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MASS-ex.R
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MASS-ex.R
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library(MASS);set.seed(1)
### Name: Insurance
### Title: Numbers of Car Insurance claims
### Aliases: Insurance
### Keywords: datasets
### ** Examples
## main-effects fit as Poisson GLM with offset
glm(Claims ~ District + Group + Age + offset(log(Holders)),
data = Insurance, family = poisson)
# same via loglm
loglm(Claims ~ District + Group + Age + offset(log(Holders)),
data = Insurance)
### Name: Null
### Title: Null Spaces of Matrices
### Aliases: Null
### Keywords: algebra
### ** Examples
# The function is currently defined as
function(M)
{
tmp <- qr(M)
set <- if(tmp$rank == 0) 1:ncol(M) else - (1:tmp$rank)
qr.Q(tmp, complete = TRUE)[, set, drop = FALSE]
}
### Name: OME
### Title: Tests of Auditory Perception in Children with OME
### Aliases: OME
### Keywords: datasets
### ** Examples
# Fit logistic curve from p = 0.5 to p = 1.0
fp1 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/scal)),
c("L75", "scal"),
function(x,L75,scal)NULL)
nls(Correct/Trials ~ fp1(Loud, L75, scal), data = OME,
start = c(L75=45, scal=3))
nls(Correct/Trials ~ fp1(Loud, L75, scal),
data = OME[OME$Noise == "coherent",],
start=c(L75=45, scal=3))
nls(Correct/Trials ~ fp1(Loud, L75, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, scal=3))
# individual fits for each experiment
aa <- factor(OME$Age)
ab <- 10*OME$ID + unclass(aa)
ac <- unclass(factor(ab))
OME$UID <- as.vector(ac)
OME$UIDn <- OME$UID + 0.1*(OME$Noise == "incoherent")
rm(aa, ab, ac)
OMEi <- OME
library(nlme)
fp2 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/2)),
"L75", function(x,L75) NULL)
dec <- getOption("OutDec")
options(show.error.messages = FALSE, OutDec=".")
OMEi.nls <- nlsList(Correct/Trials ~ fp2(Loud, L75) | UIDn,
data = OMEi, start = list(L75=45), control = list(maxiter=100))
options(show.error.messages = TRUE, OutDec=dec)
tmp <- sapply(OMEi.nls, function(X)
{if(is.null(X)) NA else as.vector(coef(X))})
OMEif <- data.frame(UID = round(as.numeric((names(tmp)))),
Noise = rep(c("coherent", "incoherent"), 110),
L75 = as.vector(tmp), stringsAsFactors = TRUE)
OMEif$Age <- OME$Age[match(OMEif$UID, OME$UID)]
OMEif$OME <- OME$OME[match(OMEif$UID, OME$UID)]
OMEif <- OMEif[OMEif$L75 > 30,]
summary(lm(L75 ~ Noise/Age, data = OMEif, na.action = na.omit))
summary(lm(L75 ~ Noise/(Age + OME), data = OMEif,
subset = (Age >= 30 & Age <= 60),
na.action = na.omit), cor = FALSE)
# Or fit by weighted least squares
fpl75 <- deriv(~ sqrt(n)*(r/n - 0.5 - 0.5/(1 + exp(-(x-L75)/scal))),
c("L75", "scal"),
function(r,n,x,L75,scal) NULL)
nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal),
data = OME[OME$Noise == "coherent",],
start = c(L75=45, scal=3))
nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, scal=3))
# Test to see if the curves shift with age
fpl75age <- deriv(~sqrt(n)*(r/n - 0.5 - 0.5/(1 +
exp(-(x-L75-slope*age)/scal))),
c("L75", "slope", "scal"),
function(r,n,x,age,L75,slope,scal) NULL)
OME.nls1 <-
nls(0 ~ fpl75age(Correct, Trials, Loud, Age, L75, slope, scal),
data = OME[OME$Noise == "coherent",],
start = c(L75=45, slope=0, scal=2))
sqrt(diag(vcov(OME.nls1)))
OME.nls2 <-
nls(0 ~ fpl75age(Correct, Trials, Loud, Age, L75, slope, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, slope=0, scal=2))
sqrt(diag(vcov(OME.nls2)))
# Now allow random effects by using NLME
OMEf <- OME[rep(1:nrow(OME), OME$Trials),]
attach(OME)
OMEf$Resp <- rep(rep(c(1,0), length(Trials)),
t(cbind(Correct, Trials-Correct)))
OMEf <- OMEf[, -match(c("Correct", "Trials"), names(OMEf))]
detach("OME")
## Not run: ## this fails in R on some platforms
##D fp2 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/exp(lsc))),
##D c("L75", "lsc"),
##D function(x, L75, lsc) NULL)
##D G1.nlme <- nlme(Resp ~ fp2(Loud, L75, lsc),
##D fixed = list(L75 ~ Age, lsc ~ 1),
##D random = L75 + lsc ~ 1 | UID,
##D data = OMEf[OMEf$Noise == "coherent",], method = "ML",
##D start = list(fixed=c(L75=c(48.7, -0.03), lsc=0.24)), verbose = TRUE)
##D summary(G1.nlme)
##D
##D G2.nlme <- nlme(Resp ~ fp2(Loud, L75, lsc),
##D fixed = list(L75 ~ Age, lsc ~ 1),
##D random = L75 + lsc ~ 1 | UID,
##D data = OMEf[OMEf$Noise == "incoherent",], method="ML",
##D start = list(fixed=c(L75=c(41.5, -0.1), lsc=0)), verbose = TRUE)
##D summary(G2.nlme)
## End(Not run)
### Name: Skye
### Title: AFM Compositions of Aphyric Skye Lavas
### Aliases: Skye
### Keywords: datasets
### ** Examples
# ternary() is from the on-line answers.
ternary <- function(X, pch = par("pch"), lcex = 1,
add = FALSE, ord = 1:3, ...)
{
X <- as.matrix(X)
if(any(X < 0)) stop("X must be non-negative")
s <- drop(X %*% rep(1, ncol(X)))
if(any(s<=0)) stop("each row of X must have a positive sum")
if(max(abs(s-1)) > 1e-6) {
warning("row(s) of X will be rescaled")
X <- X / s
}
X <- X[, ord]
s3 <- sqrt(1/3)
if(!add)
{
oldpty <- par("pty")
on.exit(par(pty=oldpty))
par(pty="s")
plot(c(-s3, s3), c(0.5-s3, 0.5+s3), type="n", axes=FALSE,
xlab="", ylab="")
polygon(c(0, -s3, s3), c(1, 0, 0), density=0)
lab <- NULL
if(!is.null(dn <- dimnames(X))) lab <- dn[[2]]
if(length(lab) < 3) lab <- as.character(1:3)
eps <- 0.05 * lcex
text(c(0, s3+eps*0.7, -s3-eps*0.7),
c(1+eps, -0.1*eps, -0.1*eps), lab, cex=lcex)
}
points((X[,2] - X[,3])*s3, X[,1], ...)
}
ternary(Skye/100, ord=c(1,3,2))
### Name: addterm
### Title: Try All One-Term Additions to a Model
### Aliases: addterm addterm.default addterm.glm addterm.lm
### Keywords: models
### ** Examples
quine.hi <- aov(log(Days + 2.5) ~ .^4, quine)
quine.lo <- aov(log(Days+2.5) ~ 1, quine)
addterm(quine.lo, quine.hi, test="F")
house.glm0 <- glm(Freq ~ Infl*Type*Cont + Sat, family=poisson,
data=housing)
addterm(house.glm0, ~. + Sat:(Infl+Type+Cont), test="Chisq")
house.glm1 <- update(house.glm0, . ~ . + Sat*(Infl+Type+Cont))
addterm(house.glm1, ~. + Sat:(Infl+Type+Cont)^2, test = "Chisq")
### Name: anova.negbin
### Title: Likelihood Ratio Tests for Negative Binomial GLMs
### Aliases: anova.negbin
### Keywords: regression
### ** Examples
m1 <- glm.nb(Days ~ Eth*Age*Lrn*Sex, quine, link = log)
m2 <- update(m1, . ~ . - Eth:Age:Lrn:Sex)
anova(m2, m1)
anova(m2)
### Name: area
### Title: Adaptive Numerical Integration
### Aliases: area
### Keywords: nonlinear
### ** Examples
area(sin, 0, pi) # integrate the sin function from 0 to pi.
### Name: bacteria
### Title: Presence of Bacteria after Drug Treatments
### Aliases: bacteria
### Keywords: datasets
### ** Examples
contrasts(bacteria$trt) <- structure(contr.sdif(3),
dimnames = list(NULL, c("drug", "encourage")))
## fixed effects analyses
summary(glm(y ~ trt * week, binomial, data = bacteria))
summary(glm(y ~ trt + week, binomial, data = bacteria))
summary(glm(y ~ trt + I(week > 2), binomial, data = bacteria))
# conditional random-effects analysis
library(survival)
bacteria$Time <- rep(1, nrow(bacteria))
coxph(Surv(Time, unclass(y)) ~ week + strata(ID),
data = bacteria, method = "exact")
coxph(Surv(Time, unclass(y)) ~ factor(week) + strata(ID),
data = bacteria, method = "exact")
coxph(Surv(Time, unclass(y)) ~ I(week > 2) + strata(ID),
data = bacteria, method = "exact")
# PQL glmm analysis
library(nlme)
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
### Name: bandwidth.nrd
### Title: Bandwidth for density() via Normal Reference Distribution
### Aliases: bandwidth.nrd
### Keywords: dplot
### ** Examples
# The function is currently defined as
function(x)
{
r <- quantile(x, c(0.25, 0.75))
h <- (r[2] - r[1])/1.34
4 * 1.06 * min(sqrt(var(x)), h) * length(x)^(-1/5)
}
### Name: bcv
### Title: Biased Cross-Validation for Bandwidth Selection
### Aliases: bcv
### Keywords: dplot
### ** Examples
bcv(geyser$duration)
### Name: beav1
### Title: Body Temperature Series of Beaver 1
### Aliases: beav1
### Keywords: datasets
### ** Examples
attach(beav1)
beav1$hours <- 24*(day-346) + trunc(time/100) + (time%%100)/60
plot(beav1$hours, beav1$temp, type="l", xlab="time",
ylab="temperature", main="Beaver 1")
usr <- par("usr"); usr[3:4] <- c(-0.2, 8); par(usr=usr)
lines(beav1$hours, beav1$activ, type="s", lty=2)
temp <- ts(c(beav1$temp[1:82], NA, beav1$temp[83:114]),
start = 9.5, frequency = 6)
activ <- ts(c(beav1$activ[1:82], NA, beav1$activ[83:114]),
start = 9.5, frequency = 6)
acf(temp[1:53])
acf(temp[1:53], type = "partial")
ar(temp[1:53])
act <- c(rep(0, 10), activ)
X <- cbind(1, act = act[11:125], act1 = act[10:124],
act2 = act[9:123], act3 = act[8:122])
alpha <- 0.80
stemp <- as.vector(temp - alpha*lag(temp, -1))
sX <- X[-1, ] - alpha * X[-115,]
beav1.ls <- lm(stemp ~ -1 + sX, na.action = na.omit)
summary(beav1.ls, cor = FALSE)
detach("beav1"); rm(temp, activ)
### Name: beav2
### Title: Body Temperature Series of Beaver 2
### Aliases: beav2
### Keywords: datasets
### ** Examples
attach(beav2)
beav2$hours <- 24*(day-307) + trunc(time/100) + (time%%100)/60
plot(beav2$hours, beav2$temp, type = "l", xlab = "time",
ylab = "temperature", main = "Beaver 2")
usr <- par("usr"); usr[3:4] <- c(-0.2, 8); par(usr = usr)
lines(beav2$hours, beav2$activ, type = "s", lty = 2)
temp <- ts(temp, start = 8+2/3, frequency = 6)
activ <- ts(activ, start = 8+2/3, frequency = 6)
acf(temp[activ == 0]); acf(temp[activ == 1]) # also look at PACFs
ar(temp[activ == 0]); ar(temp[activ == 1])
arima(temp, order = c(1,0,0), xreg = activ)
dreg <- cbind(sin = sin(2*pi*beav2$hours/24), cos = cos(2*pi*beav2$hours/24))
arima(temp, order = c(1,0,0), xreg = cbind(active=activ, dreg))
library(nlme)
beav2.gls <- gls(temp ~ activ, data = beav2, corr = corAR1(0.8),
method = "ML")
summary(beav2.gls)
summary(update(beav2.gls, subset = 6:100))
detach("beav2"); rm(temp, activ)
### Name: birthwt
### Title: Risk Factors Associated with Low Infant Birth Weight
### Aliases: birthwt
### Keywords: datasets
### ** Examples
attach(birthwt)
race <- factor(race, labels = c("white", "black", "other"))
ptd <- factor(ptl > 0)
ftv <- factor(ftv)
levels(ftv)[-(1:2)] <- "2+"
bwt <- data.frame(low = factor(low), age, lwt, race,
smoke = (smoke > 0), ptd, ht = (ht > 0), ui = (ui > 0), ftv)
detach("birthwt")
options(contrasts = c("contr.treatment", "contr.poly"))
glm(low ~ ., binomial, bwt)
### Name: boxcox
### Title: Box-Cox Transformations for Linear Models
### Aliases: boxcox boxcox.default boxcox.formula boxcox.lm
### Keywords: regression models hplot
### ** Examples
data(trees)
boxcox(Volume ~ log(Height) + log(Girth), data = trees,
lambda = seq(-0.25, 0.25, length = 10))
boxcox(Days+1 ~ Eth*Sex*Age*Lrn, data = quine,
lambda = seq(-0.05, 0.45, len = 20))
### Name: caith
### Title: Colours of Eyes and Hair of People in Caithness
### Aliases: caith
### Keywords: datasets
### ** Examples
corresp(caith)
dimnames(caith)[[2]] <- c("F", "R", "M", "D", "B")
par(mfcol=c(1,3))
plot(corresp(caith, nf=2)); title("symmetric")
plot(corresp(caith, nf=2), type="rows"); title("rows")
plot(corresp(caith, nf=2), type="col"); title("columns")
par(mfrow=c(1,1))
### Name: cement
### Title: Heat Evolved by Setting Cements
### Aliases: cement
### Keywords: datasets
### ** Examples
lm(y ~ x1 + x2 + x3 + x4, cement)
### Name: confint-MASS
### Title: Confidence Intervals for Model Parameters
### Aliases: confint.glm confint.nls confint.profile.glm
### confint.profile.nls profile.glm
### Keywords: models
### ** Examples
expn1 <- deriv(y ~ b0 + b1 * 2^(-x/th), c("b0", "b1", "th"),
function(b0, b1, th, x) {})
wtloss.gr <- nls(Weight ~ expn1(b0, b1, th, Days),
data = wtloss, start = c(b0=90, b1=95, th=120))
expn2 <- deriv(~b0 + b1*((w0 - b0)/b1)^(x/d0),
c("b0","b1","d0"), function(b0, b1, d0, x, w0) {})
wtloss.init <- function(obj, w0) {
p <- coef(obj)
d0 <- - log((w0 - p["b0"])/p["b1"])/log(2) * p["th"]
c(p[c("b0", "b1")], d0 = as.vector(d0))
}
out <- NULL
w0s <- c(110, 100, 90)
for(w0 in w0s) {
fm <- nls(Weight ~ expn2(b0, b1, d0, Days, w0),
wtloss, start = wtloss.init(wtloss.gr, w0))
out <- rbind(out, c(coef(fm)["d0"], confint(fm, "d0")))
}
dimnames(out) <- list(paste(w0s, "kg:"), c("d0", "low", "high"))
out
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20 - numdead)
budworm.lg0 <- glm(SF ~ sex + ldose - 1, family = binomial)
confint(budworm.lg0)
confint(budworm.lg0, "ldose")
### Name: contr.sdif
### Title: Successive Differences contrast coding
### Aliases: contr.sdif
### Keywords: models
### ** Examples
contr.sdif(6)
### Name: corresp
### Title: Simple Correspondence Analysis
### Aliases: corresp corresp.xtabs corresp.data.frame corresp.default
### corresp.factor corresp.formula corresp.matrix
### Keywords: category multivariate
### ** Examples
(ct <- corresp(~ Age + Eth, data = quine))
## Not run: plot(ct)
corresp(caith)
biplot(corresp(caith, nf = 2))
### Name: cov.rob
### Title: Resistant Estimation of Multivariate Location and Scatter
### Aliases: cov.rob cov.mve cov.mcd
### Keywords: robust multivariate
### ** Examples
data(stackloss)
set.seed(123)
cov.rob(stackloss)
cov.rob(stack.x, method = "mcd", nsamp = "exact")
### Name: cov.trob
### Title: Covariance Estimation for Multivariate t Distribution
### Aliases: cov.trob
### Keywords: multivariate
### ** Examples
data(stackloss)
cov.trob(stackloss)
### Name: denumerate
### Title: Transform an Allowable Formula for 'loglm' into one for 'terms'
### Aliases: denumerate denumerate.formula
### Keywords: models
### ** Examples
denumerate(~(1+2+3)^3 + a/b)
## Not run: ~ (.v1 + .v2 + .v3)^3 + a/b
### Name: dose.p
### Title: Predict Doses for Binomial Assay model
### Aliases: dose.p print.glm.dose
### Keywords: regression models
### ** Examples
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20 - numdead)
budworm.lg0 <- glm(SF ~ sex + ldose - 1, family = binomial)
dose.p(budworm.lg0, cf = c(1,3), p = 1:3/4)
dose.p(update(budworm.lg0, family = binomial(link=probit)),
cf = c(1,3), p = 1:3/4)
### Name: dropterm
### Title: Try All One-Term Deletions from a Model
### Aliases: dropterm dropterm.default dropterm.glm dropterm.lm
### Keywords: models
### ** Examples
quine.hi <- aov(log(Days + 2.5) ~ .^4, quine)
quine.nxt <- update(quine.hi, . ~ . - Eth:Sex:Age:Lrn)
dropterm(quine.nxt, test= "F")
quine.stp <- stepAIC(quine.nxt,
scope = list(upper = ~Eth*Sex*Age*Lrn, lower = ~1),
trace = FALSE)
dropterm(quine.stp, test = "F")
quine.3 <- update(quine.stp, . ~ . - Eth:Age:Lrn)
dropterm(quine.3, test = "F")
quine.4 <- update(quine.3, . ~ . - Eth:Age)
dropterm(quine.4, test = "F")
quine.5 <- update(quine.4, . ~ . - Age:Lrn)
dropterm(quine.5, test = "F")
house.glm0 <- glm(Freq ~ Infl*Type*Cont + Sat, family=poisson,
data = housing)
house.glm1 <- update(house.glm0, . ~ . + Sat*(Infl+Type+Cont))
dropterm(house.glm1, test = "Chisq")
### Name: eagles
### Title: Foraging Ecology of Bald Eagles
### Aliases: eagles
### Keywords: datasets
### ** Examples
eagles.glm <- glm(cbind(y, n - y) ~ P*A + V, data = eagles,
family = binomial)
dropterm(eagles.glm)
prof <- profile(eagles.glm)
plot(prof)
pairs(prof)
### Name: epil
### Title: Seizure Counts for Epileptics
### Aliases: epil
### Keywords: datasets
### ** Examples
summary(glm(y ~ lbase*trt + lage + V4, family = poisson,
data = epil), cor = FALSE)
epil2 <- epil[epil$period == 1, ]
epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]
epil["time"] <- 1; epil2["time"] <- 4
epil2 <- rbind(epil, epil2)
epil2$pred <- unclass(epil2$trt) * (epil2$period > 0)
epil2$subject <- factor(epil2$subject)
epil3 <- aggregate(epil2, list(epil2$subject, epil2$period > 0),
function(x) if(is.numeric(x)) sum(x) else x[1])
epil3$pred <- factor(epil3$pred,
labels = c("base", "placebo", "drug"))
contrasts(epil3$pred) <- structure(contr.sdif(3),
dimnames = list(NULL, c("placebo-base", "drug-placebo")))
summary(glm(y ~ pred + factor(subject) + offset(log(time)),
family = poisson, data = epil3), cor = FALSE)
summary(glmmPQL(y ~ lbase*trt + lage + V4,
random = ~ 1 | subject,
family = poisson, data = epil))
summary(glmmPQL(y ~ pred, random = ~1 | subject,
family = poisson, data = epil3))
### Name: farms
### Title: Ecological Factors in Farm Management
### Aliases: farms
### Keywords: datasets
### ** Examples
farms.mca <- mca(farms, abbrev = TRUE) # Use levels as names
eqscplot(farms.mca$cs, type = "n")
text(farms.mca$rs, cex = 0.7)
text(farms.mca$cs, labels = dimnames(farms.mca$cs)[[1]], cex = 0.7)
### Name: fitdistr
### Title: Maximum-likelihood Fitting of Univariate Distributions
### Aliases: fitdistr print.fitdistr coef.fitdistr logLik.fitdistr
### Keywords: distribution htest
### ** Examples
set.seed(123)
x <- rgamma(100, shape = 5, rate = 0.1)
fitdistr(x, "gamma")
## now do this directly with more control.
fitdistr(x, dgamma, list(shape = 1, rate = 0.1), lower = 0.01)
set.seed(123)
x2 <- rt(250, df = 9)
fitdistr(x2, "t", df = 9)
## allow df to vary: not a very good idea!
fitdistr(x2, "t")
## now do fixed-df fit directly with more control.
mydt <- function(x, m, s, df) dt((x-m)/s, df)/s
fitdistr(x2, mydt, list(m = 0, s = 1), df = 9, lower = c(-Inf, 0))
set.seed(123)
x3 <- rweibull(100, shape = 4, scale = 100)
fitdistr(x3, "weibull")
set.seed(123)
x4 <- rnegbin(500, mu = 5, theta = 4)
fitdistr(x4, "Negative Binomial") # R only
### Name: fractions
### Title: Rational Approximation
### Aliases: fractions Math.fractions Ops.fractions Summary.fractions
### [.fractions [<-.fractions as.character.fractions as.fractions
### is.fractions print.fractions t.fractions
### Keywords: math
### ** Examples
X <- matrix(runif(25), 5, 5)
solve(X, X/5)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2.0000e-01 3.7199e-17 1.2214e-16 5.7887e-17 -8.7841e-17
## [2,] -1.1473e-16 2.0000e-01 7.0955e-17 2.0300e-17 -1.0566e-16
## [3,] 2.7975e-16 1.3653e-17 2.0000e-01 -1.3397e-16 1.5577e-16
## [4,] -2.9196e-16 2.0412e-17 1.5618e-16 2.0000e-01 -2.1921e-16
## [5,] -3.6476e-17 -3.6430e-17 3.6432e-17 4.7690e-17 2.0000e-01
fractions(solve(X, X/5))
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1/5 0 0 0 0
## [2,] 0 1/5 0 0 0
## [3,] 0 0 1/5 0 0
## [4,] 0 0 0 1/5 0
## [5,] 0 0 0 0 1/5
fractions(solve(X, X/5)) + 1
## [,1] [,2] [,3] [,4] [,5]
## [1,] 6/5 1 1 1 1
## [2,] 1 6/5 1 1 1
## [3,] 1 1 6/5 1 1
## [4,] 1 1 1 6/5 1
## [5,] 1 1 1 1 6/5
### Name: galaxies
### Title: Velocities for 82 Galaxies
### Aliases: galaxies
### Keywords: datasets
### ** Examples
gal <- galaxies/1000
c(width.SJ(gal, method = "dpi"), width.SJ(gal))
plot(x = c(0, 40), y = c(0, 0.3), type = "n", bty = "l",
xlab = "velocity of galaxy (1000km/s)", ylab = "density")
rug(gal)
lines(density(gal, width = 3.25, n = 200), lty = 1)
lines(density(gal, width = 2.56, n = 200), lty = 3)
### Name: gamma.shape
### Title: Estimate the Shape Parameter of the Gamma Distribution in a GLM
### Fit
### Aliases: gamma.shape gamma.shape.glm print.gamma.shape
### Keywords: models
### ** Examples
clotting <- data.frame(
u = c(5,10,15,20,30,40,60,80,100),
lot1 = c(118,58,42,35,27,25,21,19,18),
lot2 = c(69,35,26,21,18,16,13,12,12))
clot1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma)
gamma.shape(clot1)
## Not run:
##D Alpha: 538.13
##D SE: 253.60
## End(Not run)
gm <- glm(Days + 0.1 ~ Age*Eth*Sex*Lrn,
quasi(link=log, variance="mu^2"), quine,
start = c(3, rep(0,31)))
gamma.shape(gm, verbose = TRUE)
## Not run:
##D Initial estimate: 1.0603
##D Iter. 1 Alpha: 1.23840774338543
##D Iter. 2 Alpha: 1.27699745778205
##D Iter. 3 Alpha: 1.27834332265501
##D Iter. 4 Alpha: 1.27834485787226
##D
##D Alpha: 1.27834
##D SE: 0.13452
## End(Not run)
summary(gm, dispersion = gamma.dispersion(gm)) # better summary
### Name: gehan
### Title: Remission Times of Leukaemia Patients
### Aliases: gehan
### Keywords: datasets
### ** Examples
library(survival)
gehan.surv <- survfit(Surv(time, cens) ~ treat, data = gehan,
conf.type = "log-log")
summary(gehan.surv)
survreg(Surv(time, cens) ~ factor(pair) + treat, gehan, dist = "exponential")
summary(survreg(Surv(time, cens) ~ treat, gehan, dist = "exponential"))
summary(survreg(Surv(time, cens) ~ treat, gehan))
gehan.cox <- coxph(Surv(time, cens) ~ treat, gehan)
summary(gehan.cox)
### Name: ginv
### Title: Generalized Inverse of a Matrix
### Aliases: ginv
### Keywords: algebra
### ** Examples
## Not run:
##D # The function is currently defined as
##D function(X, tol = sqrt(.Machine$double.eps))
##D {
##D ## Generalized Inverse of a Matrix
##D dnx <- dimnames(X)
##D if(is.null(dnx)) dnx <- vector("list", 2)
##D s <- svd(X)
##D nz <- s$d > tol * s$d[1]
##D structure(
##D if(any(nz)) s$v[, nz] %*% (t(s$u[, nz])/s$d[nz]) else X,
##D dimnames = dnx[2:1])
##D }
## End(Not run)
### Name: glm.convert
### Title: Change a Negative Binomial fit to a GLM fit
### Aliases: glm.convert
### Keywords: regression models
### ** Examples
quine.nb1 <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)
quine.nbA <- glm.convert(quine.nb1)
quine.nbB <- update(quine.nb1, . ~ . + Sex:Age:Lrn)
anova(quine.nbA, quine.nbB)
### Name: glm.nb
### Title: Fit a Negative Binomial Generalized Linear Model
### Aliases: glm.nb family.negbin logLik.negbin
### Keywords: regression models
### ** Examples
quine.nb1 <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)
quine.nb2 <- update(quine.nb1, . ~ . + Sex:Age:Lrn)
quine.nb3 <- update(quine.nb2, Days ~ .^4)
anova(quine.nb1, quine.nb2, quine.nb3)
## Don't show: ## PR#1695
y <- c(7, 5, 4, 7, 5, 2, 11, 5, 5, 4, 2, 3, 4, 3, 5, 9, 6, 7, 10, 6, 12,
6, 3, 5, 3, 9, 13, 0, 6, 1, 2, 0, 1, 0, 0, 4, 5, 1, 5, 3, 3, 4)
lag1 <- c(0, 7, 5, 4, 7, 5, 2, 11, 5, 5, 4, 2, 3, 4, 3, 5, 9, 6, 7, 10,
6, 12, 6, 3, 5, 3, 9, 13, 0, 6, 1, 2, 0, 1, 0, 0, 4, 5, 1, 5, 3, 3)
lag2 <- c(0, 0, 7, 5, 4, 7, 5, 2, 11, 5, 5, 4, 2, 3, 4, 3, 5, 9, 6, 7,
10, 6, 12, 6, 3, 5, 3, 9, 13, 0, 6, 1, 2, 0, 1, 0, 0, 4, 5, 1, 5, 3)
lag3 <- c(0, 0, 0, 7, 5, 4, 7, 5, 2, 11, 5, 5, 4, 2, 3, 4, 3, 5, 9, 6,
7, 10, 6, 12, 6, 3, 5, 3, 9, 13, 0, 6, 1, 2, 0, 1, 0, 0, 4, 5, 1, 5)
(fit <- glm(y ~ lag1+lag2+lag3, family=poisson(link=identity),
start=c(2, 0.1, 0.1, 0.1)))
try(glm.nb(y ~ lag1+lag2+lag3, link=identity))
glm.nb(y ~ lag1+lag2+lag3, link=identity, start=c(2, 0.1, 0.1, 0.1))
glm.nb(y ~ lag1+lag2+lag3, link=identity, start=coef(fit))
glm.nb(y ~ lag1+lag2+lag3, link=identity, etastart=rep(5, 42))
## End Don't show
### Name: glmmPQL
### Title: Fit Generalized Linear Mixed Models via PQL
### Aliases: glmmPQL
### Keywords: models
### ** Examples
library(nlme) # will be loaded automatically if omitted
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
## Don't show:
# an example of offset
summary(glmmPQL(y ~ trt + week, random = ~ 1 | ID,
family = binomial, data = bacteria))
summary(glmmPQL(y ~ trt + week + offset(week), random = ~ 1 | ID,
family = binomial, data = bacteria))
## End Don't show
### Name: housing
### Title: Frequency Table from a Copenhagen Housing Conditions Survey
### Aliases: housing
### Keywords: datasets
### ** Examples
options(contrasts = c("contr.treatment", "contr.poly"))
# Surrogate Poisson models
house.glm0 <- glm(Freq ~ Infl*Type*Cont + Sat, family = poisson,
data = housing)
summary(house.glm0, cor = FALSE)
addterm(house.glm0, ~. + Sat:(Infl+Type+Cont), test = "Chisq")
house.glm1 <- update(house.glm0, . ~ . + Sat*(Infl+Type+Cont))
summary(house.glm1, cor = FALSE)
1 - pchisq(deviance(house.glm1), house.glm1$df.residual)
dropterm(house.glm1, test = "Chisq")
addterm(house.glm1, ~. + Sat:(Infl+Type+Cont)^2, test = "Chisq")
hnames <- lapply(housing[, -5], levels) # omit Freq
newData <- expand.grid(hnames)
newData$Sat <- ordered(newData$Sat)
house.pm <- predict(house.glm1, newData,
type = "response") # poisson means
house.pm <- matrix(house.pm, ncol = 3, byrow = TRUE,
dimnames = list(NULL, hnames[[1]]))
house.pr <- house.pm/drop(house.pm %*% rep(1, 3))
cbind(expand.grid(hnames[-1]), round(house.pr, 2))
# Iterative proportional scaling
loglm(Freq ~ Infl*Type*Cont + Sat*(Infl+Type+Cont), data = housing)
# multinomial model
library(nnet)
(house.mult<- multinom(Sat ~ Infl + Type + Cont, weights = Freq,
data = housing))
house.mult2 <- multinom(Sat ~ Infl*Type*Cont, weights = Freq,
data = housing)
anova(house.mult, house.mult2)
house.pm <- predict(house.mult, expand.grid(hnames[-1]),
type = "probs")
cbind(expand.grid(hnames[-1]), round(house.pm, 2))
# proportional odds model
house.cpr <- apply(house.pr, 1, cumsum)
logit <- function(x) log(x/(1-x))
house.ld <- logit(house.cpr[2, ]) - logit(house.cpr[1, ])
(ratio <- sort(drop(house.ld)))
mean(ratio)
(house.plr <- polr(Sat ~ Infl + Type + Cont,
data = housing, weights = Freq))
house.pr1 <- predict(house.plr, expand.grid(hnames[-1]),
type = "probs")
cbind(expand.grid(hnames[-1]), round(house.pr1, 2))
Fr <- matrix(housing$Freq, ncol = 3, byrow = TRUE)
2*sum(Fr*log(house.pr/house.pr1))
house.plr2 <- stepAIC(house.plr, ~.^2)
house.plr2$anova
### Name: huber
### Title: Huber M-estimator of Location with MAD Scale
### Aliases: huber
### Keywords: robust
### ** Examples
huber(chem)
### Name: hubers
### Title: Huber Proposal 2 Robust Estimator of Location and/or Scale
### Aliases: hubers
### Keywords: robust
### ** Examples
hubers(chem)
hubers(chem, mu=3.68)
### Name: immer
### Title: Yields from a Barley Field Trial
### Aliases: immer
### Keywords: datasets
### ** Examples
immer.aov <- aov(cbind(Y1,Y2) ~ Loc + Var, data = immer)
summary(immer.aov)
immer.aov <- aov((Y1+Y2)/2 ~ Var + Loc, data = immer)
summary(immer.aov)
model.tables(immer.aov, type = "means", se = TRUE, cterms = "Var")
### Name: isoMDS
### Title: Kruskal's Non-metric Multidimensional Scaling
### Aliases: isoMDS Shepard
### Keywords: multivariate
### ** Examples
data(swiss)
swiss.x <- as.matrix(swiss[, -1])
swiss.dist <- dist(swiss.x)
swiss.mds <- isoMDS(swiss.dist)
plot(swiss.mds$points, type = "n")
text(swiss.mds$points, labels = as.character(1:nrow(swiss.x)))
swiss.sh <- Shepard(swiss.dist, swiss.mds$points)
plot(swiss.sh, pch = ".")
lines(swiss.sh$x, swiss.sh$yf, type = "S")
### Name: kde2d
### Title: Two-Dimensional Kernel Density Estimation
### Aliases: kde2d
### Keywords: dplot
### ** Examples