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/dports/math/R-cran-VGAM/VGAM/R/
H A Dvlm.wfit.q42 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$misc <- list(M = M, n = n) nameattr
[all …]
H A Dvglm.fit.q45 n <- nrow(x) functionVar
58 n.save <- n
132 matrix(X.vlm.save %*% coefstart, n, M, byrow = TRUE) + offset
134 matrix(X.vlm.save * coefstart, n, M, byrow = TRUE) + offset
157 U <- vchol(wz, M = M, n = n, silent = !trace)
158 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n)
159 z <- eta + vbacksub(U, tvfor, M = M, n = n) - offset
348 U <- vchol(wz, M = M, n = n, silent = !trace)
349 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n)
350 z <- eta + vbacksub(U, tvfor, M = M, n = n) - offset
[all …]
H A Drrvglm.fit.q45 n <- dim(x)[1] functionVar
62 n.save <- n
259 U <- vchol(wz, M = M, n = n, silent = !trace)
260 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n)
261 z <- eta + vbacksub(U, tvfor, M = M, n = n) - offset
421 cat("\n")
464 U <- vchol(wz, M = M, n = n, silent=!trace)
465 tvfor <- vforsub(U, as.matrix(deriv.mu), M = M, n = n)
473 if (copy.X.vlm) c.list$X.vlm <- X.vlm.save
586 df.total = n*M,
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H A Dvgam.fit.q55 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 …]
H A Dmodel.matrix.vglm.q86 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)
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H A Dprint.vglm.q26 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 …]
H A Dprint.vlm.q12 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 …]
H A Dsummary.vlm.q36 n <- object@misc$n functionVar
37 nrow.X.vlm <- object@misc$nrow.X.vlm
38 ncol.X.vlm <- object@misc$ncol.X.vlm # May be NULL for CQO objects
280 cat("\nCall:\n")
290 cat("\nPearson residuals:\n")
294 cat("\nPearson residuals:\n")
300 cat("\nCoefficients:\n")
304 cat("\nNumber of responses: ", M, "\n")
314 cat("\nNames of responses:\n")
321 "on", round(rdf, digits), "degrees of freedom\n")
[all …]
H A Danova.vglm.q54 Col.Usex.vlm <- seq_len(length(Usex.vlm))[Usex.vlm]
56 X.vlm <- big.x.vlm[, Col.Usex.vlm, drop = FALSE]
168 n.lm <- nobs(object, type = "lm")
180 OOO <- matrix(0, n.lm, M)
192 big.x.vlm <- x.vlm
341 "\n\nModel: ",
343 if (length(lfuns) > 1) "\n\nLinks: " else "\n\nLink: ",
347 "\n\nResponse: ", as.character(varlist[-1L])[1L], "\n")
438 collapse = "\n"))
444 title <- "Analysis of Deviance Table\n"
[all …]
H A Ds.vam.q21 dX.vlm <- as.integer(dim(X.vlm.save))
22 pbig <- dX.vlm[2]
31 smooth.frame$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 …]
H A Dformula.vlm.q394 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 …]
H A Dpredict.vglm.q75 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 …]
H A DbAIC.q42 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$deriv # n x M matrix
[all …]
H A Dcqo.fit.q13 X.vlm.1save, modelno, Control,
78 cbind(matrix(0, nstar, p2star), X.vlm.1save)
149 X.vlm.1save, modelno, Control,
472 X.vlm.save <- if (nice31) {
486 if (length(coefstart) && length(X.vlm.save)) {
487 eta <- if (ncol(X.vlm.save) > 1)
488 X.vlm.save %*% coefstart + offset else
489 X.vlm.save * coefstart + offset
763 (ncol(X.vlm.save) - p2star)
764 X.vlm.1save <- if (p1star > 0) X.vlm.save[, -(1:p2star)] else NULL
[all …]
H A Dsummary.vglm.q191 cat("Reversed\n\n") else
192 cat("Not reversed\n\n")
251 logLik.vlm(object, ...))
440 if (length(vll <- logLik.vlm(x))) {
462 ncol.X.vlm <- dim(correl)[2]
463 if (ncol.X.vlm > 1) {
468 print(correl[-1, -ncol.X.vlm, drop = FALSE], quote = FALSE,
490 cat("\n\n")
575 vcov.vlm <- function(object, ...) {
578 } # vcov.vlm
[all …]
H A Dsummary.vgam.q30 nrow.X.vlm <- object@misc$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 …]
H A Dvlm.R8 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
H A Dpredict.vlm.q12 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)
/dports/multimedia/vlc/vlc-3.0.16/lib/
H A Dvlm.c123 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()
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/dports/math/R-cran-VGAM/VGAM/man/
H A Ddf.residual.Rd12 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
H A DlogLikvlm.Rd1 \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)
H A Dmodel.matrixqrrvglm.Rd5 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")
H A Dguplot.Rd4 \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
/dports/math/reduce/Reduce-svn5758-src/packages/redlog/ofsf/
H A Dofsfvsblock.red714 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 …]
/dports/audio/soundtracker/soundtracker-1.0.2.1/app/
H A Dmenubar.c504 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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