Title: | Utility Functions, Datasets and Extended Examples for Survival Analysis |
---|---|
Description: | Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R". |
Authors: | Annika Tillander [ctb], Andreas Karlsson [aut], Johan Zetterqvist [ctb], Peter Strom [ctb], Benedicte Delcoigne [ctb], Mark Clements [aut, cre] |
Maintainer: | Mark Clements <[email protected]> |
License: | GPL (>= 2) |
Version: | 0.2.2 |
Built: | 2024-10-30 09:24:46 UTC |
Source: | CRAN |
Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".
Annika Tillander [ctb], Andreas Karlsson [aut], Johan Zetterqvist [ctb], Peter Strom [ctb], Benedicte Delcoigne [ctb], Mark Clements [aut, cre]
Maintainer: Mark Clements <[email protected]>
plot(muhaz2(Surv(surv_mm, status == "Dead: cancer")~1, melanoma))
plot(muhaz2(Surv(surv_mm, status == "Dead: cancer")~1, melanoma))
Column-bind a model matrix to the source data-frame
addIndicators(data, formula, drop.intercept = TRUE)
addIndicators(data, formula, drop.intercept = TRUE)
data |
source data-frame or matrix. |
formula |
model formula used to add columns. |
drop.intercept |
logical as to whether to drop the column named '(Intercept)'. |
This function calls model.matrix
, conditionally checks for and removes '(Intercept)', and binds with the original data-frame (or matrix).
data-frame or matrix.
addIndicators(data.frame(f = c("a","a","b")), ~f+0)
addIndicators(data.frame(f = c("a","a","b")), ~f+0)
Convert a bshazard object to a data-frame.
## S3 method for class 'bshazard' as.data.frame(x, ...)
## S3 method for class 'bshazard' as.data.frame(x, ...)
x |
bshazard object |
... |
other arguments |
Returns a data-frame with names time, hazard, conf.low and conf.high (cf. lower.ci and upper.ci provided in the object).
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (x, ...) { with(x, data.frame(time, hazard, conf.low = lower.ci, conf.high = upper.ci)) }
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (x, ...) { with(x, data.frame(time, hazard, conf.low = lower.ci, conf.high = upper.ci)) }
Bereavement dataset
data("brv")
data("brv")
A data frame with 399 observations on the following 11 variables.
id
a numeric vector the id of a subject
couple
a numeric vector for the id of a couple
dob
a Date for the date of birth
doe
a Date for the date of entry into study
dox
a Date for the date of exit from study
dosp
a Date for the date of bereavement
fail
a numeric vector for status at study exit 0=alive 1=died
group
a numeric vector for Group
disab
a numeric vector for disability level
health
a numeric vector for perceived health status
sex
a numeric vector for sex 1=M 2=F
data(brv) ## maybe str(brv) ; plot(brv) ...
data(brv) ## maybe str(brv) ; plot(brv) ...
Colon cancer dataset
data("colon")
data("colon")
A data frame with 15564 observations on the following 18 variables.
sex
a factor with levels Male
Female
age
a numeric vector
stage
a factor with levels Unknown
Localised
Regional
Distant
mmdx
a numeric vector
yydx
a numeric vector
surv_mm
a numeric vector
surv_yy
a numeric vector
status
a factor with levels Alive
Dead: cancer
Dead: other
Lost to follow-up
subsite
a factor with levels Coecum and ascending
Transverse
Descending and sigmoid
Other and NOS
year8594
a factor with levels Diagnosed 75-84
Diagnosed 85-94
agegrp
a factor with levels 0-44
45-59
60-74
75+
dx
a Date
exit
a Date
id
a numeric vector
ydx
a numeric vector for continuous year of diagnosis
yexit
a numeric vector for continuous year of exit
data(colon) ## maybe str(colon) ; plot(colon) ...
data(colon) ## maybe str(colon) ; plot(colon) ...
colon
dataset used for teaching.
Sample from the colon
dataset used for teaching.
data("colon_sample")
data("colon_sample")
A data frame with 35 observations on the following 9 variables.
sex
a factor with levels Male
Female
age
a numeric vector
stage
a factor with levels Unknown
Localised
Regional
Distant
mmdx
a numeric vector
yydx
a numeric vector
surv_mm
a numeric vector
surv_yy
a numeric vector
status
a factor with levels Alive
Dead: cancer
Dead: other
Lost to follow-up
subsite
a factor with levels Coecum and ascending
Transverse
Descending and sigmoid
Other and NOS
data(colon_sample) ## maybe str(colon_sample) ; plot(colon_sample) ...
data(colon_sample) ## maybe str(colon_sample) ; plot(colon_sample) ...
coxph
Smoothed hazard estimates for coxph
coxphHaz(object, newdata, n.grid = 300, kernel = "epanechnikov", from, to, ...) ## S3 method for class 'coxphHaz' print(x, digits=NULL, ...) ## S3 method for class 'coxphHaz' plot(x, xlab="Time", ylab="Hazard", type="l", ...) ## S3 method for class 'coxphHazList' plot(x, xlab="Time", ylab="Hazard", type="l", col=1:length(x), lty=1, legend.args=list(), ...) ## S3 method for class 'coxphHazList' lines(x, ...) ## S3 method for class 'coxphHaz' as.data.frame(x, row.names=NULL, optional=FALSE, level=0.95, ...) ## S3 method for class 'coxphHazList' as.data.frame(x, row.names=NULL, optional=FALSE, ...)
coxphHaz(object, newdata, n.grid = 300, kernel = "epanechnikov", from, to, ...) ## S3 method for class 'coxphHaz' print(x, digits=NULL, ...) ## S3 method for class 'coxphHaz' plot(x, xlab="Time", ylab="Hazard", type="l", ...) ## S3 method for class 'coxphHazList' plot(x, xlab="Time", ylab="Hazard", type="l", col=1:length(x), lty=1, legend.args=list(), ...) ## S3 method for class 'coxphHazList' lines(x, ...) ## S3 method for class 'coxphHaz' as.data.frame(x, row.names=NULL, optional=FALSE, level=0.95, ...) ## S3 method for class 'coxphHazList' as.data.frame(x, row.names=NULL, optional=FALSE, ...)
object |
|
newdata |
data-frame with covariates for prediction |
n.grid |
the number of grid values for which the hazard is calculated |
kernel |
the kernel used for smoothing |
from |
argument for |
to |
argument for |
x |
object |
digits |
argument passed to |
col |
graphics argument |
lty |
graphics argument |
xlab |
graphics argument |
ylab |
graphics argument |
type |
graphics argument |
level |
level for confidence intervals (default=0.95) |
row.names |
|
optional |
logical. If |
legend.args |
a list of options that are passed to the legend call. Defaults are
|
... |
other arguments. For |
Smooth hazard estimates from a Cox model using kernel smoothing of the Nelson-Aalen estimator.
The coxphHaz
function returns either a class of type
c("coxphHaz","density")
when newdata
has one row or, for multiple rows in
newdata
, a class of type "coxphHazList"
, which is a list of
type c("coxphHaz","density")
.
fit <- coxph(Surv(surv_mm/12,status=="Dead: cancer")~agegrp, data=colon) newdata <- data.frame(agegrp=levels(colon$agegrp)) haz <- suppressWarnings(coxphHaz(fit,newdata)) plot(haz, xlab="Time since diagnosis (years)")
fit <- coxph(Surv(surv_mm/12,status=="Dead: cancer")~agegrp, data=colon) newdata <- data.frame(agegrp=levels(colon$agegrp)) haz <- suppressWarnings(coxphHaz(fit,newdata)) plot(haz, xlab="Time since diagnosis (years)")
Diet data set
data("diet")
data("diet")
A data frame with 337 observations on the following 15 variables.
id
a numeric vector
chd
a numeric vector
y
a numeric vector
hieng
a factor with levels low
high
energy
a numeric vector
job
a factor with levels driver
conductor
bank
month
a numeric vector
height
a numeric vector
weight
a numeric vector
doe
a Date for date of study entry
dox
a Date for date of study exit
dob
a Date for date of birth
yob
a numeric vector for continuous year of birth
yoe
a numeric vector for continuous year of entry
yox
a numeric vector for continuous year of exit
data(diet) ## maybe str(diet) ; plot(diet) ...
data(diet) ## maybe str(diet) ; plot(diet) ...
irr
and or
use eform with a different name for the estimator.
eform(object, ...) ## Default S3 method: eform(object, parm, level = 0.95, method = c("Delta","Profile"), name = "exp(beta)", ...) irr(..., name = "IRR") or(..., name = "OR")
eform(object, ...) ## Default S3 method: eform(object, parm, level = 0.95, method = c("Delta","Profile"), name = "exp(beta)", ...) irr(..., name = "IRR") or(..., name = "OR")
object |
A fitted model object with |
parm |
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |
level |
the confidence level required |
method |
string to determine method to use the delta method ( |
name |
name of the estimator. |
... |
arguments to pass from |
A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in
## from example(glm) counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3, 1, 9); treatment <- gl(3, 3) glm.D93 <- glm(counts ~ outcome + treatment, family = poisson()) eform(glm.D93) eform(glm.D93, method="Profile")
## from example(glm) counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3, 1, 9); treatment <- gl(3, 3) glm.D93 <- glm(counts ~ outcome + treatment, family = poisson()) eform(glm.D93) eform(glm.D93, method="Profile")
Create cohort life table.
lifetab(tis, ninit, nlost, nevent)
lifetab(tis, ninit, nlost, nevent)
tis |
a vector of end points of time intervals, whose length is 1 greater than nlost and nevent. |
ninit |
the number of subjects initially entering the study. |
nlost |
a vector of the number of individuals lost follow or withdrawn alive for whatever reason. |
nevent |
a vector of the number of individuals who experienced the event |
This is a minor update of the lifetab
function from the
KMsurv package, where the start and stop times of the intervals
are now included in the return value.
A data.frame with the following columns:
tstart |
interval start time. |
tstop |
interval end time. |
nsubs |
the number of subject entering the intervals who have not experienced the event. |
nlost |
the number of individuals lost follow or withdrawn alive for whatever reason. |
nrisk |
the estimated number of individuals at risk of experiencing the event. |
nevent |
the number of individuals who experienced the event. |
surv |
the estimated survival function at the start of the intervals. |
pdf |
the estimated probability density function at the midpoint of the intervals. |
hazard |
the estimated hazard rate at the midpoint of the intervals. |
se.surv |
the estimated standard deviation of survival at the beginning of the intervals. |
se.pdf |
the estimated standard deviation of the prbability density function at the midpoint of the intervals. |
se.hazard |
the estimated standard deviation of the hazard function at the midpoint of the intervals |
The row.names are the intervals.
Jun Yan [email protected]
tis <- c(0, 2, 3, 5, 7, 11, 17, 25, 37, 53, NA) nsubs <- c(927, 848, 774, 649, 565, 449, 296, 186, 112, 27) nlost <- c(2, 3, 6, 9, 7, 5, 3, rep(0, 3)) nevent <- c(77, 71, 119, 75, 109, 148, 107, 74, 85, 27) lifetab(tis, nsubs[1], nlost, nevent)
tis <- c(0, 2, 3, 5, 7, 11, 17, 25, 37, 53, NA) nsubs <- c(927, 848, 774, 649, 565, 449, 296, 186, 112, 27) nlost <- c(2, 3, 6, 9, 7, 5, 3, rep(0, 3)) nevent <- c(77, 71, 119, 75, 109, 148, 107, 74, 85, 27) lifetab(tis, nsubs[1], nlost, nevent)
lifetab
from the
KMsurv
package.
Calculate a life table using the actuarial method using a formula and a data-frame with optional breaks.
lifetab2(formula, data, subset, breaks = NULL) ## S3 method for class 'lifetab2' plot(x, y=NULL, ...) ## S3 method for class 'lifetab2' lines(x, y=NULL, ...)
lifetab2(formula, data, subset, breaks = NULL) ## S3 method for class 'lifetab2' plot(x, y=NULL, ...) ## S3 method for class 'lifetab2' lines(x, y=NULL, ...)
formula |
formula with the left-hand side being a |
data |
optional |
subset |
optional |
breaks |
optional numeric vector of breaks. If this is not provided, then the
unique time values from the |
x |
|
y |
unused argument (part of the generic function) |
... |
other arguments |
See lifetab
for details. This wrapper is meant to make
life easier.
A copy of the lifetab
function has been included in
the biostat3 package to reduce dependencies.
A data.frame
as per lifetab
.
Mark Clements for the wrapper.
## we can use unique transformed times (colon_sample) lifetab2(Surv(floor(surv_yy),status=="Dead: cancer")~1, colon_sample) ## we can also use the breaks argument (colon) lifetab2(Surv(surv_yy,status=="Dead: cancer")~1, colon, breaks=0:10)
## we can use unique transformed times (colon_sample) lifetab2(Surv(floor(surv_yy),status=="Dead: cancer")~1, colon_sample) ## we can also use the breaks argument (colon) lifetab2(Surv(surv_yy,status=="Dead: cancer")~1, colon, breaks=0:10)
Using results calculated by the linearHypothesis
function in
the car
package, calculate a linear combination of regression parameters.
lincom(model, specification, level = 0.95, eform = FALSE, ...)
lincom(model, specification, level = 0.95, eform = FALSE, ...)
model |
regression model object (as per the |
specification |
specification of the linear combination. This is the same as a
single component of the |
level |
the confidence level required |
eform |
logical for whether to exponentiate the confidence interval (default=FALSE) |
... |
other arguments to the |
Multiple specifications of linear combinations are called individually.
A matrix with columns including the estimate, a normal-based confidence interval, test statistic and p-values.
See Also linearHypothesis
.
fit <- glm(chd ~ hieng*job + offset(log(y)), data=diet, family=poisson) lincom(fit, c("hienghigh+hienghigh:jobconductor", "hienghigh+hienghigh:jobbank"), eform=TRUE)
fit <- glm(chd ~ hieng*job + offset(log(y)), data=diet, family=poisson) lincom(fit, c("hienghigh+hienghigh:jobconductor", "hienghigh+hienghigh:jobbank"), eform=TRUE)
Melanoma cancer dataset
data("melanoma")
data("melanoma")
A data frame with 7775 observations on the following 18 variables.
sex
a factor with levels Male
Female
age
a numeric vector
stage
a factor with levels Unknown
Localised
Regional
Distant
mmdx
a numeric vector
yydx
a numeric vector
surv_mm
a numeric vector
surv_yy
a numeric vector
status
a factor with levels Alive
Dead: cancer
Dead: other
Lost to follow-up
subsite
a factor with levels Head and Neck
Trunk
Limbs
Multiple and NOS
year8594
a factor with levels Diagnosed 75-84
Diagnosed 85-94
dx
a Date
exit
a Date
agegrp
a factor with levels 0-44
45-59
60-74
75+
id
a numeric vector
ydx
a numeric vector for continuous year of diagnosis
yexit
a numeric vector for continuous year of exit
data(melanoma) ## maybe str(melanoma) ; plot(melanoma) ...
data(melanoma) ## maybe str(melanoma) ; plot(melanoma) ...
muhaz
function from the
muhaz
package.
Formula wrapper for the muhaz
function from the
muhaz
package.
muhaz2(formula, data, subset, max.time, ...) ## S3 method for class 'muhaz2' plot(x, haz.scale=1, ylab="Hazard", ylim=NULL, log="", ...) ## S3 method for class 'muhazList' plot(x, lty=1:5, col=1:length(x), log="", legend.args=list(), ...) ## S3 method for class 'muhaz2' lines(x, ..., haz.scale = 1) ## S3 method for class 'muhazList' lines(x, lty=1, col=1:length(x), ...) ## S3 method for class 'muhazList' summary(object, ...) ## S3 method for class 'muhazList' ggplot(data, mapping=NULL, xlab="Time", ylab="Hazard", ..., environment = parent.frame()) ## S3 method for class 'muhazList' as.data.frame(x, row.names, optional, ...) ## S3 method for class 'muhaz' as.data.frame(x, row.names, optional, ...)
muhaz2(formula, data, subset, max.time, ...) ## S3 method for class 'muhaz2' plot(x, haz.scale=1, ylab="Hazard", ylim=NULL, log="", ...) ## S3 method for class 'muhazList' plot(x, lty=1:5, col=1:length(x), log="", legend.args=list(), ...) ## S3 method for class 'muhaz2' lines(x, ..., haz.scale = 1) ## S3 method for class 'muhazList' lines(x, lty=1, col=1:length(x), ...) ## S3 method for class 'muhazList' summary(object, ...) ## S3 method for class 'muhazList' ggplot(data, mapping=NULL, xlab="Time", ylab="Hazard", ..., environment = parent.frame()) ## S3 method for class 'muhazList' as.data.frame(x, row.names, optional, ...) ## S3 method for class 'muhaz' as.data.frame(x, row.names, optional, ...)
formula |
formula with the left-hand side being a |
data |
optional |
subset |
subset predictate for the dataset |
max.time |
maximum follow-up time for the hazards |
xlab |
graphics argument for xlab (x-axis label) |
ylab |
graphics argument for ylab (y-axis label) |
lty |
graphics argument for line type |
col |
graphics argument for line colour |
legend.args |
a list of options that are passed to the legend call. Defaults are |
haz.scale |
scale for the hazard in the plot |
row.names |
not currently used |
object |
muhazList object |
ylim |
graphics argument for the limits of the y axis |
log |
graphics argument for a log transformation of the x or y axes |
x |
muhazList or muhaz object |
environment |
*[Deprecated]* Used prior to tidy evaluation. |
optional |
not currently used |
mapping |
Default list of aesthetic mappings to use for plot. If not specified, must be supplied in each layer added to the plot. |
... |
other arguments |
For a single strata, this is a muhaz
object. For multiple strata, this is a muhazList
object, which includes methods for
plot(muhaz2(Surv(surv_mm, status == "Dead: cancer")~1, melanoma))
plot(muhaz2(Surv(surv_mm, status == "Dead: cancer")~1, melanoma))
A wrapper for the poisson.test
that allows for vector values.
poisson.ci(x, T = 1, alternative = c("two.sided", "less", "greater"), conf.level = 0.95)
poisson.ci(x, T = 1, alternative = c("two.sided", "less", "greater"), conf.level = 0.95)
x |
number of events. |
T |
time base for event count. |
alternative |
indicates the type of confidence interval and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. |
conf.level |
confidence level for the returned confidence interval. |
This uses a vectorised algorithm based on stats::poisson.test
.
A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in % (by default 2.5% and 97.5%). for the two-sided alternative.
### These are paraphrased from data sets in the ISwR package ## SMR, Welsh Nickel workers poisson.ci(137, 24.19893) ## eba1977, compare Fredericia to other three cities for ages 55-59 poisson.ci(c(11, 6+8+7), c(800, 1083+1050+878))
### These are paraphrased from data sets in the ISwR package ## SMR, Welsh Nickel workers poisson.ci(137, 24.19893) ## eba1977, compare Fredericia to other three cities for ages 55-59 poisson.ci(c(11, 6+8+7), c(800, 1083+1050+878))
popmort dataset, with population-based mortality rates
data("popmort")
data("popmort")
A data frame with 10600 observations on the following 5 variables.
sex
a numeric vector
a numeric vector
a numeric vector
prob
a numeric vector
rate
a numeric vector
data(popmort) ## maybe str(popmort) ; plot(popmort) ...
data(popmort) ## maybe str(popmort) ; plot(popmort) ...
Simple implementation for kernel density smoothing of the Nelson-Aalen
estimator. Prefer muhaz
for right censored data and
bshazard
for left truncated and right censored data.
smoothHaz(object, n.grid = 300, kernel = "epanechnikov", from = NULL, to = NULL, min.n.risk = 1, ...) ## S3 method for class 'smoothHaz' plot(x, xlab = "Time", ylab = "Hazard", type = "l", ...)
smoothHaz(object, n.grid = 300, kernel = "epanechnikov", from = NULL, to = NULL, min.n.risk = 1, ...) ## S3 method for class 'smoothHaz' plot(x, xlab = "Time", ylab = "Hazard", type = "l", ...)
object |
|
n.grid |
number of grid points; passed to |
kernel |
kernel used; passed to |
from |
left boundary; passed to |
to |
right boundary; passed to |
min.n.risk |
minimum number at risk |
x |
object of class |
xlab |
graphics argument |
ylab |
graphics argument |
type |
graphics argument |
... |
Other arguments |
Plot of log(time) versus -log(-log(survival)) to assess non-proportionality. A constant distance between curves suggest proportionality.
survPHplot(formula, data, subset, contrasts, weights, col = 1:5, lty = 1:5, pch = 19, xlab = "Time (log scale)", ylab = "-log(-log(Survival))", log = "x", legend.args = list(), ...)
survPHplot(formula, data, subset, contrasts, weights, col = 1:5, lty = 1:5, pch = 19, xlab = "Time (log scale)", ylab = "-log(-log(Survival))", log = "x", legend.args = list(), ...)
formula |
either (i) formula with a |
data |
data argument passed to |
subset |
subset argument passed to |
contrasts |
contrasts argument passed to |
weights |
weights argument passed to |
col |
colours of the curves passed to |
lty |
line type of the curves passed to |
pch |
pch for the curves passed to |
xlab |
xlab graphics argument passed to |
ylab |
ylab graphics argument passed to |
log |
log graphics argument passed to |
legend.args |
list of arguments passed to |
... |
Other arguments passed to |
The default plot is to use straight lines between the transformed survival values for each strata, rather than using steps.
Primary purpose is for plotting (side effect). The return value is initial plot.
survPHplot(Surv(surv_mm/12, status == "Dead: cancer") ~ year8594, data=colon, subset=(stage=="Localised"), legend.args=list(bty="n"))
survPHplot(Surv(surv_mm/12, status == "Dead: cancer") ~ year8594, data=colon, subset=(stage=="Localised"), legend.args=list(bty="n"))
Describe rates using the Surv
function.
survRate(formula, data, subset, addvars = TRUE, ci=TRUE, ...)
survRate(formula, data, subset, addvars = TRUE, ci=TRUE, ...)
formula |
formula with the left-hand-side being a |
data |
source dataset |
subset |
subset conditions for the source dataset |
addvars |
logical for whether to add the stratification variables to the output (default=TRUE). This is useful for subsequent analysis. |
ci |
logical for whether to calculate the confidence interval (default=TRUE). |
... |
other arguments to the |
data-frame with columns tstop
, event
, rate
,
lower
and upper
. Covariates are appended if
addvar=TRUE
.
Confidence intervals use stats::poisson.test
.
## incidence rates for CHD for low- or high-energy diets survRate(Surv(y,chd) ~ hieng, data=diet)
## incidence rates for CHD for low- or high-energy diets survRate(Surv(y,chd) ~ hieng, data=diet)
biostat3
package
Utility functions for the biostat3
package.
updateList(object, ...) format_perc(probs, digits)
updateList(object, ...) format_perc(probs, digits)
object |
base object (list) |
... |
arguments to update |
probs |
probability to express as a percentage |
digits |
number of significant digits |
Update the names in the base object
list that are specified in
the arguments to update.
list
updateList(list(a=1,b=2), a=10, c=30)
updateList(list(a=1,b=2), a=10, c=30)
Date
vector to a numeric vector
Convert a Date
vector to a numeric vector (either continuous or truncated).
year(date, trunc = FALSE, year.length = 365.24)
year(date, trunc = FALSE, year.length = 365.24)
date |
Date vector |
trunc |
logical for whether to truncate the date to a whole year or consider the date as a double (default). |
year.length |
assumed length of a year |
For the double calculation, we use (truncated year of Date) + (date - 1 Jan of Year)/year.length.
numeric vector
c(year(as.Date("2001-07-01")),year(as.Date("2001-01-01"),trunc=TRUE))
c(year(as.Date("2001-07-01")),year(as.Date("2001-01-01"),trunc=TRUE))