# Winsorizing in r

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Save preferences.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. That value should be replaced by the second highest or second lowest value in the same column, without loosing the rest of the details in the observation.

For example.

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Therefore, only 2 values per column will be changes, the highest and the minimum, the rest will remain the same. Any advice? If you data are in a data frame datthen we can windsoroize the data using your procedure via:. Note the function will only work if there is one minimum and maximum values respectively in each column owing to the way which. If you have multiple entries taking the same max or min value then we would need something different:.

Same with the top. Here you're just choosing the top and bottom value, but winsorizing usually involves specifying a percentage of values at the top and bottom to replace. Strictly speaking, "winsorization" is the act of replacing the most extreme data points with an acceptable percentile as mentioned in some of the other answers.

One fairly standard R function to do this is winsor from the psych package. Note that this might make the extreme data equal to a percentile number which may or may not exist in your data set: the theoretical n-th percentile of the data.

The statar package works very well for this. Copying the relevant snippet from the readme file:. Learn more. Winsorize dataframe Ask Question. Asked 9 years, 4 months ago. Active 2 years, 11 months ago. Viewed 7k times. This isn't quite making sense.

Your before and after max value are the same. I'm also perplexed by the use of plyr. Are you doing some type of grouping by date? Plyr is a tool for splitting, applying and combining.

**Viewing series, Missing Data, Outliers**

I can't tell what your split is here. JD Long. This is just a subset of the actual dataframe. The actual dataframe includes another column with different values. Different "events" are grouped by a combination of two variables, hence, plyr is useful in my case. I did a mistake in my description, the before and after values are not the same.

Thank you for your comment, and sorry for the confusion. Active Oldest Votes. Gavin Simpson Gavin Simpson k 25 25 gold badges silver badges bronze badges. Works exactly as needed.

It is obvious now that I know the answer! PatrickT PatrickT 6, 5 5 gold badges 54 54 silver badges 90 90 bronze badges.Winsorized mean is a method of averaging that initially replaces the smallest and largest values with the observations closest to them.

This is done to limit the effect of abnormal extreme values, or outliers, on the calculation. After replacing the values, arithmetic mean formula is then used to calculate the winsorized mean.

Winsorized means are expressed in two ways. A "k n " winsorized mean refers to the replacement of the 'k' smallest and largest observations, where 'k' is an integer. The winsorized mean is calculated by replacing the smallest and largest data points, then summing all the data points and dividing the sum by the total number of data points.

The winsorized mean is less sensitive to outliers because it can replace them with less extreme values. That is, it is less susceptible to outlines versus the mean.

However, if a distribution has fat tails, the effect of removing the highest and lowest values in the distribution will have little influence because of the high number of variability in the distribution figures. One can calculate the winsorized mean for the following data set: 1, 5, 7, 8, 9, 10, In this example, we assume the winsorized mean is in the first order, we replace the smallest and largest values with their nearest observations. The dataset now appears as follows: 5, 5, 7, 8, 9, 10, Taking an arithmetic average of the new set produces a winsorized mean of 7.

We will winsorize the following data set: 2, 4, 7, 8, 11, 14, 18, 23, 23, 27, 35, 40, 49, 50, 55, 60, 61, 61, 62, Thus, the new data set is: 7, 7, 7, 8, 11, 14, 18, 23, 23, 27, 35, 40, 49, 50, 55, 60, 61, 61, 61, The winsorized mean is The winsorized mean includes modifying data points, while the trimmed mean involves removing data points. One major downside for winsorized means is that they introduce bias into the data set. Granted, the data set is ideally less biased after the modification than if outliers were left in.

For related insight, read more about the differences between key mean calculations. Financial Ratios. Financial Analysis. Tools for Fundamental Analysis. Your Money.Clean data by means of winsorization, i. For the "data.

For the other methods, additional arguments to be passed down to robStandardize. Ignored if standardized is TRUE. Possible values are "data" for returning the cleaned data, or "weights" for returning data cleaning weights. The borders of the main part of the data are defined on the scale of the robustly standardized data. In the multivariate case, a normal distribution is assumed and the data are shrunken towards the boundary of a tolerance ellipse with coverage probability prob.

If standardize is TRUE and return is "weights"a set of data cleaning weights. Multiplying each observation of the standardized data by the corresponding weight yields the cleaned standardized data.

Otherwise an object of the same type as the original data x containing the cleaned data is returned. Data cleaning weights are only meaningful for standardized data. In the general case, the data need to be standardized first, then the data cleaning weights can be computed and applied to the standardized data, after which the cleaned standardized data need to be backtransformed to the original scale.

Khan, J. Journal of the American Statistical Association, Created by DataCamp. Data cleaning by winsorization Clean data by means of winsorization, i. Community examples Looks like there are no examples yet. Post a new example: Submit your example. API documentation. Put your R skills to the test Start Now.Winsorizing or winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers.

It is named after the engineer-turned-biostatistician Charles P. Winsor — The effect is the same as clipping in signal processing. The distribution of many statistics can be heavily influenced by outliers. Winsorized estimators are usually more robust to outliers than their more standard forms, although there are alternatives, such as trimmingthat will achieve a similar effect. Values shown in bold. R can winsorize data using the DescTools package:. Note that winsorizing is not equivalent to simply excluding data, which is a simpler procedure, called trimming or truncationbut is a method of censoring data.

In a trimmed estimator, the extreme values are discarded; in a winsorized estimator, the extreme values are instead replaced by certain percentiles the trimmed minimum and maximum. Thus a winsorized mean is not the same as a truncated mean.

In the previous example the trimmed mean would be obtained from the smaller set:. More formally, they are distinct because the order statistics are not independent. From Wikipedia, the free encyclopedia. Redirected from Winsorising. Categories : Statistical data transformation Robust statistics. Hidden categories: Articles with example Python programming language code Articles with example R code. Namespaces Article Talk. Views Read Edit View history.

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### Winsorized Mean Definition

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