000342, 2.08790894000342, 2.08790894000342, 2.08790894000342, 3.17809437418561, 3.17809437418561, 3.17809437418561, 3.17809437418561, 2.04436395030194, 2.04436395030194, 2.04436395030194, 2.04436395030194, 3.07858724822404, 3.07858724822404, 3.07858724822404, 3.07858724822404, 2.83818194018515, 2.83818194018515, 2.83818194018515, 2.83818194018515, 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), factors = list()), y = c(1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), family = "poisson", offset = c(-2.24236513716394, -2.24236513716394, -1.03256988159489, -2.24236513716394, -0.484314147990968, -2.24236513716394, 0.880761940622377, -2.24236513716394, 1.0934998969951, -2.24236513716394, 1.19010716592462, -2.24236513716394, 1.20744263390279, -2.24236513716394, 1.26306453432275, -2.24236513716394, 1.28253525158156, -2.24236513716394, 1.74776927527718, -2.24236513716394, 1.74776927527718, -1.61354556937815, -2.24236513716394, 1.74776927527718, -1.20932445118175, -2.24236513716394, 1.74776927527718, 0.0341880282768864, -2.24236513716394, 1.74776927527718, 0.0859551662021467, -2.24236513716394, 1.74776927527718, 0.537428636102455, -2.24236513716394, 1.74776927527718, 0.573503709141078, -2.24236513716394, 1.74776927527718, 0.643309202578801, -2.24236513716394, 1.74776927527718, 0.714882728058492, -2.24236513716394, 1.74776927527718, 0.970503733706702, -2.24236513716394, 1.74776927527718, 1.14425729130492, -2.24236513716394, 1.74776927527718, 1.14425729130492, -1.76009447152746, -2.24236513716394, 1.74776927527718, 1.14425729130492, -0.549425453433788, -2.24236513716394, 1.74776927527718, 1.14425729130492, 0.190617559071275, -2.24236513716394, 1.74776927527718, 1.14425729130492, 0.74784744709797, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.08594381011609, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.21764030365622, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.29687399430634, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.52379935570823, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.65520793420484, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.75112696449948), lambda = 0.01, intercept = TRUE)
#>
#> Df %Dev Lambda
#> 1 3 3.77 0.01
#>
#> 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 = NULL, pmax = NULL, 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, cox.ties = c("breslow", "efron"), control = list(), ...) { this.call = match.call() cox.ties.user <- !missing(cox.ties) 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") rc <- .resolve_control(control = control, nvars = nvars, deprecated = list(thresh = if (!missing(thresh)) thresh, maxit = if (!missing(maxit)) maxit, dfmax = if (!missing(dfmax)) dfmax, pmax = if (!missing(pmax)) pmax, trace.it = if (!missing(trace.it)) trace.it)) resolved_control <- rc$control if (!is.null(rc$restore)) on.exit(rc$restore(), add = TRUE) thresh <- resolved_control$thresh maxit <- resolved_control$maxit dfmax <- resolved_control$dfmax pmax <- resolved_control$pmax trace.it <- resolved_control$trace.it if (is.null(weights)) weights = rep(1, nobs) else if (length(weights) != nobs) stop(paste("number of elements in weights (", length(weights), ") not equal to the number of rows of x (", nobs, ")", sep = "")) if (is.function(exclude)) exclude <- check.exclude(exclude(x = x, y = y, weights = weights), nvars) if (is.function(penalty.factor)) penalty.factor <- penalty.factor(x = x, y = y, weights = weights) penalty.factor <- check.penalty.factor(penalty.factor, nvars) if (!is.character(family)) { fit = glmnet.path(x, y, weights = weights, lambda = lambda, nlambda = nlambda, lambda.min.ratio = lambda.min.ratio, alpha = alpha, offset = offset, family = family, standardize = standardize, intercept = intercept, penalty.factor = penalty.factor, exclude = exclude, lower.limits = lower.limits, upper.limits = upper.limits, control = resolved_control) fit$call = this.call } else { family = match.arg(family) cox.ties = match.arg(cox.ties) if (family == "cox" && !cox.ties.user) { warning("Starting in glmnet 5.1, the default Cox tie-handling ", "method will change from 'breslow' to 'efron' (matching ", "survival::coxph). To silence this message and lock in the ", "v5.0 default, pass cox.ties = 'breslow' explicitly. To ", "preview the v5.1 behavior, pass cox.ties = 'efron'.", call. = FALSE) } efron = (cox.ties == "efron") if (FALSE) { } 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) if (trace.it && !resolved_control$itrace) { glmnet.control(itrace = 1) on.exit(glmnet.control(itrace = 0), add = TRUE) } if (any(lower.limits > 0)) { stop("Lower limits should be non-positive") } if (any(upper.limits < 0)) { stop("Upper limits should be non-negative") } lower.limits[lower.limits == -Inf] = -resolved_control$big upper.limits[upper.limits == Inf] = resolved_control$big if (length(lower.limits) < nvars) { if (length(lower.limits) == 1) lower.limits = rep(lower.limits, nvars) else stop("Require length 1 or nvars lower.limits") } else lower.limits = lower.limits[seq(nvars)] if (length(upper.limits) < nvars) { if (length(upper.limits) == 1) upper.limits = rep(upper.limits, nvars) else stop("Require length 1 or nvars upper.limits") } else upper.limits = upper.limits[seq(nvars)] cl = rbind(lower.limits, upper.limits) if (any(cl == 0)) { fdev = resolved_control$fdev if (fdev != 0) { glmnet.control(fdev = 0) on.exit(glmnet.control(fdev = fdev), add = TRUE) } } 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, pb, efron), 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, 3L, 4L, 5L, 6L, 13L, 14L, 15L, 16L, 17L, 18L, 25L, 26L, 27L, 31L, 32L, 33L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 73L, 74L, 75L, 76L, 85L, 86L, 87L, 88L, 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, 2L, 4L, 6L, 8L, 10L, 12L, 14L, 16L, 18L, 20L, 23L, 26L, 29L, 32L, 35L, 38L, 41L, 44L, 47L, 50L, 54L, 58L, 62L, 66L, 70L, 74L, 78L, 82L, 86L, 21L, 24L, 27L, 30L, 33L, 36L, 39L, 42L, 45L, 48L, 51L, 55L, 59L, 63L, 67L, 71L, 75L, 79L, 83L, 87L, 52L, 56L, 60L, 64L, 68L, 72L, 76L, 80L, 84L, 88L), p = c(0L, 45L, 134L, 163L, 183L, 193L, 193L), Dim = c(89L, 6L), Dimnames = list(c("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"), c("sex1", "value_LDL", "noden2", "noden3", "noden4", "noden5")), 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, 3.75113702973314, 1.96896348671084, 1.96896348671084, 3.19444441475125, 3.19444441475125, 3.35338653089683, 3.35338653089683, 2.91838033829596, 2.91838033829596, 1.14800918111279, 1.14800918111279, 2.40052867415048, 2.40052867415048, 2.27470856391387, 2.27470856391387, 2.61945764272373, 2.61945764272373, 3.70333915671442, 3.70333915671442, 1.31146768642795, 1.31146768642795, 1.31146768642795, 1.24899053984835, 1.24899053984835, 1.24899053984835, 3.7000560710817, 3.7000560710817, 3.7000560710817, 3.37801485750764, 3.37801485750764, 3.37801485750764, 2.61063895986695, 2.61063895986695, 2.61063895986695, 2.24223853989282, 2.24223853989282, 2.24223853989282, 1.42267575624826, 1.42267575624826, 1.42267575624826, 1.97769203467776, 1.97769203467776, 1.97769203467776, 1.8713537355373, 1.8713537355373, 1.8713537355373, 1.22433572211247, 1.22433572211247, 1.22433572211247, 2.32654594208734, 2.32654594208734, 2.32654594208734, 2.32654594208734, 2.8350904586317, 2.8350904586317, 2.8350904586317, 2.8350904586317, 2.16879414036456, 2.16879414036456, 2.16879414036456, 2.16879414036456, 0.609800496031174, 0.609800496031174, 0.609800496031174, 0.609800496031174, 1.7660400611433, 1.7660400611433, 1.7660400611433, 1.7660400611433, 2.08790894000342, 2.08790894000342, 2.08790894000342, 2.08790894000342, 3.17809437418561, 3.17809437418561, 3.17809437418561, 3.17809437418561, 2.04436395030194, 2.04436395030194, 2.04436395030194, 2.04436395030194, 3.07858724822404, 3.07858724822404, 3.07858724822404, 3.07858724822404, 2.83818194018515, 2.83818194018515, 2.83818194018515, 2.83818194018515, 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), factors = list()), y = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), family = "poisson", offset = c(-2.24236513716394, -2.24236513716394, -1.03256988159489, -2.24236513716394, -0.484314147990968, -2.24236513716394, 0.880761940622377, -2.24236513716394, 1.0934998969951, -2.24236513716394, 1.19010716592462, -2.24236513716394, 1.20744263390279, -2.24236513716394, 1.26306453432275, -2.24236513716394, 1.28253525158156, -2.24236513716394, 1.74776927527718, -2.24236513716394, 1.74776927527718, -1.61354556937815, -2.24236513716394, 1.74776927527718, -1.20932445118175, -2.24236513716394, 1.74776927527718, 0.0341880282768864, -2.24236513716394, 1.74776927527718, 0.0859551662021467, -2.24236513716394, 1.74776927527718, 0.537428636102455, -2.24236513716394, 1.74776927527718, 0.573503709141078, -2.24236513716394, 1.74776927527718, 0.643309202578801, -2.24236513716394, 1.74776927527718, 0.714882728058492, -2.24236513716394, 1.74776927527718, 0.970503733706702, -2.24236513716394, 1.74776927527718, 1.14425729130492, -2.24236513716394, 1.74776927527718, 1.14425729130492, -1.76009447152746, -2.24236513716394, 1.74776927527718, 1.14425729130492, -0.549425453433788, -2.24236513716394, 1.74776927527718, 1.14425729130492, 0.190617559071275, -2.24236513716394, 1.74776927527718, 1.14425729130492, 0.74784744709797, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.08594381011609, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.21764030365622, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.29687399430634, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.52379935570823, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.65520793420484, -2.24236513716394, 1.74776927527718, 1.14425729130492, 1.75112696449948), lambda = 0.01, intercept = TRUE)
#>
#> Df %Dev Lambda
#> 1 2 41.45 0.01