{smcl} {* Help for -scdensity- version 1.0.1 16January2013, Joerg Luedicke}{...} {hline} help {hi:scdensity} {hline} {title:Univariate self-consistent density estimation} {title:Syntax} {p 8 17 2} {cmd:scdensity} {varname} {ifin} [ {cmd:,} {opt n(#)} {opt r:ange(# #)} {opt ex:pand} {opt at(var_x)} {opt cor:rection} {opt gtd} {opt tol:erance(#)} {opt ini:tial(#)} {opt inter:val(#)} {opt g:enerate(newvar1 [newvar2])} {opt nog:raph} {it:{help twoway_options}} ] {title:Description} {p 4 4 2} {cmd:scdensity} is an implementation of the self-consistent density estimator as described in Bernacchia & Pigolotti (2011). The self-consistent method nonparametrically estimates a probability density function from a number of data points, without relying on any a-priori fixation of parameters like smoothing parameters in kernel density estimation, for example. {title:Options} {dlgtab:Estimation} {p 4 8 2}{cmd:n(}#{cmd:)} Number of grid points to be used at which the density is evaluated. If the number of data points is greater than N=1,000, the number of grid points defaults to n=1,000. If the number of data points is lower than N=1,000, the number of grid points defaults to n=N. If a number larger than the actual sample size is requested, n is set to N. {p 4 8 2}{cmdab:r:ange(}# #{cmd:)} Defines the grid range at which the density is to be evaluated. Per default, the endpoints of the evaluation grid are determined by the minimum and maximum value of the actual data points and the {cmdab:r:ange()} option can be used to change this default behavior. The input of two numbers is required with the first one being the minimum and the second one being the maximum of the range. {p 4 8 2}{cmdab:ex:pand} Expands the evaluation grid. {cmd:scdensity}'s default is to use the endpoints of the data range as grid end points. If the {cmd:expand} option is used, the grid range is expanded at both ends as a function of sample size. Let the width of the data range be w = max(x) - min(x) where x are the data points, then the expanded range r_e is defined by min(r_e) = min(x) - 0.5 * N^-0.3 * w and max(r_e) = max(x) + 0.5 * N^-0.3 * w, with N being the sample size of x. {p 4 8 2}{cmd:at(}{it:var_x}{cmd:)} Evaluates the density at {it:var_x}. Note that if {it:var_x} is not a regular grid of points, densities that contain negative values cannot be corrected. {dlgtab:Density correction} {p 4 8 2}{cmdab:cor:rection} Correction of the density to be strictly non-negative. The unique and well-identified value {it:xi} is found such that the positive part of the density integrates to 1 (plus tolerance) when {it:xi} is subtracted from the density, after which the negative part is set to zero. This approach is described in Glad et al. (2003). The used search algorithm starts with an initial ballpark estimate of {it:xi_i} divided by 10 where {it:xi_i} is derived by integrating over the positive part of the density to determine the excess probability mass, divided by the product of the number of grid points with non-negative density values and {it:dx}, the distance between points of the regular grid. The search interval defaults to {it:xi_i}/10/s, where s=1/tolerance/100 and is thus proportional to the specified tolerance to assure a sufficiently small resolution of the interval with respect to the tolerance resolution. The tolerance defaults to 0.0001. All defaults can be changed using the {cmd:tolerance()}, {cmd:initial()}, and {cmd:interval()} options. Changing the {cmd:initial()} and {cmd:interval()} options will rarely be needed. {p 4 8 2}{cmd:gtd} The default algorithm usually finds {it:xi} fast and reliably. Theoretically, however, it could happen that it does not find {it:xi} in which case an alternative algorithm can be used by specifying the {cmd:gtd} option. With this alternative algorithm, {it:xi} is found surely but it can take a substantial amount of time, especially for very small tolerance values. {p 4 8 2}{cmdab:tol:erance(}#{cmd:)} Change the default tolerance. {p 4 8 2}{cmdab:ini:tial(}#{cmd:)} Change the initial value of {it:xi} at which the search is started. {p 4 8 2}{cmdab:inter:val(}#{cmd:)} Change the default search interval. {dlgtab:Storing and graph options} {p 4 8 2} {cmdab:g:enerate(}{it:d} {it:[x]}{cmd:)} Store the density estimate in {it:{help newvar}} {it:d} and the evaluation grid in {it:{help newvar}} {it:x}. {p 4 8 2}{cmdab:nog:raph} Suppress the graph. {p 4 8 2}{it:{help twoway_options}} Any options other than {opt by()} documented in {bind:{bf:[G] {it:twoway_options}}}. {title:Saved results} {pstd} {cmd:scdensity} saves the following in {cmd:r()}: {synoptset 15 tabbed}{...} {p2col 5 15 19 2: Scalars}{p_end} {synopt:{cmd:r(n_data)}}Number of data points{p_end} {synopt:{cmd:r(n_points)}}Number of evaluation points{p_end} {synopt:{cmd:r(range_min)}}Minimum grid point{p_end} {synopt:{cmd:r(range_max)}}Maximum grid point{p_end} {title:Dependencies} {p 4 8 2} {cmd:scdensity} requires {cmd:moremata} (Jann 2005), available from SSC. Type -ssc install moremata- in Stata to obtain it. Also, {cmd:scdensity} requires Stata version 9.2 or higher. {title:Examples} {p 4 8 2}{inp:. webuse head} {p 4 8 2}{inp:. scdensity jaw} {p 4 8 2}{inp:. scdensity jaw, correction expand} {title:References} {p 4 4 2} Bernacchia, A. & Pigolotti, S. (2011). Self-Consistent Method For Density Estimation {it:Journal of the Royal Statistical Society B, 73(3)}, 407-422. {p 4 4 2} Glad, I.K., Hjort, N.L. & Ushakov, N.G. (2003). Correction of Density Estimators that are not Densities {it: Scandinavian Journal of Statistics, 30}, 415-427. {p 4 4 2} Jann, B. (2005). "MOREMATA: Stata module (Mata) to provide various functions," {it:Statistical Software Components S455001, Boston College Department of Economics}, revised 19 Feb 2009. {title:Acknowledgment} {p 4 4 2} I am thankful to Alberto Bernacchia for helpful discussions and sharing his R code. {title:Author} {p 4 4 2}Joerg Luedicke{break} Yale University and University of Florida{break} United States{break} email: joerg.luedicke@ufl.edu {title:Also see} {p 4 13 2}Help {help scdcor}, {help kdensity}, {help kdens} (if installed)