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/dports/math/R-cran-VGAM/VGAM/man/
H A Dcens.poisson.Rd1 \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 …]
H A Dcens.normal.Rd1 \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)
H A Drayleigh.Rd3 \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,
H A Dcens.gumbel.Rd1 \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)
H A Dtobit.Rd12 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".
H A DSurvS4.Rd23 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}},
/dports/math/R-cran-ipred/ipred/tests/
H A Dipred-segfault.R38 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,
/dports/math/R-cran-ipred/ipred/R/
H A Dsbrier.R55 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")
/dports/devel/R-cran-Hmisc/Hmisc/man/
H A Drcorr.cens.Rd1 \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 …]
H A Devent.convert.Rd30 \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)
/dports/devel/R-cran-Hmisc/Hmisc/R/
H A Devent.history.s15 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 …]
/dports/science/R-cran-Epi/Epi/R/
H A DsimLexis.R79 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",
/dports/science/apbs/apbs-pdb2pqr-apbs-1.5-102-g500c1473/apbs/externals/pb_s_am/pb_shared/src/
H A DBaseSys.cpp13 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 …]
H A DBaseSys.h41 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()
/dports/math/R-cran-ipred/ipred/man/
H A Drsurv.Rd13 \item{model}{ type of model. }
28 A data frame with elements \code{time}, \code{cens}, \code{X1} ...
52 coxph(Surv(time, cens) ~ ., data=simdat)
/dports/science/apbs/apbs-pdb2pqr-apbs-1.5-102-g500c1473/apbs/externals/pb_s_am/pbsam/src/
H A DSystemSAM.cpp12 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 …]
H A DSystemSAM.h129 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()
/dports/games/wolfpack/empire-4.4.1/tests/build/units/
H A D03-lands-32 | invalid type
34 cens * ?des=!
H A D01-ships-15 | invalid type
38 cens * ?des=h
H A D02-planes-22 | invalid type
37 cens * ?des=*
H A D04-nukes-42 | invalid type
50 cens * ?des=n
/dports/science/clipper/clipper-2.1/clipper/core/
H A Dspacegroup.cpp378 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 …]
/dports/science/R-cran-etm/etm/man/
H A Detm.Rd12 \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)
H A Dplot.etm.Rd24 \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)
/dports/math/R-cran-prodlim/prodlim/man/
H A Dprodlim.Rd13 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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