/dports/math/R-cran-VGAM/VGAM/R/ |
H A D | vlm.wfit.q | 42 n <- nrow(zmat) functionVar 53 U <- vchol(wz, M = M, n = n, silent = FALSE) 56 if (dU[2] != n) { 98 cat("m.objects$sp \n") 100 cat("m.objects$OFF \n") 123 cat("SP \n") 140 cat("sp.opt \n") 187 dim(fv) <- c(M, n) 188 fv <- vbacksub(U, fv, M = M, n = n) # Have to premultiply fv by U 207 ans$misc <- list(M = M, n = n) nameattr [all …]
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H A D | vglm.fit.q | 45 n <- nrow(x) functionVar 58 n.save <- n 132 matrix(X.vlm.save %*% coefstart, n, M, byrow = TRUE) + offset 134 matrix(X.vlm.save * coefstart, n, M, byrow = TRUE) + offset 157 U <- vchol(wz, M = M, n = n, silent = !trace) 158 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n) 159 z <- eta + vbacksub(U, tvfor, M = M, n = n) - offset 348 U <- vchol(wz, M = M, n = n, silent = !trace) 349 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n) 350 z <- eta + vbacksub(U, tvfor, M = M, n = n) - offset [all …]
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H A D | rrvglm.fit.q | 45 n <- dim(x)[1] functionVar 62 n.save <- n 259 U <- vchol(wz, M = M, n = n, silent = !trace) 260 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n) 261 z <- eta + vbacksub(U, tvfor, M = M, n = n) - offset 421 cat("\n") 464 U <- vchol(wz, M = M, n = n, silent=!trace) 465 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n) 473 if (copy.X.vlm) c.list$X.vlm <- X.vlm.save 586 df.total = n*M, [all …]
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H A D | vgam.fit.q | 55 n <- nrow(x) functionVar 64 n.save <- n 163 matrix(X.vlm.save %*% coefstart, n, M, byrow = TRUE) + offset 165 matrix(X.vlm.save * coefstart, n, M, byrow = TRUE) + offset 191 U <- vchol(wz, M = M, n = n, silent = !trace) 192 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n) 193 z <- eta + vbacksub(U, tvfor, M = M, n = n) - offset 317 U <- vchol(wz, M = M, n = n, silent = !trace) 318 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n) 319 z <- eta + vbacksub(U, tvfor, M = M, n = n) - offset [all …]
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H A D | model.matrix.vglm.q | 86 n.lm <- nrow(x.vlm) / M 87 if (round(n.lm) != n.lm) 88 stop("'n.lm' does not seem to be an integer") 93 X.lm.jay <- x.vlm[(0:(n.lm - 1)) * M + linpred.index, vecTF, 857 n.lm <- nobs(model, type = "lm") 863 offset <- matrix(model@offset, n.lm, M) 876 dfbeta <- matrix(0, n.lm, p.vlm) 884 for (ii in 1:n.lm) { 886 cat("\n", "Observation ", ii, "\n") 890 w.orig <- if (length(orig.w) != n.lm) [all …]
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H A D | print.vglm.q | 26 X.vlm.save <- model.matrix(object, type = "vlm") 28 X.vlm <- mux111(U, X.vlm.save, M = M) 29 X.vlm.aug <- rbind(X.vlm, 43 mgcv::magic.post.proc(X.vlm.aug, 61 endf.all0 <- diag(solve(crossprod(X.vlm.aug), crossprod(X.vlm))) 65 qr1 <- qr(X.vlm.aug) 66 qr2 <- qr(X.vlm) 163 llx <- logLik.vlm(object = object) 231 llx <- logLik.vlm(object = object) 294 llx <- logLik.vlm(object = object) [all …]
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H A D | print.vlm.q | 12 show.vlm <- function(object) { 14 cat("Call:\n") 25 n <- object@misc$n functionVar 46 setMethod("show", "vlm", 48 show.vlm(object)) 57 print.vlm <- function(x, ...) { 70 n <- x@misc$n functionVar 93 setMethod("show", "vlm", 95 print.vlm(object)) 101 setMethod("print", "vlm", [all …]
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H A D | summary.vlm.q | 36 n <- object@misc$n functionVar 37 nrow.X.vlm <- object@misc$nrow.X.vlm 38 ncol.X.vlm <- object@misc$ncol.X.vlm # May be NULL for CQO objects 280 cat("\nCall:\n") 290 cat("\nPearson residuals:\n") 294 cat("\nPearson residuals:\n") 300 cat("\nCoefficients:\n") 304 cat("\nNumber of responses: ", M, "\n") 314 cat("\nNames of responses:\n") 321 "on", round(rdf, digits), "degrees of freedom\n") [all …]
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H A D | anova.vglm.q | 54 Col.Usex.vlm <- seq_len(length(Usex.vlm))[Usex.vlm] 56 X.vlm <- big.x.vlm[, Col.Usex.vlm, drop = FALSE] 168 n.lm <- nobs(object, type = "lm") 180 OOO <- matrix(0, n.lm, M) 192 big.x.vlm <- x.vlm 341 "\n\nModel: ", 343 if (length(lfuns) > 1) "\n\nLinks: " else "\n\nLink: ", 347 "\n\nResponse: ", as.character(varlist[-1L])[1L], "\n") 438 collapse = "\n")) 444 title <- "Analysis of Deviance Table\n" [all …]
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H A D | s.vam.q | 21 dX.vlm <- as.integer(dim(X.vlm.save)) 22 pbig <- dX.vlm[2] 31 smooth.frame$n.lm <- dx[1] 78 smooth.frame$n.lm * sum(ncolHlist[nwhich])) { 82 xnrow.X.vlm <- labels(X.vlm.save)[[2]] 83 asgn <- attr(X.vlm.save, "assign") 89 smooth.frame$xnrow.X.vlm <- xnrow.X.vlm # Stored here 125 n.lm <- smooth.frame$n.lm 164 smomat = as.double(smomat), etamat = double(M * n.lm), 191 dim(fit$qr) <- dim(X.vlm.save) [all …]
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H A D | formula.vlm.q | 394 p.vlm <- ncol(x.vlm) 395 n.lm <- nobs(object, type = "lm") 425 OOO <- matrix(0, n.lm, M) 438 Wts <- rep(1, n.lm) # Safest (uses recycling and is a vector) 445 big.x.vlm <- x.vlm 522 n.vlm <- nobs(fit, type = "vlm") 523 edf <- n.vlm - df.residual(fit) 565 n.lm <- nobs(object, type = "lm") 571 OOO <- matrix(0, n.lm, M) 584 Wts <- rep.int(1, n.lm) [all …]
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H A D | predict.vglm.q | 75 predict.vlm(object, newdata = newdata, 80 predict.vlm(object, newdata = newdata, 93 predict.vlm(object, newdata = newdata, 129 predict.vlm(object, newdata = newdata, 134 predict.vlm(object, newdata = newdata, 224 predict.vlm(object, newdata = newdata, 229 predict.vlm(object, newdata = newdata, 362 n.ahead = 1, 372 predn$junk.component <- rep_len(coef(object), n.ahead) 373 predn$se.fit.junk.component <- rep_len(diag(vcov(object)), n.ahead) [all …]
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H A D | bAIC.q | 42 nparam.vlm <- function(object, dpar = TRUE, ...) { 186 setMethod("nparam", "vlm", 188 nparam.vlm(object, ...)) 192 nparam.vlm(object, ...)) 235 tot.par <- nparam.vlm(object, dpar = TRUE) 332 setMethod("AIC", "vlm", 367 setMethod("AICc", "vlm", 403 setMethod("BIC", "vlm", 461 X.vlm <- model.matrix(object, type = "vlm") 463 deriv1 <- derivmat$deriv # n x M matrix [all …]
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H A D | cqo.fit.q | 13 X.vlm.1save, modelno, Control, 78 cbind(matrix(0, nstar, p2star), X.vlm.1save) 149 X.vlm.1save, modelno, Control, 472 X.vlm.save <- if (nice31) { 486 if (length(coefstart) && length(X.vlm.save)) { 487 eta <- if (ncol(X.vlm.save) > 1) 488 X.vlm.save %*% coefstart + offset else 489 X.vlm.save * coefstart + offset 763 (ncol(X.vlm.save) - p2star) 764 X.vlm.1save <- if (p1star > 0) X.vlm.save[, -(1:p2star)] else NULL [all …]
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H A D | summary.vglm.q | 191 cat("Reversed\n\n") else 192 cat("Not reversed\n\n") 251 logLik.vlm(object, ...)) 440 if (length(vll <- logLik.vlm(x))) { 462 ncol.X.vlm <- dim(correl)[2] 463 if (ncol.X.vlm > 1) { 468 print(correl[-1, -ncol.X.vlm, drop = FALSE], quote = FALSE, 490 cat("\n\n") 575 vcov.vlm <- function(object, ...) { 578 } # vcov.vlm [all …]
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H A D | summary.vgam.q | 30 nrow.X.vlm <- object@misc$nrow.X.vlm 78 heading <- "DF for Terms\n\n" 124 cat("\nCall:\n", paste(deparse(x@call), sep = "\n", collapse = "\n"), 125 "\n", sep = "") 136 cat("\nPearson residuals:\n") 140 cat("\nPearson residuals:\n") 157 cat("\nNumber of additive predictors: ", M, "\n") 192 cat(paste("\n", prose, "Dispersion Parameter for ", 202 if (length(logLik.vlm(x))) 203 cat("\nLog-likelihood:", format(round(logLik.vlm(x), digits)), [all …]
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H A D | vlm.R | 8 vlm <- 71 n <- nrow(x) functionVar 79 prior.weights <- rep_len(1, n) 85 wz <- matrix(prior.weights, n, M) 131 n = nrow(x), nameattr 159 "df.total" = n*M, 199 attr(vlm, "smart") <- TRUE
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H A D | predict.vlm.q | 12 predict.vlm <- 142 nn <- if (!is.null(newdata)) nrow(newdata) else object@misc$n 194 object <- as(object, "vlm") # Coerce 200 Build.terms.vlm(x = X_vlm, coefs = coefs, 210 Build.terms.vlm(x = X_vlm, coefs = coefs, 334 } # predict.vlm() 338 setMethod("predict", "vlm", 340 predict.vlm(object, ...)) 354 U <- vchol(weights(fit, type = "working"), M = M, n = nn)
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/dports/multimedia/vlc/vlc-3.0.16/lib/ |
H A D | vlm.c | 123 free( p_instance->vlm ); in libvlc_vlm_release() 124 p_instance->vlm = NULL; in libvlc_vlm_release() 130 if( !p_instance->vlm ) in libvlc_vlm_init() 132 p_instance->vlm = malloc( sizeof(*p_instance->vlm) ); in libvlc_vlm_init() 384 int n; in libvlc_vlm_add_broadcast() local 397 for( n = 0; n < i_options; n++ ) in libvlc_vlm_add_broadcast() 402 if( n ) in libvlc_vlm_add_broadcast() 417 int n; in libvlc_vlm_add_vod() local 428 for( n = 0; n < i_options; n++ ) in libvlc_vlm_add_vod() 433 if( n ) in libvlc_vlm_add_vod() [all …]
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/dports/math/R-cran-VGAM/VGAM/man/ |
H A D | df.residual.Rd | 12 df.residual_vlm(object, type = c("vlm", "lm"), \dots) 43 is \eqn{n - p_{j}} where \eqn{p_{j}} is the number of 51 When \code{type = "vlm"} this is a single integer, and 69 head(model.matrix(fit, type = "vlm")) 72 df.residual(fit, type = "vlm") # n * M - p_VLM 73 nobs(fit, type = "vlm") # n * M 74 nvar(fit, type = "vlm") # p_VLM 76 df.residual(fit, type = "lm") # n - p_LM(j); Useful in some situations 77 nobs(fit, type = "lm") # n
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H A D | logLikvlm.Rd | 1 \name{logLik.vlm} 2 \alias{logLik.vlm} 12 \method{logLik}{vlm}(object, summation = TRUE, \dots) 24 If \code{FALSE} then a \eqn{n}-vector or 25 \eqn{n}-row matrix (with the number of responses as 51 If \code{summation = FALSE} then a \eqn{n}-vector or 52 \eqn{n}-row matrix (with the number of responses as 111 % logLik.vlm(object, summation = TRUE, \dots)
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H A D | model.matrixqrrvglm.Rd | 5 model.matrixqrrvglm(object, type = c("latvar", "lm", "vlm"), \dots) 19 The value \code{"vlm"} is the big VLM model matrix \emph{given C}. 31 returned: a large one (class \code{"vlm"} or one that inherits 44 When \code{type = "vlm"} this function calls \code{fnumat2R()} 85 set.seed(1); n <- 40; p <- 3; S <- 4; myrank <- 1 86 mydata <- rcqo(n, p, S, Rank = myrank, es.opt = TRUE, eq.max = TRUE) 92 model.matrix(mycqo, type = "vlm")
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H A D | guplot.Rd | 4 \alias{guplot.vlm} 17 guplot.vlm(object, ...) 27 \item{object}{ An object that inherits class \code{"vlm"}, 46 here to be \eqn{(i-0.5)/n}. 47 Here, \eqn{n} is the number of observations. 54 \code{guplot.default} and \code{guplot.vlm} are some
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/dports/math/reduce/Reduce-svn5758-src/packages/redlog/ofsf/ |
H A D | ofsfvsblock.red | 714 vlm := nil . 0; 716 vlm := vsdb_2gmln(nd, cdr vlm + 1, "#C0C0C0", vlm); 718 vlm := vsdb_2gmln(nd, cdr vlm + 1, "#00FF00", vlm); 720 vlm := vsdb_2gmln(nd, cdr vlm + 1, "#FF0000", vlm); 726 asserted procedure vsdb_2gmln(nd: VSnd, n: Integer, c: String, vlm: DottedPair): DottedPair; 732 return vlm; 733 vsdb_2gmln!-printn(nd, n, c); 736 vlm := vsdb_2gmln(p, n + 1, "#C0C0C0", vlm); 739 vsdb_2gmln!-printe(nd, cdr w, n) 741 return ((nd . n) . car vlm) . if n > cdr vlm then n else cdr vlm [all …]
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/dports/audio/soundtracker/soundtracker-1.0.2.1/app/ |
H A D | menubar.c | 504 struct vlm { struct 509 guint n; member 510 struct vlm volumes[1]; argument 521 for (i = 0; i < aaa->n; i++) { in amp_adjust_undo() 534 guint i, j, k, n; in amp_adjust() local 541 for (i = 0, n = 0; i < ST_NUM_INSTRUMENTS(xm); i++) in amp_adjust() 542 n += st_instrument_num_samples(&xm->instruments[i]); in amp_adjust() 543 if (!n) /* Module without samples, nothing to do */ in amp_adjust() 546 asize = sizeof(AmpAdjArg) + sizeof(struct vlm) * (n - 1); in amp_adjust() 548 arg->n = n; in amp_adjust()
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