/dports/math/R-cran-VGAM/VGAM/man/ |
H A D | cens.poisson.Rd | 1 \name{cens.poisson} 2 %\alias{cens.poisson} 3 \alias{cens.poisson} 12 cens.poisson(link = "loglink", imu = NULL, 63 nor is \code{type = "counting"}. 124 fit <- vglm(SurvS4(cY, status, type = "left") ~ 1, cens.poisson, 147 table(ii <- print(SurvS4(Lvec, Uvec, status, type = "interval")))) 148 fit <- vglm(SurvS4(Lvec, Uvec, status, type = "interval") ~ 1, 149 cens.poisson, data = cdata, trace = TRUE) 159 type = "interval")))) # Check [all …]
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H A D | cens.normal.Rd | 1 \name{cens.normal} 2 \alias{cens.normal} 15 cens.normal(lmu = "identitylink", lsd = "loglink", imethod = 1, zero = "sd") 49 greater than the observed value). To indicate which type of censoring, 81 % Function \code{\link{cens.normal1}} will be depreciated soon. 82 % It is exactly the same as \code{\link{cens.normal}}. 89 \code{\link{double.cens.normal}}. 106 fit1 <- vglm(y ~ x2, cens.normal, data = cdata, crit = "c", extra = Extra)
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H A D | rayleigh.Rd | 3 \alias{cens.rayleigh} 14 type.fitted = c("mean", "percentiles", "Qlink"), 16 cens.rayleigh(lscale = "loglink", oim = TRUE) 60 \item{type.fitted, percentiles}{ 80 The \pkg{VGAM} family function \code{cens.rayleigh} handles 82 value). To indicate which type of censoring, input \code{extra = 161 fit <- vglm(y ~ 1, cens.rayleigh, data = rdata, trace = TRUE,
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H A D | cens.gumbel.Rd | 1 \name{cens.gumbel} 2 \alias{cens.gumbel} 13 cens.gumbel(llocation = "identitylink", lscale = "loglink", iscale = NULL, 63 greater than the observed value). To indicate which type of censoring, 124 fam = cens.gumbel(mean = FALSE, perc = c(5, 25, 50, 75, 95))) 138 fit <- vglm(y ~ 1, trace = TRUE, extra = extra, fam = cens.gumbel)
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H A D | tobit.Rd | 12 type.fitted = c("uncensored", "censored", "mean.obs"), 15 % 20151024 yettodo: maybe add a new option to 'type.fitted': 16 % type.fitted = c("uncensored", "censored", "mean.obs", "truncated"), 62 \item{type.fitted}{ 174 The function \code{\link{cens.normal}} is an alternative 193 \code{\link{cens.normal}}, 195 \code{\link{double.cens.normal}}, 232 fit2 <- vglm(y2 ~ x2, tobit(Lower = Lower, Upper = Upper, type.f = "cens"), 239 Upper = with(tdata, Upper.vec), type.f = "cens"), 245 # fit4 is fit3 but with type.fitted = "uncen".
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H A D | SurvS4.Rd | 23 SurvS4(time, time2, event, type =, origin = 0) 57 \item{type}{ 58 character string specifying the type of censoring. Possible values 91 SurvS4(time, time2, event, type=, origin=0) 104 If \code{type = "interval2"} then the representation given 106 If `type="interval" \code{event} must be given. 138 \code{\link{cens.poisson}}). 153 If \code{type="interval"} then these should not be used in \pkg{VGAM}: 155 % zz is this for type="count" only? 162 \code{\link{cens.poisson}},
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/dports/math/R-cran-ipred/ipred/tests/ |
H A D | ipred-segfault.R | 38 predict(mod, newdata=test[1:10,], type="prob") 39 predict(mod, newdata=test[1:10,], type="prob", agg="aver") 40 predict(mod, newdata=test[1:10,], type="prob", agg="wei") 86 mod <- bagging(Surv(time, cens) ~ ., data=learn, nbagg=10) 95 errorest(Surv(time, cens) ~ ., data=learn, model=bagging, 97 errorest(Surv(time, cens) ~ ., data=learn, model=bagging, 104 lapply(errorest(Surv(time, cens) ~ ., data=learn, model=bagging, 125 mods <- bagging(Surv(time,cens) ~ ., data=GBSG2, nbagg=10,
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/dports/math/R-cran-ipred/ipred/R/ |
H A D | sbrier.R | 55 cens <- obj[ot,2] functionVar 106 hatcdist <- prodlim(Surv(time, cens) ~ 1,reverse = TRUE) 107 csurv <- predict(hatcdist, times = time, type = "surv") 114 csurv_btime <- predict(hatcdist, times = btime, type = "surv") 124 help1 <- as.integer(time <= btime[j] & cens == 1) 145 help1 <- as.integer(time <= btime & cens == 1) 147 cs <- predict(hatcdist, times=btime, type = "surv")
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/dports/devel/R-cran-Hmisc/Hmisc/man/ |
H A D | rcorr.cens.Rd | 1 \name{rcorr.cens} 2 \alias{rcorr.cens} 16 \code{rcorr.cens} handles one predictor variable. \code{rcorrcens} 27 rcorr.cens(x, S, outx=FALSE) 43 relevant pair. This results in a Goodman--Kruskal gamma type rank 106 rcorr.cens(x, y, outx=TRUE) # can correlate non-censored variables 112 cens <- runif(400,.5,2) 113 death <- d.time <= cens 114 d.time <- pmin(d.time, cens) 115 rcorr.cens(age, Surv(d.time, death)) [all …]
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H A D | event.convert.Rd | 30 \code{event.convert} function converts this type of data into 32 type, suitable for the \code{event.chart} function. 62 cens.ind <- c(1,0,1,1,0) 63 surv.data <- cbind(surv.time,cens.ind)
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/dports/devel/R-cran-Hmisc/Hmisc/R/ |
H A D | event.history.s | 15 cens.density = 10, mult.end.cens = 1.05, 16 cens.mark.right = FALSE, cens.mark = '-', 17 cens.mark.ahead = .5, cens.mark.cutoff = -1e-8, cens.mark.cex = 1.0, 72 cens.consec.vec <- rep(NA, dim(data)[1]) 77 cens.consec.vec[i] <- 0 81 cens.consec.vec[i] <- cnt - 1 101 if((n - cens.consec.vec[i]) > i) { 149 plot(x=c(0, max(survtime.col, na.rm=TRUE) * mult.end.cens), y=c(0,1), type='n', 157 cens.cnt <- 0 241 if(cens.mark.right & temp.prob.plot >= cens.mark.cutoff) [all …]
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/dports/science/R-cran-Epi/Epi/R/ |
H A D | simLexis.R | 79 type="response", 84 type="expected", 134 function( obj, tS, cens ) argument 142 cens = zz+cens ) ) nameattr 144 ww <- ww[ww[,tS[1]] < ww$cens,] 316 matplot( as.numeric(rownames(x)), x, type="n",
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/dports/science/apbs/apbs-pdb2pqr-apbs-1.5-102-g500c1473/apbs/externals/pb_s_am/pb_shared/src/ |
H A D | BaseSys.cpp | 13 BaseMolecule::BaseMolecule(int type, int type_idx, string movetype, in BaseMolecule() argument 18 qs_(qs), pos_(pos), vdwr_(vdwr), type_(type), typeIdx_(type_idx), in BaseMolecule() 24 BaseMolecule::BaseMolecule(int type, int type_idx, string movetype, in BaseMolecule() argument 26 vector<double> vdwr, vector<Pt> cens, in BaseMolecule() argument 28 :BaseMolecule(type, type_idx, movetype, qs, pos, vdwr, drot, dtrans) in BaseMolecule() 30 centers_ = cens; in BaseMolecule() 37 BaseMolecule::BaseMolecule(int type, int type_idx, string movetype, in BaseMolecule() argument 41 :BaseMolecule(type, type_idx, movetype, qs, pos, vdwr, drot, dtrans) in BaseMolecule() 48 void BaseMolecule::set_Dtr_Drot(string type) in set_Dtr_Drot() argument 50 if ((type == "stat") or (type == "rot")) dtrans_ = 0.0; in set_Dtr_Drot() [all …]
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H A D | BaseSys.h | 41 void set_Dtr_Drot(string type); 48 BaseMolecule(int type, int type_idx, string movetype, vector<double> qs, 53 BaseMolecule(int type, int type_idx, string movetype, vector<double> qs, 54 vector<Pt> pos, vector<double> vdwr, vector<Pt> cens, 58 BaseMolecule(int type, int type_idx, string movetype, vector<double> qs, 170 const int get_mol_global_idx(int type, int ty_idx) in get_mol_global_idx() argument 172 vector<int> keys = {type, ty_idx}; in get_mol_global_idx()
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/dports/math/R-cran-ipred/ipred/man/ |
H A D | rsurv.Rd | 13 \item{model}{ type of model. } 28 A data frame with elements \code{time}, \code{cens}, \code{X1} ... 52 coxph(Surv(time, cens) ~ ., data=simdat)
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/dports/science/apbs/apbs-pdb2pqr-apbs-1.5-102-g500c1473/apbs/externals/pb_s_am/pbsam/src/ |
H A D | SystemSAM.cpp | 12 MoleculeSAM::MoleculeSAM(int type, int type_idx, string movetype, vector<double> qs, in MoleculeSAM() argument 13 vector<Pt> pos, vector<double> vdwr, vector<Pt> cens, in MoleculeSAM() argument 15 :BaseMolecule(type, type_idx, movetype, qs, pos, vdwr, cens, as, drot, dtrans), in MoleculeSAM() 16 cgCharges_((int) cens.size()), in MoleculeSAM() 17 cgGridPts_((int) cens.size()), in MoleculeSAM() 18 cgGdPtExp_((int) cens.size()), in MoleculeSAM() 19 cgGdPtBur_((int) cens.size()) in MoleculeSAM() 28 MoleculeSAM::MoleculeSAM(int type, int type_idx, string movetype, vector<double> qs, in MoleculeSAM() argument 33 :BaseMolecule(type, type_idx, movetype, qs, pos, vdwr, drot, dtrans) in MoleculeSAM() 44 MoleculeSAM::MoleculeSAM(int type, int type_idx, string movetype, vector<double> qs, in MoleculeSAM() argument [all …]
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H A D | SystemSAM.h | 129 MoleculeSAM(int type, int type_idx, string movetype, vector<double> qs, 130 vector<Pt> pos, vector<double> vdwr, vector<Pt> cens, 133 MoleculeSAM(int type, int type_idx, string movetype, vector<double> qs, 139 MoleculeSAM(int type, int type_idx, string movetype, vector<double> qs, 226 const int get_mol_global_idx(int type, int ty_idx) in get_mol_global_idx() argument 228 vector<int> keys = {type, ty_idx}; in get_mol_global_idx()
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/dports/games/wolfpack/empire-4.4.1/tests/build/units/ |
H A D | 03-lands-3 | 2 | invalid type 34 cens * ?des=!
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H A D | 01-ships-1 | 5 | invalid type 38 cens * ?des=h
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H A D | 02-planes-2 | 2 | invalid type 37 cens * ?des=*
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H A D | 04-nukes-4 | 2 | invalid type 50 cens * ?des=n
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/dports/science/clipper/clipper-2.1/clipper/core/ |
H A D | spacegroup.cpp | 378 Symop_codes cens = ops.centering_ops(); in generator_ops() local 384 for ( int i = 1; i < cens.size(); i++ ) { in generator_ops() 386 if ( cens[i] == gend[j] ) goto skip1; in generator_ops() 387 gens.push_back( cens[i] ); in generator_ops() 492 if ( type == Unknown ) { in Spgr_descr() 494 type = Hall; in Spgr_descr() 496 type = XHM; in Spgr_descr() 498 type = Symops; in Spgr_descr() 500 type = Number; in Spgr_descr() 508 if ( type == Symops ) { in Spgr_descr() [all …]
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/dports/science/R-cran-etm/etm/man/ |
H A D | etm.Rd | 12 \S3method{etm}{data.frame}(data, state.names, tra, cens.name, s, t = "last", 33 \item{cens.name}{ A character giving the code for censored 76 matrix is an estimator of the Greenwood type. 104 \item{cens.name}{How the censored observation are coded in the data 179 tr.prob <- etm(sir.cont, c("0", "1", "2"), tra, "cens", 1)
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H A D | plot.etm.Rd | 24 \item{lty}{Vector of line type. Default is 1:number of transitions} 37 \item{ci.lty}{Line type of the confidence intervals. Default is 3.} 43 \item{legend.bty}{Box type for the legend} 79 my.etm <- etm(sir.cont,c("0","1","2"),tra,"cens", s = 0)
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/dports/math/R-cran-prodlim/prodlim/man/ |
H A D | prodlim.Rd | 13 exact = TRUE, type) 40 plain Wald-type confidence limits are available.} 81 \item{type}{In two state models either \code{"surv"} for the Kaplan-Meier estimate of the survival 106 \code{Hist(time,status,cens.code="4")}. 173 sfit <- survfit(Surv(time,status)~1,data=dat,conf.type="plain") 183 rsfit <- survfit(Surv(time,1-status)~1,data=rdat,conf.type="plain") 223 # Changing the cens.code: 227 fit <- prodlim(Hist(time,status,cens.code="2")~1,data=dat)
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