Last updated on 2024-12-25 05:48:32 CET.
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r-devel-linux-x86_64-debian-clang | 0.2.0 | 9.66 | 73.63 | 83.29 | ERROR | |
r-devel-linux-x86_64-debian-gcc | 0.2.0 | 6.65 | 49.99 | 56.64 | NOTE | |
r-devel-linux-x86_64-fedora-clang | 0.2.0 | 129.33 | NOTE | |||
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Version: 0.2.0
Check: Rd files
Result: NOTE
checkRd: (-1) DCchoice-package.Rd:134: Lost braces
134 | in KG M\"{a}ler, JR Vincent (eds.), \emph{Handbook of Environmental Economics}.
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checkRd: (-1) DCchoice-package.Rd:188: Lost braces
188 | Kristr\"{o}m B (1990).
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checkRd: (-1) KR.Rd:4: Lost braces
4 | \title{Kristr\"{o}m's single-bounded dichotomous choice CVM data}
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checkRd: (-1) KR.Rd:7: Lost braces
7 | Kristr\"{o}m (1990).
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checkRd: (-1) KR.Rd:27: Lost braces
27 | the eleven virgin forests in Sweden. See Kristr\"{o}m (1990)
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checkRd: (-1) KR.Rd:32: Lost braces
32 | The data are used in Kristr\"{o}m (1990).
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checkRd: (-1) KR.Rd:35: Lost braces
35 | Professor Bengt Kristr\"{o}m, Swedish University of Agricultural Sciences.
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checkRd: (-1) KR.Rd:41: Lost braces
41 | Kristr\"{o}m B (1990).
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checkRd: (-1) dbchoice.Rd:155: Lost braces
155 | G, Mourato S, \"{O}zdemiro\={g}lu E, Pearce DW, Sugden R, Swanson J (eds.) (2002).
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checkRd: (-1) dbchoice.Rd:155: Lost braces
155 | G, Mourato S, \"{O}zdemiro\={g}lu E, Pearce DW, Sugden R, Swanson J (eds.) (2002).
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checkRd: (-1) dbchoice.Rd:161: Lost braces
161 | in KG M\"{a}ler, JR Vincent (eds.), \emph{Handbook of Environmental Economics}.
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checkRd: (-1) kristrom.Rd:7: Lost braces
7 | The Kristr\"{o}m's nonparametric approach to analyze single-bounded dichotomous choice contingent valuation data
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checkRd: (-1) kristrom.Rd:12: Lost braces
12 | of the Kristr\"{o}m's nonparametric method.
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checkRd: (-1) kristrom.Rd:32: Lost braces
32 | valuation (CV) data on the basis of Kristr\"{o}m's nonparametric method (Kristr\"{o}m 1990).
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checkRd: (-1) kristrom.Rd:32: Lost braces
32 | valuation (CV) data on the basis of Kristr\"{o}m's nonparametric method (Kristr\"{o}m 1990).
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checkRd: (-1) kristrom.Rd:64: Lost braces
64 | \item{estimates}{a matrix of the estimated Kristr\"{o}m's survival probabilities.}
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checkRd: (-1) kristrom.Rd:67: Lost braces
67 | and displays the estimated Kristr\"{o}m's survival probabilities.
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checkRd: (-1) kristrom.Rd:69: Lost braces
69 | The extractor function \code{summary()} is used to display the estimated Kristr\"{o}m's survival
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checkRd: (-1) kristrom.Rd:88: Lost braces
88 | Kristr\"{o}m B (1990).
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checkRd: (-1) sbchoice.Rd:102: Lost braces
102 | G, Mourato S, \"{O}zdemiro\={g}lu E, Pearce DW, Sugden R, Swanson J (eds.) (2002).
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checkRd: (-1) sbchoice.Rd:102: Lost braces
102 | G, Mourato S, \"{O}zdemiro\={g}lu E, Pearce DW, Sugden R, Swanson J (eds.) (2002).
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checkRd: (-1) sbchoice.Rd:112: Lost braces
112 | in KG M\"{a}ler, JR Vincent (eds.), \emph{Handbook of Environmental Economics}.
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checkRd: (-1) spike.Rd:69: Lost braces
69 | The functions \code{\link{sbspike}}, \code{\link{oohbspike}}, and \code{\link{dbspike}} implement a spike model analysis of single-, one-and-one-half-, and double-bounded dichotomous choice contingent valuation (SB, OOHB, and DB DCCV) data, respectively. A simple spike model assumes a non-zero probability of zero willingness to pay (WTP) for a good/service and a zero probability of negative WTP. These functions are developed according to the original simplest spike model proposed by Kristr\"{o}m (1997) and its follow-up studies (i.e., Yoo and Kwak (2002) for DB DCCV and Kwak et al. (2013) for OOHB DCCV). These functions use a maximum likelihood methods to fit the models with the CV data.
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checkRd: (-1) spike.Rd:75: Lost braces
75 | The other difference is about an argument \code{formula}, which is assigned an object of the S3 class \code{'\link[Formula]{Formula}'}. For a model formula for the ordinary model functions, the left-handed side of the tilde (\code{~}) contains only response variable(s) (i.e., the response to SB DCCV question, \code{R1}, for \code{\link{sbchoice}}; the response to the first stage of OOHB/DB DCCV question, \code{R1}, and the second one, \code{R2}, for \code{\link{oohbchoice}} and \code{\link{dbchoice}}), while it contains both the response variable(s) and spike variable for the spike model functions. The spike variable, \code{S}, which must be set in the second part (after the vertical bar [\code{|}]) of the left-handed side of the tilde, takes the value of \code{1} if the respondent has a positive WTP for a good specified in the DCCV question and \code{0} otherwise. See Kristr\"{o}m (1997) for a question to measure whether the respondent has a positive WTP or not. A typical structure of the formula for spike model functions consists of the following four parts:
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checkRd: (-1) spike.Rd:99: Lost braces
99 | The spike model functions return an S3 \code{'spike'} class object. Various methods for the S3 \code{"spike"} class object are provided as follows: \code{print()} displays estimated coefficients; \code{summary()} extracts detailed information on the fitted model; \code{summary.print()} displays information extracted by \code{summary()}; \code{logLik()} extracts the value of a log-likelihood function at estimates; \code{vcov()} returns the variance-covariance matrix of the fitted model; and \code{plot()} draws an estimated survival distribution of the WTP according to the fitted model. These S3 methods correspond to those for the ordinary DCCV functions \code{\link{sbchoice}}, \code{\link{oohbchoice}}, and \code{\link{dbchoice}}. Therefore, for details, see helps for the corresponding methods for ordinary DCCV functions. Note that the mean and median WTPs calculated by \code{summary()} for the spike model functions are defined as follows (see Kristr\"{o}m 1997): mean WTP = ln(1 + exp(A))/B if the parameter for a bid variable (B) is positive (A is the constant), and NA otherwise; median WTP = A/B if 1/(1 + exp(-A)) < 0.5, and 0 otherwise. When covariates are included in the fitted model, the constant in the mean and median WTPs is replaced with \bold{x'b}, where \bold{x} is a row vector of covariates at the sample mean including the value of 1 for the constant, and \bold{b} is a column vector of estimates for covariates including the constant. See Yoo and Kwak (2009), Kwak et al. (2013), and Lee et al. (2010) for SB, OOHB, and DB spike models with covariates, respectively.
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checkRd: (-1) spike.Rd:149: Lost braces
149 | Kristr\"{o}m B. (1997) Spike models in contingent valuation. \emph{American Journal of Agricultural Economics} \bold{79}: 1013--1023.
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checkRd: (-1) summary.kristrom.Rd:6: Lost braces
6 | Summarizing the Kristr\"{o}m's nonparametric estimation of WTP
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checkRd: (-1) turnbull.Rd:119: Lost braces; missing escapes or markup?
119 | $bid_{end}$, is found, it is straightforward to compute the triangular
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checkRd: (-1) turnbull.Rd:120: Lost braces; missing escapes or markup?
120 | area by $0.5(bid_{end} - bid_{max})P_{max}$ where $bid_{max}$ is
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checkRd: (-1) turnbull.Rd:120: Lost braces; missing escapes or markup?
120 | area by $0.5(bid_{end} - bid_{max})P_{max}$ where $bid_{max}$ is
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checkRd: (-1) turnbull.Rd:120: Lost braces; missing escapes or markup?
120 | area by $0.5(bid_{end} - bid_{max})P_{max}$ where $bid_{max}$ is
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checkRd: (-1) turnbull.Rd:120: Lost braces; missing escapes or markup?
120 | area by $0.5(bid_{end} - bid_{max})P_{max}$ where $bid_{max}$ is
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checkRd: (-1) turnbull.Rd:121: Lost braces; missing escapes or markup?
121 | the maximum bid and $P_{max}$ is the acceptance probability for
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checkRd: (-1) turnbull.Rd:122: Lost braces; missing escapes or markup?
122 | $bid_{max}$, both of which are reported in the summarized output.
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Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64
Version: 0.2.0
Check: examples
Result: ERROR
Running examples in ‘DCchoice-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: dbchoice
> ### Title: Parametric approach to analyze double-bounded dichotomous choice
> ### contingent valuation data
> ### Aliases: dbchoice print.dbchoice vcov.dbchoice logLik.dbchoice
> ### Keywords: DCchoice double-bounded nonlinear
>
> ### ** Examples
>
> ## Examples are based on a data set NaturalPark in the package
> ## Ecdat (Croissant 2011): DBDCCV style question for measuring
> ## willingness to pay for the preservation of the Alentejo Natural
> ## Park. The data set (dataframe) 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")
Error in find.package(package, lib.loc, verbose = verbose) :
there is no package called ‘Ecdat’
Calls: data -> find.package
Execution halted
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