UPSnnltd                package:USPS                R Documentation

_N_e_a_r_e_s_t _N_e_i_g_h_b_o_r _D_i_s_t_r_i_b_u_t_i_o_n _o_f _L_T_D_s _i_n _U_n_s_u_p_e_r_v_i_s_e_d _P_r_o_p_e_n_s_i_y _S_c_o_r_i_n_g

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

     For a given number of patient clusters in baseline X-covariate
     space, UPSnnltd() characterizes the distribution of Nearest
     Neighbor "Local Treatemnt Differences" (LTDs) on a specified
     Y-outcome variable.

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

       nnobj <- UPSnnltd(numclust)

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

numclust: Number of clusters in baseline X-covariate space.

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

     Multiple calls to UPSnnltd(n) for varying numbers of clusters, n,
     are typically made after first invoking UPShclus() to
     hierarchically cluster patients in X-space and then invoking
     UPSaccum() to specify a Y outcome variable and a two-level
     treatment factor t. UPSnnltd(n) then determines the LATE
     Difference Distribution corresponding to n clusters and,
     optionally, displays this distribution in a "Snowball" plot.

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

     An output list object of class UPSnnltd: 

  hiclus: Name of clustering object created by UPShclus().

  dframe: Name of data.frame containing X, t & Y variables.

    trtm: Name of treatment factor variable.

    yvar: Name of outcome Y variable.

numclust: Number of clusters requested.

actclust: Number of clusters actually produced.

  scedas: Scedasticity assumption: "homo" or "hete"

  PStdif: Character string describing the treatment difference.

nnhbindf: Vector containing cluster number for each patient. 

 rawmean: Unadjusted outcome mean by treatment group.

 rawvars: Unadjusted outcome variance by treatment group.

 rawfreq: Number of patients by treatment group.

  ratdif: Unadjusted mean outcome difference between treatments.

  ratsde: Standard error of unadjusted mean treatment difference.

 binmean: Unadjusted mean outcome by cluster and treatment.

 binvars: Unadjusted variance by cluster and treatment.

 binfreq: Number of patients by bin and treatment.

  awbdif: Across cluster average difference with cluster size weights.

  awbsde: Standard error of awbdif.

  wwbdif: Across cluster average difference, inverse variance weights.

  wwbsde: Standard error of wwbdif.

  faclev: Maximum number of different numerical values an outcome
          variable can assume without automatically being converted
          into a "factor" variable; faclev=1 causes a binary indicator
          to be treated as a continuous variable determining an average
          or proportion.

 youtype: "contin"uous => only next eight outputs; "factor" => only
          last three outputs.

 aovdiff: ANOVA summary for treatment main effect only.

   form2: Formula for outcome differences due to bins and to treatment
          nested within bins.

 bindiff: ANOVA summary for treatment nested within cluster.

    sig2: Estimate of error mean square in nested model.

 pbindif: Unadjusted treatment difference by cluster.

 pbinsde: Standard error of the unadjusted difference by cluster.

 pbinsiz: Cluster radii measure: square root of total number of
          patients.

  symsiz: Symbol size of largest possible Snowball in a UPSnnltd() plot
          with 1 cluster.

  factab: Marginal table of counts by Y-factor level and treatment.

  cumchi: Cumulative Chi-Square statistic for interaction in the
          three-way, nested table.

   cumdf: Degrees of-Freedom for the Cumulative Chi-Squared.

_A_u_t_h_o_r(_s):

     Bob Obenchain <sunsetstats@earthlink.net>

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

     Obenchain RL. (2004) Unsupervised Propensity Scoring: NN and IV
     Plots. _Proceedings of the American Statistical Association (on
     CD)_ 8 pages.

     Obenchain RL. (2005) *USPSinR.pdf*  ../R_HOME/library/USPS  40
     pages.

     Rosenbaum PR, Rubin RB. (1983) The Central Role of the Propensity
     Score in Observational Studies for Causal Effects. _Biometrika_
     *70*: 41-55.

     Rubin DB. (1980) Bias reduction using Mahalanobis metric matching.
     _Biometrics_ *36*: 293-298.

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

     'UPSivadj', 'UPSaccum' and 'UPSgraph'.

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

         data(lindner)
         UPSxvars <- c("stent", "height", "female", "diabetic", "acutemi", "ejecfrac", "ves1proc")
         UPSharch <- UPShclus(lindner, UPSxvars)
         UPSaccum(UPSharch, lindner, abcix, lifepres, faclev=1, scedas="homo", accobj="ABClife")
         
         lif070nn <- UPSnnltd(70)
         lif070nn
         plot(lif070nn)

