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/dports/math/R-cran-MSwM/MSwM/man/
H A DAIC.Rd1 \name{AIC-methods}
3 \alias{AIC}
4 \alias{AIC-methods}
5 \alias{AIC,MSM.glm-method}
6 \alias{AIC,MSM.lm-method}
7 \alias{AIC.MSM.lm}
8 \alias{AIC.MSM.glm}
16 AIC(object, ..., k = 2)
20 …meric value for the penalty per parameter to be used. The default \code{k=2} is the classical AIC.}
26 Returns a numeric value with the corresponding AIC (or BIC, or ..., depending on k).
[all …]
/dports/finance/R-cran-strucchange/strucchange/man/
H A DlogLik.breakpoints.Rd4 \alias{AIC.breakpointsfull}
8 Computation of log likelihood and AIC type information criteria
14 \method{AIC}{breakpointsfull}(object, breaks = NULL, ..., k = 2)
23 is the classical AIC, \code{k = log(n)} gives the BIC, if \code{n}
41 the AIC respectively.
57 plot(0:5, AIC(bp.nile, k = log(bp.nile$nobs)), type = "b")
58 ## AIC
59 plot(0:5, AIC(bp.nile), type = "b")
61 ## BIC, AIC, log likelihood of a single partition
63 AIC(bp.nile1, k = log(bp.nile1$nobs))
[all …]
/dports/math/R-cran-forecast/forecast/R/
H A Dtbats.R232 best.model$AIC <- Inf
249 if (new.model$AIC > best.model$AIC) {
320 aic.vector <- c(up.model$AIC, level.model$AIC, down.model$AIC)
338 if (down.model$AIC > best.model$AIC) {
366 if (up.model$AIC > best.model$AIC) {
381 if (non.seasonal.model$AIC < best.model$AIC) {
423 if (best.seasonal.model$AIC < best.model$AIC) {
435 if (new.model$AIC < best.model$AIC) {
494 if (second.model$AIC < first.model$AIC) {
563 if (second.model$AIC < first.model$AIC) {
[all …]
H A Dbats.R127 … AIC = -Inf, likelihood = -Inf, variance = 0, alpha = 0.9999, method = "BATS", call = match.call() nameattr
211 aics[i] <- models.list[[i]]$AIC
230 if (current.model$AIC < best.aic) {
231 best.aic <- current.model$AIC
256 return(list(AIC = Inf)) nameattr
263 if (first.model$AIC > non.seasonal.model$AIC) {
286 if (second.model$AIC < first.model$AIC) {
306 return(list(AIC = Inf)) nameattr
313 if (first.model$AIC > non.seasonal.model$AIC) {
336 if (second.model$AIC < first.model$AIC) {
[all …]
/dports/math/R/R-4.1.2/src/library/stats/man/
H A DAIC.Rd1 % File src/library/stats/man/AIC.Rd
6 \name{AIC}
8 \alias{AIC}
23 AIC(object, \dots, k = 2)
33 default \code{k = 2} is the classical AIC.}
37 the smaller the AIC or BIC, the better the fit.
74 AIC (or BIC, or \dots, depending on \code{k}).
94 AIC(lm1)
95 stopifnot(all.equal(AIC(lm1),
96 AIC(logLik(lm1))))
[all …]
H A DextractAIC.Rd8 \title{Extract AIC from a Fitted Model}
25 part in the AIC formula.}
40 \deqn{AIC = - 2\log L + k \times \mbox{edf},}{AIC = - 2*log L + k * edf,}
48 \code{\link{logLik}} and hence \code{\link{AIC}}. If \eqn{RSS}
53 \code{\link{AIC}} only handles unknown scale and uses the formula
55 where \eqn{w} are the weights. Further \code{AIC} counts the scale
59 compute the AIC: see the note under \code{logLik} about the
62 \code{k = 2} corresponds to the traditional AIC, using \code{k =
66 assumptions from those of methods for \code{\link{AIC}} (usually
72 to compare models of the same class (where only differences in AIC
[all …]
/dports/math/libRmath/R-4.1.1/src/library/stats/man/
H A DAIC.Rd1 % File src/library/stats/man/AIC.Rd
6 \name{AIC}
8 \alias{AIC}
23 AIC(object, \dots, k = 2)
33 default \code{k = 2} is the classical AIC.}
37 the smaller the AIC or BIC, the better the fit.
74 AIC (or BIC, or \dots, depending on \code{k}).
94 AIC(lm1)
95 stopifnot(all.equal(AIC(lm1),
96 AIC(logLik(lm1))))
[all …]
H A DextractAIC.Rd8 \title{Extract AIC from a Fitted Model}
25 part in the AIC formula.}
40 \deqn{AIC = - 2\log L + k \times \mbox{edf},}{AIC = - 2*log L + k * edf,}
48 \code{\link{logLik}} and hence \code{\link{AIC}}. If \eqn{RSS}
53 \code{\link{AIC}} only handles unknown scale and uses the formula
55 where \eqn{w} are the weights. Further \code{AIC} counts the scale
59 compute the AIC: see the note under \code{logLik} about the
62 \code{k = 2} corresponds to the traditional AIC, using \code{k =
66 assumptions from those of methods for \code{\link{AIC}} (usually
72 to compare models of the same class (where only differences in AIC
[all …]
/dports/math/R-cran-memisc/memisc/R/
H A Dxx-getSummary.R32 AIC <- AIC(obj) functionVar
33 BIC <- AIC(obj,k=log(N))
44 AIC = AIC, nameattr
106 AIC <- AIC(obj) functionVar
107 BIC <- AIC(obj,k=log(N))
119 AIC = AIC, nameattr
H A Dyy-mtable-ext-JasonWMorgan.R90 AIC <- -2*ll + 2*K functionVar
93 sumstat <- c(logLik = ll, AIC = AIC, BIC = BIC, N = N) nameattr
130 AIC <- -2*ll + 2*K functionVar
132 sumstat <- c(logLik = ll, AIC = AIC, BIC = BIC, N = N) nameattr
171 AIC <- -2*ll + 2*K functionVar
173 sumstat <- c(logLik = ll, AIC = AIC, BIC = BIC, N = N) nameattr
H A Dyz-getSummary-ordinal.R66 AIC <- AIC(obj) functionVar
67 BIC <- AIC(obj,k=log(N))
78 AIC = AIC, nameattr
183 AIC <- AIC(obj) functionVar
195 AIC = AIC, nameattr
H A Dyy-mtable-ext-DaveAtkins.R61 AIC <- AIC(obj) functionVar
62 BIC <- AIC(obj,k=log(N))
74 AIC = AIC, nameattr
/dports/math/R-cran-dlmodeler/dlmodeler/man/
H A DAIC.dlmodeler.fit.Rd1 \name{AIC.dlmodeler.fit}
4 \alias{AIC.dlmodeler.fit}
6 Log-likelihood and AIC of a model
9 Returns the log-likelihood or the AIC for a fitted DLM object.
16 \method{AIC}{dlmodeler.fit}(object, ..., k = 2)
26 The AIC is computed according to the formula
29 and \eqn{k = 2} for the usual AIC,
34 Returns a numeric value with the corresponding log-likelihiid, AIC,
/dports/math/gretl/gretl-2021d/doc/tex/
H A Dcriteria.tex50 {\rm AIC} = -2 \ell(\hat{\theta}) + 2k
58 the researcher seeks the minimum AIC.
65 {\rm AIC} = \ell(\hat{\theta}) - k
69 wants to maximize AIC.
83 {\rm AIC} = n(1 + \log 2\pi - \log n) + n\log {\rm SSR} + 2k
90 {\rm AIC} = n\log \left( \frac{\rm SSR}{n} \right) + 2k +
105 writing AIC$_G$ for the version given by Greene, we have
108 {\rm AIC}_G = \frac{1}{n} {\rm AIC} - (1 + \log 2\pi)
115 {\rm AIC}_R = \left( \frac{\rm SSR}{n} \right) e^{2k/n}
135 An alternative to the AIC which avoids this problem is the
[all …]
/dports/math/R-cran-VGAM/VGAM/man/
H A DAICvlm.Rd44 the default is the classical AIC.
54 in the fitted model, and \eqn{k = 2} for the usual AIC.
93 AIC has not been defined for QRR-VGLMs, yet.
96 Using AIC to compare \code{\link{posbinomial}} models
106 \code{AICc(...)} is the same as \code{AIC(..., corrected = TRUE)}.
128 The general applicability of \code{AIC} for the VGLM/VGAM classes
140 \code{\link[stats]{AIC}},
161 AIC(fit1)
163 AIC(fit1, corrected = TRUE) # Slow way
167 AIC(fit2)
[all …]
H A DBICvlm.Rd48 BIC, AIC and other ICs can have have many additive
98 \code{\link[stats]{AIC}}.
125 %c(AIC(fit.l), AIC(fit.g), AIC(fit.v))
126 %c(AIC(fit.l) - AIC(fit.v),
127 % AIC(fit.g) - AIC(fit.v))
/dports/math/R-cran-spdep/spdep/R/
H A Danova.sarlm.R47 AIC <- unlist(lapply(aux, AIC)) functionVar
49 AIC = AIC, logLik = logLik, check.names = FALSE) nameattr
76 AIC <- AIC(LL)
77 res <- data.frame("AIC"=AIC, "Log likelihood"=LL, "df"=attr(LL, "df"),
/dports/textproc/adabrowse/adabrowse_4.0.3/
H A Dad-driver.adb154 package AIC renames AD.Indices.Configuration; packspec
236 (Idx : in AIC.Index_Type;
241 AIC.Enter_Index (Idx);
436 AIC.Set_Structured (AIC.Unit_Index, True);
441 AIC.Set_Structured (AIC.Unit_Index, True);
442 AIC.Set_File_Name
446 AIC.Set_Structured (AIC.Unit_Index, False);
451 AIC.Set_Structured (AIC.Unit_Index, False);
452 AIC.Set_File_Name
460 AIC.Set_File_Name
[all …]
/dports/math/gretl/gretl-2021d/doc/tex_it/
H A Dcriteria.tex49 {\rm AIC} = -2 \ell(\hat{\theta}) + 2k
55 modello''. In questa formulazione, con AIC correlato negativamente alla
64 {\rm AIC} = \ell(\hat{\theta}) - k
68 si cercher� di massimizzare l'AIC.
82 {\rm AIC} = n(1 + \log 2\pi - \log n) + n\log {\rm SSR} + 2k
89 {\rm AIC} = n\log \left( \frac{\rm SSR}{n} \right) + 2k +
99 {\rm AIC} = \log \left( \frac{\rm SSR}{n} \right) + \frac{2k}{n}
104 AIC$_G$ la versione proposta da Greene, abbiamo
107 {\rm AIC}_G = \frac{1}{n} {\rm AIC} - (1 + \log 2\pi)
114 {\rm AIC}_R = \left( \frac{\rm SSR}{n} \right) e^{2k/n}
[all …]
/dports/biology/hyphy/hyphy-2.5.33/res/TemplateBatchFiles/
H A DAAModelComparison.bf71 AIC = 2(-res[1][0]+params);
90 Format (AIC, 9,3), " | ");
105 resultMatrix[midx][2] = AIC;
109 if (AIC < bestAIC)
111 bestAIC = AIC;
176 capString * "| Log Likelihood | #prms | AIC Score | c-AIC Score | Tree Length |\n";
222 fprintf (stdout, "\n\nBest AIC model:\n\t", modelMatrixList[bestAICidx][0], " with the score of ", …
226 …fprintf (stdout, "\n\nBest c-AIC model:\n\t", modelMatrixList[bestCAICidx][0], " with the score of…
229 labelMatrix = {{"Log-likelihood","Parameters","AIC","c-AIC","Total tree length",""}};
/dports/math/R-cran-maxLik/maxLik/man/
H A DmaxLik-methods.Rd1 \name{AIC.maxLik}
2 \alias{AIC.maxLik}
14 \method{AIC}{maxLik}(object, \dots, k=2)
23 \sQuote{k = 2} is the classical AIC.}
34 \item{AIC}{calculates Akaike's Information Criterion (and other
60 AIC(ml)
/dports/devel/R-cran-classInt/classInt/man/
H A DlogLik.classIntervals.Rd22 within an interval, and with the AIC, a per-interval penalty can be used to
27 selected for a set of data. The `logLik()` function (and associated `AIC()`
31 As illustrated by the examples below (the AIC comparison does not
39 AIC(x) # By having a logLik method, AIC.default is used.
51 # AIC will make selection of the optimal intervals easier.
56 AIC(x_2, x_3, x_4)
/dports/math/octave/octave-6.4.0/liboctave/external/amos/
H A Dcuoik.f29 REAL AARG, AIC, ALIM, APHI, ASCLE, AX, AY, ELIM, FNN, FNU, GNN, local
34 DATA AIC / 1.265512123484645396E+00 /
82 IF (IFORM.EQ.2) RCZ = RCZ - 0.25E0*ALOG(AARG) - AIC
92 IF (IFORM.EQ.2) RCZ = RCZ - 0.25E0*ALOG(AARG) - AIC
104 CZ = CZ - CMPLX(0.25E0,0.0E0)*CLOG(ARG) - CMPLX(AIC,0.0E0)
136 IF (IFORM.EQ.2) RCZ = RCZ - 0.25E0*ALOG(AARG) - AIC
148 CZ = CZ - CMPLX(0.25E0,0.0E0)*CLOG(ARG) - CMPLX(AIC,0.0E0)
/dports/math/slatec/src/
H A Dcuoik.f39 REAL AARG, AIC, ALIM, APHI, ASCLE, AX, AY, ELIM, FNN, FNU, GNN,
44 DATA AIC / 1.265512123484645396E+00 /
93 IF (IFORM.EQ.2) RCZ = RCZ - 0.25E0*ALOG(AARG) - AIC
103 IF (IFORM.EQ.2) RCZ = RCZ - 0.25E0*ALOG(AARG) - AIC
115 CZ = CZ - CMPLX(0.25E0,0.0E0)*CLOG(ARG) - CMPLX(AIC,0.0E0)
147 IF (IFORM.EQ.2) RCZ = RCZ - 0.25E0*ALOG(AARG) - AIC
159 CZ = CZ - CMPLX(0.25E0,0.0E0)*CLOG(ARG) - CMPLX(AIC,0.0E0)
/dports/math/xlife++/xlifepp-sources-v2.0.1-2018-05-09/ext/Amos/
H A Dcuoik.f29 REAL AARG, AIC, ALIM, APHI, ASCLE, AX, AY, ELIM, FNN, FNU, GNN,
34 DATA AIC / 1.265512123484645396E+00 /
82 IF (IFORM.EQ.2) RCZ = RCZ - 0.25E0*ALOG(AARG) - AIC
92 IF (IFORM.EQ.2) RCZ = RCZ - 0.25E0*ALOG(AARG) - AIC
104 CZ = CZ - CMPLX(0.25E0,0.0E0)*CLOG(ARG) - CMPLX(AIC,0.0E0)
136 IF (IFORM.EQ.2) RCZ = RCZ - 0.25E0*ALOG(AARG) - AIC
148 CZ = CZ - CMPLX(0.25E0,0.0E0)*CLOG(ARG) - CMPLX(AIC,0.0E0)

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