| Title: | Tests for Outliers |
|---|---|
| Description: | A collection of some tests commonly used for identifying outliers. |
| Authors: | Lukasz Komsta <[email protected]> |
| Maintainer: | Lukasz Komsta <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.15 |
| Built: | 2024-11-01 11:28:22 UTC |
| Source: | https://github.com/cran/outliers |
Performs a chisquared test for detection of one outlier in a vector.
chisq.out.test(x, variance=var(x), opposite = FALSE)chisq.out.test(x, variance=var(x), opposite = FALSE)
x |
a numeric vector for data values. |
variance |
known variance of population. if not given, estimator from sample is taken, but there is not so much sense in such test (it is similar to z-scores) |
opposite |
a logical indicating whether you want to check not the value with largest difference from the mean, but opposite (lowest, if most suspicious is highest etc.) |
This function performs a simple test for one outlier, based on chisquared distribution of squared differences between data and sample mean. It assumes known variance of population. It is rather not recommended today for routine use, because several more powerful tests are implemented (see other functions mentioned below). It was discussed by Dixon (1950) for the first time, as one of the tests taken into account by him.
A list with class htest containing the following components:
statistic |
the value of chisquared-statistic. |
p.value |
the p-value for the test. |
alternative |
a character string describing the alternative hypothesis. |
method |
a character string indicating what type of test was performed. |
data.name |
name of the data argument. |
This test is known to reject only extreme outliers, if no known variance is specified.
Lukasz Komsta
Dixon, W.J. (1950). Analysis of extreme values. Ann. Math. Stat. 21, 4, 488-506.
set.seed(1234) x = rnorm(10) chisq.out.test(x) chisq.out.test(x,opposite=TRUE)set.seed(1234) x = rnorm(10) chisq.out.test(x) chisq.out.test(x,opposite=TRUE)
This test is useful to check if largest variance in several groups of data is "outlying" and this group should be rejected. Alternatively, if one group has very small variance, we can test for "inlying" variance.
cochran.test(object, data, inlying = FALSE)cochran.test(object, data, inlying = FALSE)
object |
A vector of variances or formula. |
data |
If object is a vector, data should be another vector, giving number of data in each corresponding group. If object is a formula, data should be a dataframe. |
inlying |
Test smallest variance instead of largest. |
The corresponding p-value is calculated using pcochran function.
A list with class htest containing the following components:
statistic |
the value of Cochran-statistic. |
p.value |
the p-value for the test. |
alternative |
a character string describing the alternative hypothesis. |
method |
a character string indicating what type of test was performed. |
data.name |
name of the data argument. |
estimate |
vector of variance estimates |
Lukasz Komsta
Snedecor, G.W., Cochran, W.G. (1980). Statistical Methods (seventh edition). Iowa State University Press, Ames, Iowa.
set.seed(1234) x=rnorm(100) d=data.frame(x=x,group=rep(1:10,10)) cochran.test(x~group,d) cochran.test(x~group,d,inlying=TRUE) x=runif(5) cochran.test(x,rep(5,5)) cochran.test(x,rep(100,5))set.seed(1234) x=rnorm(100) d=data.frame(x=x,group=rep(1:10,10)) cochran.test(x~group,d) cochran.test(x~group,d,inlying=TRUE) x=runif(5) cochran.test(x,rep(5,5)) cochran.test(x,rep(100,5))
Performs several variants of Dixon test for detecting outlier in data sample.
dixon.test(x, type = 0, opposite = FALSE, two.sided = TRUE)dixon.test(x, type = 0, opposite = FALSE, two.sided = TRUE)
x |
a numeric vector for data values. |
opposite |
a logical indicating whether you want to check not the value with largest difference from the mean, but opposite (lowest, if most suspicious is highest etc.) |
type |
an integer specyfying the variant of test to be performed. Possible values are
compliant with these given by Dixon (1950): 10, 11, 12, 20, 21. If this value is set to zero,
a variant of the test is chosen according to sample size (10 for 3-7, 11 for 8-10, 21 for 11-13,
22 for 14 and more). The lowest or highest value is selected automatically, and can be reversed
used |
two.sided |
treat test as two-sided (default). |
The p-value is calculating by interpolation using qdixon and qtable.
According to Dixon (1951) conclusions, the critical values can be obtained numerically only for n=3.
Other critical values are obtained by simulations, taken from original Dixon's paper, and
regarding corrections given by Rorabacher (1991).
A list with class htest containing the following components:
statistic |
the value of Dixon Q-statistic. |
p.value |
the p-value for the test. |
alternative |
a character string describing the alternative hypothesis. |
method |
a character string indicating what type of test was performed. |
data.name |
name of the data argument. |
Lukasz Komsta
Dixon, W.J. (1950). Analysis of extreme values. Ann. Math. Stat. 21, 4, 488-506.
Dixon, W.J. (1951). Ratios involving extreme values. Ann. Math. Stat. 22, 1, 68-78.
Rorabacher, D.B. (1991). Statistical Treatment for Rejection of Deviant Values: Critical Values of Dixon Q Parameter and Related Subrange Ratios at the 95 percent Confidence Level. Anal. Chem. 83, 2, 139-146.
set.seed(1234) x = rnorm(10) dixon.test(x) dixon.test(x,opposite=TRUE) dixon.test(x,type=10)set.seed(1234) x = rnorm(10) dixon.test(x) dixon.test(x,opposite=TRUE) dixon.test(x,type=10)
Performs Grubbs' test for one outlier, two outliers on one tail, or two outliers on opposite tails, in small sample.
grubbs.test(x, type = 10, opposite = FALSE, two.sided = FALSE)grubbs.test(x, type = 10, opposite = FALSE, two.sided = FALSE)
x |
a numeric vector for data values. |
opposite |
a logical indicating whether you want to check not the value with largest difference from the mean, but opposite (lowest, if most suspicious is highest etc.) |
type |
Integer value indicating test variant. 10 is a test for one outlier (side is
detected automatically and can be reversed by |
two.sided |
Logical value indicating if there is a need to treat this test as two-sided. |
The function can perform three tests given and discussed by Grubbs (1950).
First test (10) is used to detect if the sample dataset contains one outlier, statistically different than the other values. Test is based by calculating score of this outlier G (outlier minus mean and divided by sd) and comparing it to appropriate critical values. Alternative method is calculating ratio of variances of two datasets - full dataset and dataset without outlier. The obtained value called U is bound with G by simple formula.
Second test (11) is used to check if lowest and highest value are two outliers on opposite tails of sample. It is based on calculation of ratio of range to standard deviation of the sample.
Third test (20) calculates ratio of variance of full sample and sample without two extreme observations. It is used to detect if dataset contains two outliers on the same tail.
The p-values are calculated using qgrubbs function.
statistic |
the value statistic. For type 10 it is difference between outlier and the mean divided by standard deviation, and for type 20 it is sample range divided by standard deviation. Additional value U is ratio of sample variances with and withour suspicious outlier. According to Grubbs (1950) these values for type 10 are bound by simple formula and only one of them can be used, but function gives both. For type 20 the G is the same as U. |
p.value |
the p-value for the test. |
alternative |
a character string describing the alternative hypothesis. |
method |
a character string indicating what type of test was performed. |
data.name |
name of the data argument. |
Lukasz Komsta
Grubbs, F.E. (1950). Sample Criteria for testing outlying observations. Ann. Math. Stat. 21, 1, 27-58.
set.seed(1234) x = rnorm(10) grubbs.test(x) grubbs.test(x,type=20) grubbs.test(x,type=11)set.seed(1234) x = rnorm(10) grubbs.test(x) grubbs.test(x,type=20) grubbs.test(x,type=11)
Finds value with largest difference between it and sample mean, which can be an outlier.
outlier(x, opposite = FALSE, logical = FALSE)outlier(x, opposite = FALSE, logical = FALSE)
x |
a data sample, vector in most cases. If argument is a dataframe, then outlier is calculated for
each column by |
opposite |
if set to TRUE, gives opposite value (if largest value has maximum difference from the mean, it gives smallest and vice versa) |
logical |
if set to TRUE, gives vector of logical values, and possible outlier position is marked by TRUE |
A vector of value(s) with largest difference from the mean.
Lukasz Komsta, corrections by Markus Graube
set.seed(1234) y=rnorm(100) outlier(y) outlier(y,opposite=TRUE) dim(y) <- c(20,5) outlier(y) outlier(y,opposite=TRUE)set.seed(1234) y=rnorm(100) outlier(y) outlier(y,opposite=TRUE) dim(y) <- c(20,5) outlier(y) outlier(y,opposite=TRUE)
This functions calculates quantiles (critical values) and reversively p-values for Cochran test for outlying variance.
qcochran(p, n, k) pcochran(q, n, k)qcochran(p, n, k) pcochran(q, n, k)
p |
vector of probabilities. |
q |
vector of quantiles. |
n |
number of values in each group (if not equal, use arithmetic mean). |
k |
number of groups. |
Vector of p-values or critical values.
Lukasz Komsta
Snedecor, G.W., Cochran, W.G. (1980). Statistical Methods (seventh edition). Iowa State University Press, Ames, Iowa.
qcochran(0.05,5,5) pcochran(0.293,5,5)qcochran(0.05,5,5) pcochran(0.293,5,5)
Approximated quantiles (critical values) and distribution function (giving p-values) for Dixon tests for outliers.
qdixon(p, n, type = 10, rev = FALSE) pdixon(q, n, type = 10)qdixon(p, n, type = 10, rev = FALSE) pdixon(q, n, type = 10)
p |
vector of probabilities. |
q |
vector of quantiles. |
n |
length of sample. |
type |
integer value: 10, 11, 12, 20, or 21. For description see |
rev |
function |
This function is based on tabularized Dixon distribution, given by Dixon (1950) and corrected
by Rorabacher (1991). Continuity is reached due to smart interpolation using qtable function.
By now, numerical procedure to obtain these values for n>3 is not known.
Critical value or p-value (vector).
Lukasz Komsta
Dixon, W.J. (1950). Analysis of extreme values. Ann. Math. Stat. 21, 4, 488-506.
Dixon, W.J. (1951). Ratios involving extreme values. Ann. Math. Stat. 22, 1, 68-78.
Rorabacher, D.B. (1991). Statistical Treatment for Rejection of Deviant Values: Critical Values of Dixon Q Parameter and Related Subrange Ratios at the 95 percent Confidence Level. Anal. Chem. 83, 2, 139-146.
This function is designed to calculate critical values for Grubbs tests for outliers detecting and to approximate p-values reversively.
qgrubbs(p, n, type = 10, rev = FALSE) pgrubbs(q, n, type = 10)qgrubbs(p, n, type = 10, rev = FALSE) pgrubbs(q, n, type = 10)
p |
vector of probabilities. |
q |
vector of quantiles. |
n |
sample size. |
type |
Integer value indicating test variant. 10 is a test for one outlier (side is
detected automatically and can be reversed by |
rev |
if set to TRUE, function |
The critical values for test for one outlier is calculated according to approximations given by Pearson and Sekar (1936). The formula is simply reversed to obtain p-value.
The values for two outliers test (on opposite sides) are calculated according to David, Hartley,
and Pearson (1954). Their formula cannot be rearranged to obtain p-value, thus such values are
obtained by uniroot.
For test checking presence of two outliers at one tail, the tabularized distribution (Grubbs, 1950)
is used, and approximations of p-values are interpolated using qtable.
A vector of quantiles or p-values.
Lukasz Komsta
Grubbs, F.E. (1950). Sample Criteria for testing outlying observations. Ann. Math. Stat. 21, 1, 27-58.
Pearson, E.S., Sekar, C.C. (1936). The efficiency of statistical tools and a criterion for the rejection of outlying observations. Biometrika, 28, 3, 308-320.
David, H.A, Hartley, H.O., Pearson, E.S. (1954). The distribution of the ratio, in a single normal sample, of range to standard deviation. Biometrika, 41, 3, 482-493.
This function calculates critical values or p-values which cannot be obtained numerically, and only tabularized version is available.
qtable(p, probs, quants)qtable(p, probs, quants)
p |
vector of probabilities. |
probs |
vector of given probabilities. |
quants |
vector of given corresponding quantiles. |
This function is internal routine used to obtain Grubbs and Dixon critical values. It fits linear or cubical regression to closests values of its argument, then uses obtained function to obtain quantile by interpolation.
A vector of interpolated values
You can simply do "reverse" interpolation (p-value calculating) by reversing probabilities and quantiles (2 and 3 argument).
Lukasz Komsta
If the outlier is detected and confirmed by statistical tests, this function can remove it or replace by sample mean or median.
rm.outlier(x, fill = FALSE, median = FALSE, opposite = FALSE)rm.outlier(x, fill = FALSE, median = FALSE, opposite = FALSE)
x |
a dataset, most frequently a vector. If argument is a dataframe, then outlier is removed
from each column by |
fill |
If set to TRUE, the median or mean is placed instead of outlier. Otherwise, the outlier(s) is/are simply removed. |
median |
If set to TRUE, median is used instead of mean in outlier replacement. |
opposite |
if set to TRUE, gives opposite value (if largest value has maximum difference from the mean, it gives smallest and vice versa) |
A dataset of the same type as argument, with outlier(s) removed or replacement by appropriate means or medians.
Lukasz Komsta
set.seed(1234) y=rnorm(100) outlier(y) outlier(y,opposite=TRUE) rm.outlier(y) rm.outlier(y,opposite=TRUE) dim(y) <- c(20,5) outlier(y) outlier(y,logical=TRUE) outlier(y,logical=TRUE,opposite=TRUE) rm.outlier(y) rm.outlier(y,opposite=TRUE)set.seed(1234) y=rnorm(100) outlier(y) outlier(y,opposite=TRUE) rm.outlier(y) rm.outlier(y,opposite=TRUE) dim(y) <- c(20,5) outlier(y) outlier(y,logical=TRUE) outlier(y,logical=TRUE,opposite=TRUE) rm.outlier(y) rm.outlier(y,opposite=TRUE)
This function calculates normal, t, chi-squared, IQR and MAD scores of given data.
scores(x, type = c("z", "t", "chisq", "iqr", "mad"), prob = NA, lim = NA)scores(x, type = c("z", "t", "chisq", "iqr", "mad"), prob = NA, lim = NA)
x |
a vector of data. |
type |
"z" calculates normal scores (differences between each value and the mean
divided by sd), "t" calculates t-Student scores (transformed by |
prob |
If set, the corresponding p-values instead of scores are given. If value is set to 1, p-value are returned. Otherwise, a logical vector is formed, indicating which values are exceeding specified probability. In "z" and "mad" types, there is also possibility to set this value to zero, and then scores are confirmed to (n-1)/sqrt(n) value, according to Shiffler (1998). The "iqr" type does not support probabilities, but "lim" value can be specified. |
lim |
This value can be set for "iqr" type of scores, to form logical vector, which values has this limit exceeded. |
A vector of scores, probabilities, or logical vector.
Lukasz Komsta, corrections by Alan Richter
Schiffler, R.E (1998). Maximum Z scores and outliers. Am. Stat. 42, 1, 79-80.
mad, IQR, grubbs.test,
set.seed(1234) x = rnorm(10) scores(x) scores(x,prob=1) scores(x,prob=0.5) scores(x,prob=0.1) scores(x,prob=0.93) scores(x,type="iqr") scores(x,type="mad") scores(x,prob=0)set.seed(1234) x = rnorm(10) scores(x) scores(x,prob=1) scores(x,prob=0.5) scores(x,prob=0.1) scores(x,prob=0.93) scores(x,type="iqr") scores(x,type="mad") scores(x,prob=0)