library(Hmisc,T)
library(Design,T)
attach(support[support$totcst > 0 |
 is.na(support$totcst), ])
length(age)
describe(totcst)
s <- summary(totcst ~ age + sex + dzgroup +
 race + meanbp,
 fun=function(y)c(Mean=mean(y),
                  Median=median(y)))
plot(s, which=1:2, pch=1:2)
s
options(digits=2,width=70)
s
spear <- spearman2(totcst ~
 age + sex + dzgroup + num.co +
 scoma + race + meanbp + pafi +
 alb, p=2)
plot(spear)
table(race)
plot(varclus(~age+sex+dzgroup+
 num.co+scoma+race+meanbp+
 hrt+temp+pafi+alb))
names(support)
resize()
names(support)
nac <- naclus(support[,c(1,3,7,9,12,14,
   17,18,20,22:24)])
plot(nac)
naplot(nac)
library(rpart)
f <- rpart(is.na(pafi) ~
 age + sex + dzgroup + num.co +
 scoma + race + meanbp + hrt + temp)
plot(f);text(f)
race <- impute(race)
alb <- impute(alb, 3.5)
pafi <- impute(pafi, 333.3)
table(race)
race <- combine.levels(race,minlev=.1)
table(race)
dd <- datadist(support)
dd <- datadist(dd, race)
options(datadist='dd')
find(race)
f <- ols(totcst ~ rcs(age,3)+sex+
 dzgroup+pol(num.co,2)+pol(scoma,2)+
 race + rcs(meanbp,4)+rcs(hrt,4)+
 rcs(temp,5)+rcs(alb,5)+rcs(pafi,3))
qqnorm(resid(f));qqline(resid(f))
xYplot(resid(f) ~ fitted(f),
 method='quantile')
f$stats
totcst <- ifelse(is.na(totcst),
 exp(.25501+.924418*log(charges)),
 totcst)
f_update(f)
f$stats
plot(log(charges),log(totcst))
f <- update(f,log(totcst)~.)
qqnorm(resid(f));qqline(resid(f))
xYplot(resid(f) ~ fitted(f),
 method='quantile')
g <- areg.boot(totcst ~ dzgroup+
  meanbp + temp + alb + pafi, B=20)
plot(g)
abline(h=0)
qqnorm(resid(f));qqline(resid(f))
xYplot(resid(f) ~ fitted(f),
 method='quantile')
abline(h=0)
?xYplot
xYplot(resid(f) ~ fitted(f),
 method='quantile', nx=100)
f$stats
page(f)
an <- anova(f)
page(an)
options(width=100)
page(an)
args(print.anova.Design)
print(an,'subscripts')
coef(f)
plot(an)
args(plot.anova.Design)
?anova.Design
plot(an,'partial')
plot(an,'proportion')
bwplot(dzgroup ~ resid(f))
par(mfrow=c(3,4))
plot(f, ref.zero=T,
 ylim=c(-1.5,.7))
dev.off()
plot(f, age=NA)
scat1d(age)
plot(f, age=NA, sex=NA,
 conf.int=F)
specs(f,long=T)
plot(f,meanbp=NA,age=NA)
plot(f,meanbp=NA,age=NA,
 method='image')
Legend()
p <- plot(f,meanbp=NA,age=NA,
 method='image')
Legend(p)
f <- update(f,x=T,y=T)
validate(f,B=100)
cal <- calibrate(f,B=100)
page(f)
plot(cal)
s <- summary(f,antilog=T)
plot(s, log=T)
nomogram(f, fun=
 function(x)exp(x)/1000,
 funlabel='Cost ($/1000)')
#summary(f, age=c(40,60))
nomogram(f, fun=
 function(x)exp(x)/1000,
 funlabel='Cost ($/1000)',
 cex.var=.6, cex.axis=.5)
g <- Function(f)
page(g)
page(g)
w <- latex(f,fi='/windows/temp/fit.tex')
args(g)
g()
g(age=30)
g(age=40)
g(age=c(30,40,50))
options(digits=4)
g(age=c(30,40,50))
g(age=c(30,40,50,80))
g(age=c(30,40,50,80),sex='female')
sascode(g)
names(f)
dim(f$x)
page(f$x[1:5,])
# f$x <- f$y <- NULL
# f <- update(f, x=T,y=T)
page(val)
f$stats
gamma.hat <- (677.8-31)/677.8
gamma.hat
31/983
1/15
search()
objects(1)
page(an)
options(width=90)
page(an)
formula(f)
page(an)
plot(an)
objects()
resize()
objects()
v <- Cs(pclass, survived, age, sex, sibsp, parch)
v
dd <- datadist(titanic3[,v])
options(datadist='dd')
attach(titanic3[,v])
masked()
plot(age, survived, group=interaction(pclass,
  sex), col=1:6, datadensity=T)
plsmo(age, survived, group=interaction(pclass,
  sex), col=1:6, datadensity=T)
f1 <- lrm(survived ~ sex*pclass*rcs(age,5)+
 rcs(age,5)*(sibsp + parch))
f <- lrm(survived ~ (sex + pclass + rcs(age,5))^2 +
  rcs(age,5)*sibsp)
page(f)
plot(f, age=NA, pclass=NA, sex='male', fun=plogis)
plot(f, age=NA, pclass=NA, sex='male', fun=plogis,
  conf.int=F)
plot(f, age=NA, pclass=NA, sex='female', fun=plogis,
  conf.int=F)
an <- anova(f)
page(an)
plot(an)
f$stats
(554.9 - 26)/554.9
f <- update(f, x=T, y=T)
validate(f, B=15)
find(rpart)
who.na <- rpart(is.na(age) ~ sex + pclass + survived +
 sibsp + parch, minbucket=15)
plot(who.na); text(who.na)
m <- lrm(is.na(age) ~ sex*pclass+survived+sibsp+parch)
plot(anova(m))
set.seed(17)
xtrams <- transcan(~I(age) + sex + pclass + sibsp + parch +
xtrans <- transcan(~I(age) + sex + pclass + sibsp + parch +
   survived, n.impute=5, trantab=T, imputed=T, transformed=F)
attr(xtrans,'imputed')$age[1:10,]
f.mi <- fit.mult.impute(survived ~ (sex + pclass +
 rcs(age,5))^2 + rcs(age,5)*sibsp, lrm, xtrans)
page(f.mi)
an <- anova(f.mi)
page(an)
page(f.mi)
combos <- expand.grid(age=c(2,21,50),sex=levels(sex),
 pclass=levels(pclass), sibsp=0)
dim(combos)
data.frame(combos, predict(f.mi, combos, type='fitted'))
data.frame(combos, phat=predict(f.mi, combos, type='fitted'))
?predict.lrm
pred.logit <- Function(f.mi)
plogis(pred.logit(age=c(2,21,50),sex='male',pclass='3rd'))
args(pred.logit)
nomogram(f.mi, fun=plogis, funlabel='Prob(survive)')
drep <- dataRep(~ roundN(age,10)+sex+pclass+roundN(sibsp,
 clip=0:1))
page(drep)
Dialog(fitPar('f.mi', lp=F, 
 fun=list('Prob[Survival]'=plogis)),
 limits='data', basename='Titanic',
 vary=list(sex=c('female','male')), datarep=drep)
page(CallBack.Titanic)
runmenu.Titanic()
lrm(cut2(totcst,g=2) ~ dzgroup, data=support)$stats
lrm(cut2(totcst,g=3) ~ dzgroup, data=support)$stats
lrm(cut2(totcst,g=4) ~ dzgroup, data=support)$stats
lrm(cut2(totcst,g=5) ~ dzgroup, data=support)$stats
lrm(cut2(totcst,g=6) ~ dzgroup, data=support)$stats
lrm(cut2(totcst,g=7) ~ dzgroup, data=support)$stats
lrm(cut2(totcst,g=10) ~ dzgroup, data=support)$stats
lrm(cut2(totcst,g=100) ~ dzgroup, data=support)$stats
g <- areg.boot(totcst ~ dzgroup + meanbp, B=30, data=support)
plot(g)
page(g)
page(summary(g))
h <- Function(g)
names(h)
page(h$totcst)
plot(h$totcst(support$totcst), log(support$totcst))
?summary.areg.boot
summary(g, statistic='mean')
search()
detach(3)
detach(2)
search()
objects()
rm(alb,pafi,race)
acute <- support$dzclass %in% c('ARF/MOSF','Coma')
table(acute)
ecdf(support[acute,c(1,12,14,18,19:31,35)],
 group=support$dzclass, col=c(1,.35),
 label.curves=F, subtitles=F)
ecdf(support[acute,c(1,12,14,18,19:31,35)],
 group=support$dzclass[acute], col=c(1,.35),
 label.curves=F, subtitles=F)
dd <- datadist(support[acute,])
options(datadist='dd')
attach(support[acute,])
length(age)
years <- d.time/365.25
units(years) <- 'Year'
S <- Surv(years, death)
dim(S)
S[1:10,]
class(S)
names(attributes(S))
attr(S,'type')
?Surv
help(Surv)
find(Surv)
survplot(survfit(S ~ dzgroup), conf='none')
survplot(survfit(S ~ dzgroup), conf='none',fun=qnorm,
 logt=T)
f <- psm(S ~ dzgroup + rcs(age,5) + rcs(meanbp,5),
 dist='gauss', y=T)
r <- resid(f)
survplot(r)
r[1:10]
survplot(r, dzgroup, label.curve=F)
survplot(r, age, label.curve=F)
survplot(r, meanbp, label.curve=F)
survplot(r, runif(length(age)), label.curve=F)
shortest.follow.up <- min(d.time[death==0], na.rm=T)
shortest.follow.up
d.timet <- pmin(d.time, shortest.follow.up)
ecdf(d.timet)
w <- spearman2(d.timet ~ age+num.co+scoma+meanbp+
 hrt+resp+temp+crea+sod+adlsc+wblc+pafi+ph+dzgroup+race,
 p=2)
plot(w)
args(nlme)
wblc <- impute(wblc, 9)
describe(wblc)
pafi <- impute(pafi, 333.3)
ph <- impute(ph, 7.4)
race2 <- race
levels(race2) <- list(white='white',other=levels(race)[-1])
table(race2)
race2[is.na(race2)] <- 'white'
dd <- datadist(dd, race2)
f <- psm(S ~ rcs(age,3)+sex+dzgroup+pol(num.co,2)+
 pol(scoma,2)+pol(adlsc,2)+race2+rcs(meanbp,5)+
 rcs(hrt,3)+resp+rcs(temp,3)+rcs(crea,5)+sod+
 rcs(wblc,3)+rcs(pafi,3), dist='gau')
page(f)
an <- anova(f)
page(an)
plot(an)
s <- summary(f)
page(s)
options(width=100)
page(s)
plot(s)
h <- Hazard(f)
page(h)
surv <- Survival(f)
page(surv)
w <- latex(f)
g <- update(f, x=T, y=T)
validate(g, B=10, dxy=T)
f$stats
(271.1 - 30)/271.1
cal <- calibrate(f, u=1, m=60, B=30)
cal <- calibrate(g, u=1, m=60, B=30)
Z <- predict(f)
summary(Z)
summary(exp(Z))
?update
a <- ols(Z ~ rcs(age,3)+sex+dzgroup+pol(num.co,2)+
 pol(scoma,2)+pol(adlsc,2)+race2+rcs(meanbp,5)+
 rcs(hrt,3)+resp+rcs(temp,3)+rcs(crea,5)+sod+
 rcs(wblc,3)+rcs(pafi,3), sigma=1
)
page(a)
fastbw(a, aics=10000)
f.approx <- ols(Z ~ dzgroup+rcs(meanbp,5)+
 rcs(crea,5)+pol(scoma,2)+rcs(age,3)+
 rcs(hrt,3)+rcs(pafi,3)+pol(adlsc,2)+
 pol(num.co,2), x=T)
f.approx$stats
expected.surv <- Mean(f)
page(expected.surv)
quantiles.surv <- Quantile(f)
quantile.surv
quantiles.surv
median.surv <- function(x) quantiles.surv(lp=x)
nomogram(f.approx,
 fun=list('Median Survival Time'=median.surv,
 'Mean Survival Time'=expected.surv))