The number for the option quantile () has to lie between. Confidence Intervals with ci and centileA quantile regression can be implemented in STATA quite easily with the following command: qreg y x1 x2, quantile ( 0.25) The above command executes the quantile regression of the dependent variable y on the explanatory variables x1 and x2 for the 25th percentile of the distribution of y. Alternatively, I can obtain percentiles with the. Stata conveniently provides these descriptive statistics with the summarize commands detail option. You can’t score better or worse than yourself so 0th and 100th percentile doesn’t exist.
For instance, you may remove 5 per cent of the lowest and 5 per cent of the highest values.Winsorizing works differently: The values at the tails of the distribution are not removed, but are recoded to less extreme values. That is, a percentage of the lowest and (normally an equal percentage of) the highest values of a variable are removed from the data when computing the mean. Trimming and winsorizing are procedures that may help to assess the magnitude of such influences and to possibly arrive at measures that are subject to such influences to a lesser degree.Trimming means discarding values at the tails of the distribution. Multiple Imputation: Analysis and Pooling StepsIt is generally known that the mean (typically we have the arithmetic mean in mind) may be heavily influenced by outlying values.
Note that removing 50 per cent on each tail will not be done literally rather, the value 'in the middle', i.e. 50 per cent of the cases on each tail of the distribution and show the means computed on each of the trimmed samples. Thus,Will remove 0, 5, 10. You may indicate single values, several values (value lists) or starting and ending points with an increment. TrimmeanThis procedure basically works like this: You inform Stata about percentages or (absolute) numbers of cases to be removed, and Stata reports the means computed based on the trimmed values.
+-+Some options are available, among which ci adds standard errors and confidence intervals to the means. Therefore, the untrimmed mean is much higher than any trimmed mean. The variable investigated is very skewed more than 50 per cent of the values are exactly 1, the 75th percentile is 3, the 90th percentile is 13, and the maximum is almost 400. Likewise,Will successively remove 100, 200, 300 and finally 500 cases on each tail of the distribution and compute the means.The following table was produced with the help of the command shown above with the percent option.
Cox Yujun Lian seemingly used the code and expanded the file to create winsor2 (see ). WinsorizingIn contrast to the trimming procedures described above, winsorizing transforms your current working dataset by creating new ("winsorized") variables that can be used for further analysis.The winsor ado file was written by Nicholas J. The simplest version isOptions include by() to plot the means for subgroups defined by a variable that is indicated within the parentheses, or p, which will request Stata to display the percentage of removed cases on the x axis instead of the absolute number of cases.
As you can see, you are not required to winsorize an equal number of cases at each tail.Winsor income, trim cuts(5 80) suffix(_tr)Will trim variable income (at the same percentiles as before) and write the resulting variable to variable "income_tr".© W. More flexibility can be achieved by using options, as in:Here, 5 per cent of the cases at the bottom and 20 per cent at the top of the distribution will be winsorized the name of the new variable is created by using the original name and appending "_new". Winsor2This procedure may be invoked without using any options in this case, 1 per cent at each tail of the distribution will be winsorized and the resulting variable will be written to a variable the name of which is derived from the original variable name by adding "_w" at the end. The other option indicates the name of an as yet nonextant variable to which the winsorized values will be written.Will recode the bottom and the top 10 per cent of the cases in variable 'income' to the values corresponding to the 10th and the 90th percentile, respectively, and write the result to variable inc_w10.Will recode the bottom and the top 100 cases to the values of the largest (at the bottom) and the smallest (at the top) of these cases, respectively, and write the result to variable inc_w10. Furthermore, this procedure can be used to trim a variable.Both ado files can be installed from ssc:This procedure requires two options: One option informs Stata about the number or the percentage of cases to be modified in each tail this translates into h() followed by a number that is at least 1 and not larger than half of the cases, or p() followed by a fraction larger than 0 and smaller than. In particular, winsor2 allows to replace an extant variable by its winsorized version, but it also allows to 'winsorize' different numbers (or percentages) of cases on both ends of the distribution.
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