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/dports/math/R-cran-KFAS/KFAS/man/
H A Dhatvalues.KFS.Rd2 % 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)))
H A Dresiduals.KFS.Rd2 % 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:
H A Dfitted.SSModel.Rd2 % 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)
H A Dcoef.SSModel.Rd2 % 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)
H A Dprint.KFS.Rd3 \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.}
H A DKFAS-defunct.Rd5 \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.
H A Drstandard.KFS.Rd2 % 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"))
H A DmvInnovations.Rd10 \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
/dports/math/R-cran-KFAS/KFAS/
H A DNAMESPACE6 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)
H A DMD56 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 …]
H A DChangeLog28 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 …]
/dports/biology/sra-tools/sra-tools-2.11.0/ncbi-vdb/interfaces/klib/
H A Ddebug.h107 _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)
/dports/biology/ncbi-vdb/ncbi-vdb-2.11.0/interfaces/klib/
H A Ddebug.h107 _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)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/tests/results/
H A Dtest_exact_diffuse_filtering_multivariate.R22 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=…
H A Dtest_exact_diffuse_filtering.R89 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=…
H A Dtest_simulation_smoothing.R22 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=…
/dports/math/R-cran-KFAS/KFAS/tests/testthat/
H A DtestGLM.R6 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 …]
H A DtestNumerical.R16 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)
/dports/math/R-cran-KFAS/KFAS/inst/doc/
H A DKFAS.R28 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$model, filtering = "mean", smoothing = "none",
/dports/science/siesta/siesta-4.1.5/Src/SiestaXC/
H A Dggaxc.f558 DKFDD = THD * KFS / DS(IS)
1072 DKFDD = THD * KFS / DS(1)
1525 DKFDD = THD * KFS / DS(IS)
1747 DKFDD = THD * KFS / DS(IS)
1960 . KFS, PI, S, VXUNIF(2), ZETA
2009 DKFDD = KFS / DS(IS) / 3
2292 DKFDD = KFS / DS(IS) / 3
2390 . KFS, PI, S, VXUNIF(2), ZETA
2442 DKFDD = KFS / DS(IS) / 3
2676 . KFS, PI, S, S2, VXUNIF(2)
[all …]
/dports/math/R-cran-KFAS/KFAS/R/
H A Dpredict.SSModel.R193 out <- KFS(model = object, filtering = "mean", smoothing = "none")
199 out <- KFS(model = object, filtering = "none", smoothing = "mean")
203 out <- KFS(model = object, filtering = "state", smoothing = "none")
211 out <- signal(KFS(model = object, filtering = "none", smoothing = "state"),
234 out <- KFS(model = object, filtering = "signal", smoothing = "none",
241 out <- KFS(model = object, smoothing = "signal", maxiter = maxiter, expected = expected)
245 out <- KFS(model = object, filtering = "state", smoothing = "none",
254 out <- signal(KFS(model = object, smoothing = "state",
306 d <- KFS(approxSSM(object, maxiter = maxiter, expected = expected), smoothing = "none")$d
H A Dfitted.KFS.R48 out <- KFS(object, filtering = "mean", smoothing = "none", nsim = nsim, ...)
50 out <- KFS(object, filtering = "none", smoothing = "mean", nsim = nsim, ...)
/dports/science/libgridxc/libgridxc-libgridxc-0.9.6/src/
H A Dggaxc.F90624 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 …]
/dports/science/openmx/openmx3.8/source/
H A DXC_PBE.c47 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()
/dports/math/R-cran-sspir/sspir/man/
H A DFkfs.Rd7 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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