svyciprop               package:survey               R Documentation

_C_o_n_f_i_d_e_n_c_e _i_n_t_e_r_v_a_l_s _f_o_r _p_r_o_p_o_r_t_i_o_n_s

_D_e_s_c_r_i_p_t_i_o_n:

     Computes confidence intervals for proportions using methods that
     may be more accurate near 0 and 1 than simply using
     'confint(svymean())'.

_U_s_a_g_e:

     svyciprop(formula, design, method = c("logit", "likelihood", "asin", "beta",
     "mean"), level = 0.95, ...)

_A_r_g_u_m_e_n_t_s:

 formula: Model formula specifying a single binary variable

  design: survey design object

  method: See Details below. Partial matching is done on the argument.

   level: Confidence level for interval

     ...: for future methods

_D_e_t_a_i_l_s:

     The '"logit"' method fits a logistic regression model and computes
     a Wald-type interval on the log-odds scale, which is then
     transformed to the probability scale.

     The '"likelihood"' method uses the (Rao-Scott) scaled chi-squared
     distribution for the loglikelihood from a binomial distribution.

     The '"asin"' method uses the variance-stabilising transformation
     for the binomial distribution, the arcsine square root, and then
     back-transforms the interval to the probability scale

     The '"beta"' method uses the incomplete beta function as in
     'binom.test', with an effective sample size based on the estimated
     variance of the proportion. (Korn and Graubard, 1998)

     The '"mean"' method is a Wald-type interval on the probability
     scale, the same as 'confint(svymean())'

     All methods undercover for probabilities close enough to zero or
     one, but '"beta"', '"likelihood"' and '"logit"' are noticeably
     better than the other two. None of the methods will work when the
     observed proportion is exactly 0 or 1.

     The 'confint' method extracts the confidence interval; the 'vcov'
     and 'SE' methods just report the variance or standard error of the
     mean.

_V_a_l_u_e:

     The point estimate of the proportion, with the confidence interval
     as an attribute

_R_e_f_e_r_e_n_c_e_s:

     Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway
     Contingency Tables with Proportions Estimated From Survey Data"
     Annals of Statistics 12:46-60.

     Korn EL, Graubard BI. (1998) Confidence Intervals For Proportions
     With Small Expected Number of Positive Counts Estimated From
     Survey Data. Survey Methodology 23:193-201.

_S_e_e _A_l_s_o:

     'svymean'

_E_x_a_m_p_l_e_s:

     data(api)
     dclus1<-svydesign(id=~dnum, fpc=~fpc, data=apiclus1)

     svyciprop(~I(ell==0), dclus1, method="li")
     svyciprop(~I(ell==0), dclus1, method="lo")
     svyciprop(~I(ell==0), dclus1, method="as")
     svyciprop(~I(ell==0), dclus1, method="be")
     svyciprop(~I(ell==0), dclus1, method="me")

