aout.nbinom: Find alpha-outliers in negative Binomial data
In alphaOutlier: Obtain Alpha-Outlier Regions for Well-Known Probability Distributions
Description
Usage
Arguments
Value
Author(s)
See Also
Examples
Description
Given the parameters of a negative Binomial distribution, aout.nbinom identifies α-outliers in a given data set.
Usage
1
aout.nbinom(data, param, alpha = 0.1, hide.outliers = FALSE)
Arguments
data
a vector. The data set to be examined.
param
a vector. Contains the parameters of the negative Binomial distribution: N, p.
alpha
an atomic vector. Determines the maximum amount of probability mass the outlier region may contain. Defaults to 0.1.
hide.outliers
boolean. Returns the outlier-free data if set to TRUE. Defaults to FALSE.
Value
Data frame of the input data and an index named is.outlier that flags the outliers with TRUE. If hide.outliers is set to TRUE, a simple vector of the outlier-free data.
Author(s)
A. Rehage
See Also
Examples
1
2
data(daysabs)
aout.nbinom(daysabs, c(8, 0.6), 0.05)
Example output
Loading required package: Rsolnp
Loading required package: nleqslv
Loading required package: quantreg
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
data is.outlier
1 4 FALSE
2 4 FALSE
3 2 FALSE
4 3 FALSE
5 3 FALSE
6 13 TRUE
7 11 FALSE
8 7 FALSE
9 10 FALSE
10 9 FALSE
11 4 FALSE
12 5 FALSE
13 5 FALSE
14 6 FALSE
15 1 FALSE
16 0 FALSE
17 1 FALSE
18 0 FALSE
19 5 FALSE
20 24 TRUE
21 2 FALSE
22 0 FALSE
23 1 FALSE
24 0 FALSE
25 8 FALSE
26 6 FALSE
27 7 FALSE
28 0 FALSE
29 2 FALSE
30 3 FALSE
31 0 FALSE
32 1 FALSE
33 3 FALSE
34 0 FALSE
35 0 FALSE
36 28 TRUE
37 8 FALSE
38 5 FALSE
39 5 FALSE
40 27 TRUE
41 5 FALSE
42 18 TRUE
43 19 TRUE
44 9 FALSE
45 9 FALSE
46 4 FALSE
47 2 FALSE
48 3 FALSE
49 9 FALSE
50 20 TRUE
51 6 FALSE
52 0 FALSE
53 27 TRUE
54 12 TRUE
55 34 TRUE
56 1 FALSE
57 28 TRUE
58 8 FALSE
59 3 FALSE
60 2 FALSE
61 1 FALSE
62 7 FALSE
63 4 FALSE
64 8 FALSE
65 6 FALSE
66 16 TRUE
67 6 FALSE
68 4 FALSE
69 3 FALSE
70 5 FALSE
71 0 FALSE
72 9 FALSE
73 0 FALSE
74 8 FALSE
75 0 FALSE
76 14 TRUE
77 4 FALSE
78 2 FALSE
79 35 TRUE
80 23 TRUE
81 13 TRUE
82 6 FALSE
83 3 FALSE
84 6 FALSE
85 0 FALSE
86 11 FALSE
87 11 FALSE
88 11 FALSE
89 4 FALSE
90 6 FALSE
91 23 TRUE
92 5 FALSE
93 5 FALSE
94 29 TRUE
95 7 FALSE
96 1 FALSE
97 9 FALSE
98 11 FALSE
99 18 TRUE
100 12 TRUE
101 6 FALSE
102 0 FALSE
103 4 FALSE
104 10 FALSE
105 19 TRUE
106 1 FALSE
107 12 TRUE
108 3 FALSE
109 0 FALSE
110 9 FALSE
111 14 TRUE
112 7 FALSE
113 3 FALSE
114 10 FALSE
115 12 TRUE
116 6 FALSE
117 35 TRUE
118 16 TRUE
119 3 FALSE
120 10 FALSE
121 6 FALSE
122 3 FALSE
123 2 FALSE
124 6 FALSE
125 5 FALSE
126 13 TRUE
127 7 FALSE
128 5 FALSE
129 3 FALSE
130 30 TRUE
131 16 TRUE
132 15 TRUE
133 12 TRUE
134 1 FALSE
135 4 FALSE
136 7 FALSE
137 1 FALSE
138 10 FALSE
139 3 FALSE
140 30 TRUE
141 2 FALSE
142 13 TRUE
143 5 FALSE
144 5 FALSE
145 5 FALSE
146 4 FALSE
147 3 FALSE
148 20 TRUE
149 12 TRUE
150 34 TRUE
151 6 FALSE
152 14 TRUE
153 16 TRUE
154 9 FALSE
155 15 TRUE
156 15 TRUE
157 0 FALSE
158 1 FALSE
159 4 FALSE
160 0 FALSE
161 0 FALSE
162 2 FALSE
163 1 FALSE
164 2 FALSE
165 0 FALSE
166 0 FALSE
167 0 FALSE
168 7 FALSE
169 2 FALSE
170 9 FALSE
171 6 FALSE
172 4 FALSE
173 1 FALSE
174 7 FALSE
175 0 FALSE
176 0 FALSE
177 4 FALSE
178 2 FALSE
179 0 FALSE
180 4 FALSE
181 2 FALSE
182 21 TRUE
183 1 FALSE
184 0 FALSE
185 1 FALSE
186 16 TRUE
187 9 FALSE
188 16 TRUE
189 0 FALSE
190 11 FALSE
191 1 FALSE
192 2 FALSE
193 3 FALSE
194 4 FALSE
195 2 FALSE
196 7 FALSE
197 7 FALSE
198 0 FALSE
199 1 FALSE
200 1 FALSE
201 0 FALSE
202 13 TRUE
203 1 FALSE
204 0 FALSE
205 1 FALSE
206 0 FALSE
207 4 FALSE
208 0 FALSE
209 4 FALSE
210 0 FALSE
211 2 FALSE
212 0 FALSE
213 4 FALSE
214 0 FALSE
215 4 FALSE
216 3 FALSE
217 1 FALSE
218 0 FALSE
219 1 FALSE
220 4 FALSE
221 1 FALSE
222 9 FALSE
223 0 FALSE
224 4 FALSE
225 8 FALSE
226 13 TRUE
227 0 FALSE
228 0 FALSE
229 0 FALSE
230 0 FALSE
231 2 FALSE
232 5 FALSE
233 1 FALSE
234 0 FALSE
235 3 FALSE
236 1 FALSE
237 2 FALSE
238 4 FALSE
239 5 FALSE
240 3 FALSE
241 1 FALSE
242 3 FALSE
243 6 FALSE
244 8 FALSE
245 21 TRUE
246 1 FALSE
247 7 FALSE
248 5 FALSE
249 1 FALSE
250 1 FALSE
251 0 FALSE
252 4 FALSE
253 0 FALSE
254 14 TRUE
255 2 FALSE
256 2 FALSE
257 2 FALSE
258 0 FALSE
259 1 FALSE
260 19 TRUE
261 2 FALSE
262 11 FALSE
263 3 FALSE
264 5 FALSE
265 16 TRUE
266 0 FALSE
267 5 FALSE
268 3 FALSE
269 2 FALSE
270 2 FALSE
271 8 FALSE
272 0 FALSE
273 3 FALSE
274 0 FALSE
275 1 FALSE
276 9 FALSE
277 1 FALSE
278 0 FALSE
279 0 FALSE
280 1 FALSE
281 12 TRUE
282 3 FALSE
283 0 FALSE
284 1 FALSE
285 8 FALSE
286 1 FALSE
287 1 FALSE
288 7 FALSE
289 6 FALSE
290 0 FALSE
291 8 FALSE
292 0 FALSE
293 1 FALSE
294 0 FALSE
295 4 FALSE
296 17 TRUE
297 9 FALSE
298 0 FALSE
299 0 FALSE
300 1 FALSE
301 3 FALSE
302 1 FALSE
303 2 FALSE
304 2 FALSE
305 2 FALSE
306 5 FALSE
307 3 FALSE
308 7 FALSE
309 1 FALSE
310 1 FALSE
311 3 FALSE
312 0 FALSE
313 0 FALSE
314 2 FALSE
alphaOutlier documentation built on May 2, 2019, 3:59 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Author(s) See Also Examples
Description
Given the parameters of a negative Binomial distribution, aout.nbinom identifies α-outliers in a given data set.
Usage
1 | aout.nbinom(data, param, alpha = 0.1, hide.outliers = FALSE)
|
Arguments
data |
a vector. The data set to be examined. |
param |
a vector. Contains the parameters of the negative Binomial distribution: N, p. |
alpha |
an atomic vector. Determines the maximum amount of probability mass the outlier region may contain. Defaults to 0.1. |
hide.outliers |
boolean. Returns the outlier-free data if set to |
Value
Data frame of the input data and an index named is.outlier that flags the outliers with TRUE. If hide.outliers is set to TRUE, a simple vector of the outlier-free data.
Author(s)
A. Rehage
See Also
Examples
1 2 | data(daysabs)
aout.nbinom(daysabs, c(8, 0.6), 0.05)
|
Example output
Loading required package: Rsolnp
Loading required package: nleqslv
Loading required package: quantreg
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
data is.outlier
1 4 FALSE
2 4 FALSE
3 2 FALSE
4 3 FALSE
5 3 FALSE
6 13 TRUE
7 11 FALSE
8 7 FALSE
9 10 FALSE
10 9 FALSE
11 4 FALSE
12 5 FALSE
13 5 FALSE
14 6 FALSE
15 1 FALSE
16 0 FALSE
17 1 FALSE
18 0 FALSE
19 5 FALSE
20 24 TRUE
21 2 FALSE
22 0 FALSE
23 1 FALSE
24 0 FALSE
25 8 FALSE
26 6 FALSE
27 7 FALSE
28 0 FALSE
29 2 FALSE
30 3 FALSE
31 0 FALSE
32 1 FALSE
33 3 FALSE
34 0 FALSE
35 0 FALSE
36 28 TRUE
37 8 FALSE
38 5 FALSE
39 5 FALSE
40 27 TRUE
41 5 FALSE
42 18 TRUE
43 19 TRUE
44 9 FALSE
45 9 FALSE
46 4 FALSE
47 2 FALSE
48 3 FALSE
49 9 FALSE
50 20 TRUE
51 6 FALSE
52 0 FALSE
53 27 TRUE
54 12 TRUE
55 34 TRUE
56 1 FALSE
57 28 TRUE
58 8 FALSE
59 3 FALSE
60 2 FALSE
61 1 FALSE
62 7 FALSE
63 4 FALSE
64 8 FALSE
65 6 FALSE
66 16 TRUE
67 6 FALSE
68 4 FALSE
69 3 FALSE
70 5 FALSE
71 0 FALSE
72 9 FALSE
73 0 FALSE
74 8 FALSE
75 0 FALSE
76 14 TRUE
77 4 FALSE
78 2 FALSE
79 35 TRUE
80 23 TRUE
81 13 TRUE
82 6 FALSE
83 3 FALSE
84 6 FALSE
85 0 FALSE
86 11 FALSE
87 11 FALSE
88 11 FALSE
89 4 FALSE
90 6 FALSE
91 23 TRUE
92 5 FALSE
93 5 FALSE
94 29 TRUE
95 7 FALSE
96 1 FALSE
97 9 FALSE
98 11 FALSE
99 18 TRUE
100 12 TRUE
101 6 FALSE
102 0 FALSE
103 4 FALSE
104 10 FALSE
105 19 TRUE
106 1 FALSE
107 12 TRUE
108 3 FALSE
109 0 FALSE
110 9 FALSE
111 14 TRUE
112 7 FALSE
113 3 FALSE
114 10 FALSE
115 12 TRUE
116 6 FALSE
117 35 TRUE
118 16 TRUE
119 3 FALSE
120 10 FALSE
121 6 FALSE
122 3 FALSE
123 2 FALSE
124 6 FALSE
125 5 FALSE
126 13 TRUE
127 7 FALSE
128 5 FALSE
129 3 FALSE
130 30 TRUE
131 16 TRUE
132 15 TRUE
133 12 TRUE
134 1 FALSE
135 4 FALSE
136 7 FALSE
137 1 FALSE
138 10 FALSE
139 3 FALSE
140 30 TRUE
141 2 FALSE
142 13 TRUE
143 5 FALSE
144 5 FALSE
145 5 FALSE
146 4 FALSE
147 3 FALSE
148 20 TRUE
149 12 TRUE
150 34 TRUE
151 6 FALSE
152 14 TRUE
153 16 TRUE
154 9 FALSE
155 15 TRUE
156 15 TRUE
157 0 FALSE
158 1 FALSE
159 4 FALSE
160 0 FALSE
161 0 FALSE
162 2 FALSE
163 1 FALSE
164 2 FALSE
165 0 FALSE
166 0 FALSE
167 0 FALSE
168 7 FALSE
169 2 FALSE
170 9 FALSE
171 6 FALSE
172 4 FALSE
173 1 FALSE
174 7 FALSE
175 0 FALSE
176 0 FALSE
177 4 FALSE
178 2 FALSE
179 0 FALSE
180 4 FALSE
181 2 FALSE
182 21 TRUE
183 1 FALSE
184 0 FALSE
185 1 FALSE
186 16 TRUE
187 9 FALSE
188 16 TRUE
189 0 FALSE
190 11 FALSE
191 1 FALSE
192 2 FALSE
193 3 FALSE
194 4 FALSE
195 2 FALSE
196 7 FALSE
197 7 FALSE
198 0 FALSE
199 1 FALSE
200 1 FALSE
201 0 FALSE
202 13 TRUE
203 1 FALSE
204 0 FALSE
205 1 FALSE
206 0 FALSE
207 4 FALSE
208 0 FALSE
209 4 FALSE
210 0 FALSE
211 2 FALSE
212 0 FALSE
213 4 FALSE
214 0 FALSE
215 4 FALSE
216 3 FALSE
217 1 FALSE
218 0 FALSE
219 1 FALSE
220 4 FALSE
221 1 FALSE
222 9 FALSE
223 0 FALSE
224 4 FALSE
225 8 FALSE
226 13 TRUE
227 0 FALSE
228 0 FALSE
229 0 FALSE
230 0 FALSE
231 2 FALSE
232 5 FALSE
233 1 FALSE
234 0 FALSE
235 3 FALSE
236 1 FALSE
237 2 FALSE
238 4 FALSE
239 5 FALSE
240 3 FALSE
241 1 FALSE
242 3 FALSE
243 6 FALSE
244 8 FALSE
245 21 TRUE
246 1 FALSE
247 7 FALSE
248 5 FALSE
249 1 FALSE
250 1 FALSE
251 0 FALSE
252 4 FALSE
253 0 FALSE
254 14 TRUE
255 2 FALSE
256 2 FALSE
257 2 FALSE
258 0 FALSE
259 1 FALSE
260 19 TRUE
261 2 FALSE
262 11 FALSE
263 3 FALSE
264 5 FALSE
265 16 TRUE
266 0 FALSE
267 5 FALSE
268 3 FALSE
269 2 FALSE
270 2 FALSE
271 8 FALSE
272 0 FALSE
273 3 FALSE
274 0 FALSE
275 1 FALSE
276 9 FALSE
277 1 FALSE
278 0 FALSE
279 0 FALSE
280 1 FALSE
281 12 TRUE
282 3 FALSE
283 0 FALSE
284 1 FALSE
285 8 FALSE
286 1 FALSE
287 1 FALSE
288 7 FALSE
289 6 FALSE
290 0 FALSE
291 8 FALSE
292 0 FALSE
293 1 FALSE
294 0 FALSE
295 4 FALSE
296 17 TRUE
297 9 FALSE
298 0 FALSE
299 0 FALSE
300 1 FALSE
301 3 FALSE
302 1 FALSE
303 2 FALSE
304 2 FALSE
305 2 FALSE
306 5 FALSE
307 3 FALSE
308 7 FALSE
309 1 FALSE
310 1 FALSE
311 3 FALSE
312 0 FALSE
313 0 FALSE
314 2 FALSE
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