/dports/math/R-cran-KFAS/KFAS/man/ |
H A D | hatvalues.KFS.Rd | 2 % Please edit documentation in R/hatvalues.KFS.R 3 \name{hatvalues.KFS} 4 \alias{hatvalues.KFS} 5 \title{Extract Hat Values from KFS Output} 7 \method{hatvalues}{KFS}(model, ...) 10 \item{model}{An object of class \code{KFS}.} 18 Extract hat values from KFS output, when \code{KFS} was run with signal 32 out <- KFS(model, filtering = "state", smoothing = "none") 36 c(hatvalues(KFS(model)))
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H A D | residuals.KFS.Rd | 2 % Please edit documentation in R/residuals.KFS.R 3 \name{residuals.KFS} 4 \alias{residuals.KFS} 5 \title{Extract Residuals of KFS output} 7 \method{residuals}{KFS}(object, type = c("recursive", "pearson", "response", "state"), ...) 10 \item{object}{KFS object} 17 Extract Residuals of KFS output 20 For object of class KFS, several types of residuals can be computed:
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H A D | fitted.SSModel.Rd | 2 % Please edit documentation in R/fitted.KFS.R 5 \alias{fitted.KFS} 8 \method{fitted}{KFS}(object, start = NULL, end = NULL, filtered = FALSE, ...) 13 \item{object}{An object of class \code{KFS} or \code{SSModel}.} 24 \item{...}{Additional arguments to \code{\link{KFS}}. 25 Ignored in method for object of class \code{KFS}.} 40 Computes fitted values from output of \code{KFS} 54 out <- KFS(model)
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H A D | coef.SSModel.Rd | 2 % Please edit documentation in R/coef.KFS.R 5 \alias{coef.KFS} 8 \method{coef}{KFS}( 30 \item{object}{An object of class \code{KFS} or \code{SSModel}.} 52 \item{\dots}{Additional arguments to \code{\link{KFS}}. 53 Ignored in method for object of class \code{KFS}.} 69 \code{SSModel} object or extract them from output of \code{KFS}. 83 out <- KFS(model)
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H A D | print.KFS.Rd | 3 \name{print.KFS} 4 \alias{print.KFS} 7 \method{print}{KFS}(x, type = "state", digits = max(3L, getOption("digits") - 3L), ...) 10 \item{x}{output object from function KFS.}
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H A D | KFAS-defunct.Rd | 5 \alias{deviance.KFS} 11 \method{deviance}{KFS}(object, ...) 27 Deviance.KFS was removed as it was mostly useless. The value was not a \eqn{-2*(logL-logL*)} 33 From \code{rstandard.KFS} and \code{residuals.KFS}: Computation of deviance residuals.
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H A D | rstandard.KFS.Rd | 2 % Please edit documentation in R/rstandard.KFS.R 3 \name{rstandard.KFS} 4 \alias{rstandard.KFS} 5 \title{Extract Standardized Residuals from KFS output} 7 \method{rstandard}{KFS}( 17 \item{model}{KFS object} 37 Extract Standardized Residuals from KFS output 40 For object of class KFS with fully Gaussian observations, several 88 out <- KFS(modelNile, smoothing = c("state", "mean", "disturbance"))
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H A D | mvInnovations.Rd | 10 \item{x}{Object of class \code{KFS}.} 20 step-ahead prediction errors and their variances using the output of \code{\link{KFS}}. 24 # Compute the filtered estimates based on the KFS output
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/dports/math/R-cran-KFAS/KFAS/ |
H A D | NAMESPACE | 6 S3method(coef,KFS) 8 S3method(deviance,KFS) 9 S3method(fitted,KFS) 11 S3method(hatvalues,KFS) 15 S3method(print,KFS) 17 S3method(residuals,KFS) 18 S3method(rstandard,KFS) 21 export(KFS)
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H A D | MD5 | 6 de2e7ba48e11f26c12b6690fcb5f2d2d *R/KFS.R 17 154af2eeec5c30dcc2c0de67a7f35f77 *R/coef.KFS.R 20 e54afcf285c6c10171fdf0d5dcda2a98 *R/fitted.KFS.R 21 b436a3a3c60edd5a935afd9ac9f76962 *R/hatvalues.KFS.R 33 5558ec8667b606e4f9cd255b6b39ccac *R/residuals.KFS.R 34 4ea6a178a1fac4702dc522fc5b442186 *R/rstandard.KFS.R 53 db85d62f9029123b65ce7f3c5620dbe6 *man/KFS.Rd 63 7a2dfbd50751f9694226fc3355e40d26 *man/hatvalues.KFS.Rd 70 2b3485c1a72281e67a03aa864590c53e *man/print.KFS.Rd 73 dbf5aff0515ffb1bccddeafd86891933 *man/residuals.KFS.Rd [all …]
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H A D | ChangeLog | 28 in logLik, approxSSM, importanceSSM, and KFS. 68 * KFS: Fixed a bug in KFS which caused the returned Ptt to be wrong in most cases. 70 * KFS: Added argument return_model to KFS, setting this to FALSE can save some memory. 116 * rstandard.KFS: Correct names for standardized state residuals. 117 * rstandard.KFS and residuals.KFS: Deviance residuals are now defunct. 123 estimates from the one-step-ahead predictions computed by KFS. 195 * Subset methods for SSModel and deviance.KFS are now defunct. 258 * rtandard.KFS and residuals.KFS: Deprecated deviance residuals. 259 * rtandard.KFS and residuals.KFS: Added support for recursive residuals for non-Gaussian models. 287 filtered=TRUE if they were present in KFS object. [all …]
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/dports/biology/sra-tools/sra-tools-2.11.0/ncbi-vdb/interfaces/klib/ |
H A D | debug.h | 107 _module(KFG) _module(KFS) _module(KNS) _module(KRYPTO) \ 145 _condition(KFS,MD5) _condition(KFS,DLL) _condition(KFS,KFFENTRY) _condition(KFS,KFF) \ 146 _condition(KFS,ARCENTRY) _condition(KFS,ARC) _condition(KFS,TOCENTRY) _condition(KFS,TOC) \ 147 _condition(KFS,TARENTRY) _condition(KFS,TAR) _condition(KFS,SRASORT) _condition(KFS,GZIP) \ 148 _condition(KFS,DIR) _condition(KFS,COUNTER) _condition(KFS,BZIP) _condition(KFS,SYS) \ 149 _condition(KFS,POS) _condition(KFS,PAGE) _condition(KFS,FILE)
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/dports/biology/ncbi-vdb/ncbi-vdb-2.11.0/interfaces/klib/ |
H A D | debug.h | 107 _module(KFG) _module(KFS) _module(KNS) _module(KRYPTO) \ 145 _condition(KFS,MD5) _condition(KFS,DLL) _condition(KFS,KFFENTRY) _condition(KFS,KFF) \ 146 _condition(KFS,ARCENTRY) _condition(KFS,ARC) _condition(KFS,TOCENTRY) _condition(KFS,TOC) \ 147 _condition(KFS,TARENTRY) _condition(KFS,TAR) _condition(KFS,SRASORT) _condition(KFS,GZIP) \ 148 _condition(KFS,DIR) _condition(KFS,COUNTER) _condition(KFS,BZIP) _condition(KFS,SYS) \ 149 _condition(KFS,POS) _condition(KFS,PAGE) _condition(KFS,FILE)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/tests/results/ |
H A D | test_exact_diffuse_filtering_multivariate.R | 22 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 27 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 33 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 39 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 49 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=…
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H A D | test_exact_diffuse_filtering.R | 89 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 106 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 112 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 135 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 146 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=…
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H A D | test_simulation_smoothing.R | 22 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 107 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 144 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 177 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=… 215 kf <- KFS(mod, c("state", "signal", "mean"), c("state", "signal", "mean", "disturbance"), simplify=…
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/dports/math/R-cran-KFAS/KFAS/tests/testthat/ |
H A D | testGLM.R | 6 expect_equivalent(coef(KFS(model,theta=7.1)),coef(KFS(model))) 7 expect_equivalent(coef(KFS(model,theta=-4.6)),coef(KFS(model))) 18 tmp<-KFS(model.gaussian,filtering="state",smoothing="none") 21 kfas.gaussian <-KFS(model.gaussian,smoothing=c('state','signal','mean')) 28 kfas.poisson<-KFS(model.poisson,smoothing=c('state','signal','mean')) 38 kfas.binomial<-KFS(model.binomial,smoothing=c('state','signal','mean'),maxiter=1000,convtol=1e-15) 39 kfas.binomial2<-KFS(model.binomial,smoothing=c('state','signal','mean'),maxiter=1000) 50 kfas.gamma1<-KFS(model.gamma1,smoothing=c('state','signal','mean'), expected = TRUE) 62 kfas.NB<-KFS(model.NB,smoothing='mean', expected = TRUE) 68 kfas.NB<-KFS(model.NB,smoothing='mean', expected = TRUE) [all …]
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H A D | testNumerical.R | 16 expect_warning(out <- KFS(fit$model), NA) 32 expect_warning(out <- KFS(fit$model), NA) 48 expect_warning(out <- KFS(fit$model), NA) 105 expect_warning(out <- KFS(fit$model), NA)
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/dports/math/R-cran-KFAS/KFAS/inst/doc/ |
H A D | KFAS.R | 28 out_gaussian <- KFS(fit_gaussian$model) 43 out_poisson <- KFS(fit_poisson$model, smoothing = "state") 76 (out_arima <- KFS(fit_arima$model)) 77 (out_structural <- KFS(fit_structural$model)) 148 out_poisson <- KFS(fit_poisson$model) 202 out <- KFS(fit$model, nsim = 1000) 210 res <- rstandard(KFS(fit$model, filtering = "mean", smoothing = "none",
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/dports/science/siesta/siesta-4.1.5/Src/SiestaXC/ |
H A D | ggaxc.f | 558 DKFDD = THD * KFS / DS(IS) 1072 DKFDD = THD * KFS / DS(1) 1525 DKFDD = THD * KFS / DS(IS) 1747 DKFDD = THD * KFS / DS(IS) 1960 . KFS, PI, S, VXUNIF(2), ZETA 2009 DKFDD = KFS / DS(IS) / 3 2292 DKFDD = KFS / DS(IS) / 3 2390 . KFS, PI, S, VXUNIF(2), ZETA 2442 DKFDD = KFS / DS(IS) / 3 2676 . KFS, PI, S, S2, VXUNIF(2) [all …]
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/dports/math/R-cran-KFAS/KFAS/R/ |
H A D | predict.SSModel.R | 193 out <- KFS(model = object, filtering = "mean", smoothing = "none") 199 out <- KFS(model = object, filtering = "none", smoothing = "mean") 203 out <- KFS(model = object, filtering = "state", smoothing = "none") 211 out <- signal(KFS(model = object, filtering = "none", smoothing = "state"), 234 out <- KFS(model = object, filtering = "signal", smoothing = "none", 241 out <- KFS(model = object, smoothing = "signal", maxiter = maxiter, expected = expected) 245 out <- KFS(model = object, filtering = "state", smoothing = "none", 254 out <- signal(KFS(model = object, smoothing = "state", 306 d <- KFS(approxSSM(object, maxiter = maxiter, expected = expected), smoothing = "none")$d
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H A D | fitted.KFS.R | 48 out <- KFS(object, filtering = "mean", smoothing = "none", nsim = nsim, ...) 50 out <- KFS(object, filtering = "none", smoothing = "mean", nsim = nsim, ...)
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/dports/science/libgridxc/libgridxc-libgridxc-0.9.6/src/ |
H A D | ggaxc.F90 | 624 S = GDMS / (2 * KFS * DS(IS)) 633 DKFDD = THD * KFS / DS(IS) 1119 S = GDMS / (2 * KFS * DS(1)) 1129 DKFDD = THD * KFS / DS(1) 1578 DKFDD = THD * KFS / DS(IS) 1796 DKFDD = THD * KFS / DS(IS) 2053 DKFDD = KFS / DS(IS) / 3 2329 DKFDD = KFS / DS(IS) / 3 2476 DKFDD = KFS / DS(IS) / 3 2703 KFS, PI, S, S2, VXUNIF(2) local [all …]
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/dports/science/openmx/openmx3.8/source/ |
H A D | XC_PBE.c | 47 double DS[2],GDMS,KFS,s,f,DFDD,DFXDD[2],Vx_unif[2],Ex_unif[1]; in XC_PBE() local 213 KFS = pow(3.0*PI*PI*DS[IS],THD); in XC_PBE() 214 s = GDMS/(2.0*KFS*DS[IS]); in XC_PBE() 225 DKFDD = THD * KFS/DS[IS]; in XC_PBE() 226 DSDD = s*(-(DKFDD/KFS) - 1.0/DS[IS]); in XC_PBE()
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/dports/math/R-cran-sspir/sspir/man/ |
H A D | Fkfs.Rd | 7 using \code{\link[KFAS]{KFS}} or \code{\link[KFAS]{logLik.SSModel}}} 10 \code{\link[KFAS]{KFS}} for gaussian state 28 …{SS}on which Kalman filtering followed by smoothing using \code{\link[KFAS]{KFS}} may be performed. 31 …from \code{\link[KFAS]{KFS}}. The second object, \code{ss}, of class \code{\link{SS}} with updated…
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