This function analyzes single-, one-and-one-half-, or double-bounded dichotomous choice contingent valuation (CV) data on the basis of the Kaplan-Meier-Turnbull method.
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## for the single-bounded data turnbull.sb(formula, data, subset, conf.int = FALSE, B = 200, conf.level = 0.95, timeMessage = FALSE, ...) ## for the one-and-one-half-bound data turnbull.oohb(formula, data, subset, conf.int = FALSE, B = 200, conf.level = 0.95, timeMessage = FALSE, ...) ## for the double-bounded data turnbull.db(formula, data, subset, conf.int = FALSE, B = 200, conf.level = 0.95, timeMessage = FALSE, ...) ## S3 method for class 'turnbull' print(x, digits = max(3, getOption("digits") - 1), ...)
a formula specifying the model structure.
a data frame containing the variables in the model formula.
an optional vector specifying a subset of observations.
an object of class
number of bootstrap resampling for confidence interval estimation. Defaulted to 200.
a confidence coefficient. Defaulted to 0.95.
a number of digits to display.
optional arguments. Currently not in use.
turnbull.db() analyze single-,
one-and-one-half-, and double-bounded dichotomous choice contingent valuation (CV) data, respectively, on
the basis of the modified Kaplan-Meier-Turnbull method (Carson and Steinberg, 1990).
For single-bounded dichotomous choice data
Most of the details of
turnbull.sb() is the same as those of
See the subsequent section for details.
A difference between
turnbull.db() appears in the
definition of the model formula. In
turnbull.sb(), the first part contains
only one response variable (e.g.,
R1) and the other part contains only one
bid variable (e.g.,
BD1) because respondents are requested to answer a CV
question in the single-bounded dichotomous choice CV. A typical structure of the
formula is given by
R1 ~ BD1
kristrom is an alternative nonparametric method for analyzing single-bounded
dichotomous choice data. A parametric analysis can be done by
For one-and-one-half-bound dichotomous choice data
The details of
turnbull.oohb() is the same as those of
See the the subsequent section for details.
A difference between
turnbull.db() appears in the
definition of variables. See
for the details of creating response and bid variables.
For double-bounded dichotomous choice data
A generic call to
turnbull.db() is given by
formula defines the response variables and bid variables. The argument
data is set as a data frame containing the variables in the model.
A typical structure of the formula for
turnbull.db() is defined as follows:
R1 + R2 ~ BD1 + BD2
The formula consists of two parts. The first part, the left-hand side of the
tilde sign (
~), must contain the response variables for the suggested
prices in the first and the second stage of CV questions. In the above example,
R1 denotes a binary or two-level factor response variable for a bid in
the first stage and
R2 for a bid in the second stage. Each of
"No" to the bid or
The two variables are connected with the arithmetic operator (
+). The other
part, which starts after the tilde sign, must contain bid variables (
BD2) containing suggested prices in the first and second stage of double-bounded
dichotomous choice CV question. The two variables are also connected with the
arithmetic operator (
A parametric approach for analyzing double-bounded dichotomous choice data can be
carried out by
The structure of data set which assigned to the argument
data is the same as that
dbchoice for details in the data set structure.
The confidence intervals are computed by the bootstrap method in
icfit of the interval (Fay and Shaw, 2010) package. The arguments
timeMessage are passed to
icfit(). The bootstrap can be time consuming, so that it is in general not advisable to increase the number of bootstrap resamplint
B. See the help of
icfit for futher detail.
The generic function
print() is available for a fitted model object
"turnbull" and displays simply estimated probabilities of
the distribution: this is the same as the element
pf in the function
icfit of the interval (Fay and Shaw, 2010) package.
The extractor function
summary() is also available for a fitted model
object of class
"turnbull" and the generic function
displays the survival probability and three types of WTP estimates (a Kaplan-Meier mean,
a Spearman-Karber mean, and median WTPs). In estimating the two types of mean WTP,
the area under the survivor function is truncated at the maximum bid because
there seems no unified approach to determine an ending point of bids at which
the acceptance probability is zero. Therefore, we leave the decision of how
the area is treated to the user. In practice, once the ending point,
$bid_end$, is found, it is straightforward to compute the triangular
area by $0.5(bid_end - bid_max)P_max$ where $bid_max$ is
the maximum bid and $P_max$ is the acceptance probability for
$bid_max$, both of which are reported in the summarized output.
Furthermore, the generic function
plot() is available for a fitted model object
"turnbull" and displays the empirical survival function. See
plot.turnbull for details.
turnbull.sb() return an object of S3 class
"turnbull" that is a list with the following components.
a vector of left endpoints of censored interval. The vector is
internally assigned to the argument
a vector of right endpoints of censored interval. The vector is
internally assigned to the argument
a list of components returned from
a vector of unique bids including Inf.
estimates for confidence intervals from
Croissant Y (2011). Ecdat: Data Sets for Econometrics, R package version 0.1-6.1, http://CRAN.R-project.org/package=Ecdat.
Fay MP, Shaw PA (2010). “Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R Package”, Journal of Statistical Software, 36(2), 1-34. http://www.jstatsoft.org/v36/i02/.
Krinsky I, Robb AL (1986). “On Approximating the Statistical Properties of Elasticities.” The Review of Economics and Statistics, 68, 715–719.
Krinsky I, Robb AL (1990). “On Approximating the Statistical Properties of Elasticities: A Correction.” The Review of Economics and Statistics, 72, 189–190.
Turnbull BW (1976). “The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data.” Journal of the Royal Statistical Society. Series B, 38(3), 290–295.
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## Examples are based on turnbull.db(). turnbull.sb() is similarly ## implemented. A difference appears in the definition of the model ## formula. See "Details" section of the help. ## A data set used here is NaturalPark in the package Ecdat (Croissant, ## 2011): double-bounded dichotomous choice CV style question for ## measuring willingness to pay for the preservation of the Alentejo ## Natural Park. The data frame contains seven variables: bid1 (bid in ## the initial question), bidh (higher bid in the follow-up question), ## bidl (lower bid in the follow-up question), answers (response ## outcomes in a factor format with four levels of "nn", "ny", "yn", "yy"), ## respondents' characteristic variables such as age, sex and income (see ## NaturalPark for details). data(NaturalPark, package = "Ecdat") ## The variable answers are converted into a format that is suitable for ## the function turnbull.db() as follows: NaturalPark$R1 <- ifelse(substr(NaturalPark$answers, 1, 1) == "y", 1, 0) NaturalPark$R2 <- ifelse(substr(NaturalPark$answers, 2, 2) == "y", 1, 0) ## The variables bidh and bidl are integrated into one variable (bid2) ## as follows: NaturalPark$bid2 <- ifelse(NaturalPark$R1 == 1, NaturalPark$bidh, NaturalPark$bidl) ## The formula for turnbull.sb and turnbull.db are defined respectively as follows: fmts <- R1 ~ bid1 fmtd <- R1 + R2 ~ bid1 + bid2 ## The function turnbull.db() with the formula fmtd and the data frame ## NaturalPark is executed as follows: NPts <- turnbull.sb(fmts, data = NaturalPark) NPts NPtss <- summary(NPts) NPtss plot(NPts) ## The function turnbull.db() with the formula fmtd and the data frame ## NaturalPark is executed as follows: NPtd <- turnbull.db(fmtd, data = NaturalPark) NPtd NPtds <- summary(NPtd) NPtds plot(NPtd)
Loading required package: MASS Loading required package: interval Loading required package: survival Loading required package: perm Loading required package: Icens Loading required package: MLEcens Loading required package: Formula Probability: 0.342105 0.099453 0.046246 0.044663 0.467532 Survival probability: Upper Prob. 1 0 1.0000 2 6 0.6579 3 12 0.5584 4 24 0.5122 5 48 0.4675 6 Inf 0.0000 WTP estimates: Mean: 24.665138 (Kaplan-Meier) Mean: 26.803244 (Spearman-Karber) Median in: [ 24 , 48 ] Probability: 0.301687 0.039350 0.063226 0.179070 0.191176 0.206699 0.018791 Survival probability: Upper Prob. 1 0 1.00000 2 3 0.69831 3 6 0.65896 4 12 0.59574 5 18 0.41667 6 24 0.41667 7 48 0.22549 8 120 0.01879 9 Inf 0.00000 WTP estimates: Mean: 19.410952 (Kaplan-Meier) Mean: 30.384690 (Spearman-Karber) Median in: [ 12 , 18 ]
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