gonad_mature: Estimate gonadal maturity
In sizeMat: Estimate Size at Sexual Maturity
Description
Usage
Arguments
Details
Value
Examples
View source: R/gonadMat-main.R
Description
Estimate size at gonad maturity.
Usage
1
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3
4
5
6
7
8
9
gonad_mature(
data,
varNames = c("allometric", "stage"),
inmName = "inm",
matName = "mad",
method = "fq",
niter = 999,
seed = 70388
)
Arguments
data
data.frame with allometric variables and stage of sexual maturity (gonad maturation stages).
varNames
a character string indicating the name of the allometric
and the stage of sexual maturity variables to be used for analysis.
inmName
a character string indicating the name or names of the immaturity stage.
matName
a character string indicating the name or names of the maturity stage.
method
a character string indicating the method to be applied, "fq" frequentist GLM, or "bayes" Bayes GLM (MCMClogit function).
niter
number of iterations (bootstrap resampling).
seed
a single value, interpreted as an integer.
Details
Estimate the size at gonad maturity using a logistic regression with X variable and
stages of sexual maturity (two categories: immature and mature).
The function requires a data.frame with the X (allometric variable) and
the stage of sexual maturity (gonad maturation stage).
The argument varNames requires a character string indicating the name of one allometric and the stage
of sexual maturity variable to be used for analysis (i.e. varNames = c("total_length", "stage_mat")).
So the argument varNames must contain two character strings only, the first is the allometric variable
and the second is the stage of sexual maturity.
The arguments inmName and matName require a character string indicating the name
of the stages of sexual maturity in the data.frame. The argument could contain one character string
or should be a vector (i.e. inmName = "I", matName = c("II", "III", "IV")).
The argument method requires a character string indicating which regression will be used for the test.
If method = "fq" the logistic regression is based on GLM (frequentist), if method = "bayes" a sample from
the posterior distribution of a logistic regression model using a random walk Metropolis algorithm is generated (see MCMClogit function).
The argument niter requires a number. For the GLM regression (method = "fq"), a non-parametric bootstrap method consists
in generate B bootstrap samples, by resampling with replacement the original data. Then all statistics for each parameter
can be calculated from each bootstrap sample (median and confidence intervals).
For the method = "bayes", the argument niter is related to the number of Metropolis iterations for the sampler.
Value
An object of class 'gonadMat'.
model the summary statistics of the model.
A_boot the 'n iter' values of parameter A.
B_boot the 'n iter' values of parameter B.
L50 the 'n iter' values of parameter L50 (size at gonad maturity).
out a dataframe with the allometric variable "X", stage of sexual maturity, the fitted values for
logistic regression and confidence intervals (95%). Also the summary statistics of the model is provided.
Examples
1
2
3
4
5
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7
8
9
10
Example output
[1] -8.627030 -8.792177 -9.348871 -9.087148 -9.006368 -8.264212 -8.258821
[8] -8.566862 -8.931090 -8.421382 -8.594158 -9.422902 -8.159287 -7.924324
[15] -7.816051 -8.649295 -8.441946 -7.897699 -8.527981 -9.050438 -8.726487
[22] -8.885475 -8.698325 -9.182506 -8.658300 -8.759841 -8.032643 -8.024464
[29] -8.925118 -8.583064 -8.753981 -7.831774 -8.502496 -8.475411 -8.597212
[36] -8.345696 -8.702437 -8.949270 -8.950208 -8.671547 -8.777397 -8.749831
[43] -8.070171 -9.388672 -8.190737 -8.157404 -8.741321 -8.232945 -8.610317
[50] -8.588924
[1] 0.3568461 0.3668552 0.3869750 0.3733971 0.3689496 0.3452642 0.3410802
[8] 0.3530922 0.3744906 0.3456040 0.3550986 0.3888834 0.3370025 0.3274486
[15] 0.3226383 0.3554968 0.3417472 0.3262718 0.3523094 0.3713010 0.3577442
[22] 0.3670733 0.3632974 0.3772265 0.3540632 0.3617494 0.3359198 0.3351658
[29] 0.3677076 0.3528702 0.3598843 0.3230251 0.3496224 0.3414643 0.3511483
[36] 0.3435701 0.3643522 0.3761087 0.3714209 0.3571104 0.3602560 0.3659014
[43] 0.3371633 0.3927777 0.3428081 0.3423900 0.3631556 0.3430552 0.3550637
[50] 0.3513234
[1] 24.17577 23.96634 24.15885 24.33642 24.41084 23.93591 24.21372 24.26239
[9] 23.84864 24.36715 24.20218 24.23066 24.21135 24.20021 24.22543 24.33016
[17] 24.70231 24.20589 24.20594 24.37494 24.39309 24.20627 23.94271 24.34215
[25] 24.45411 24.21522 23.91238 23.94177 24.27232 24.32357 24.32443 24.24509
[33] 24.31908 24.82078 24.48314 24.29110 23.88468 23.79437 24.09721 24.28254
[41] 24.36433 23.91309 23.93549 23.90327 23.89307 23.82489 24.07046 23.99890
[49] 24.25006 24.44734
x mature CIlower fitted CIupper
1 12 0 0.009 0.013 0.019
2 12 0 0.009 0.013 0.019
3 13 0 0.013 0.018 0.026
4 14 0 0.019 0.025 0.035
5 14 0 0.019 0.025 0.035
6 14 0 0.019 0.025 0.035
7 14 0 0.019 0.025 0.035
8 14 0 0.019 0.025 0.035
9 14 0 0.019 0.025 0.035
10 15 0 0.028 0.036 0.048
11 15 0 0.028 0.036 0.048
12 15 0 0.028 0.036 0.048
13 15 0 0.028 0.036 0.048
14 15 0 0.028 0.036 0.048
15 15 0 0.028 0.036 0.048
16 15 0 0.028 0.036 0.048
17 15 0 0.028 0.036 0.048
18 15 0 0.028 0.036 0.048
19 15 0 0.028 0.036 0.048
20 16 0 0.041 0.051 0.065
21 16 0 0.041 0.051 0.065
22 16 0 0.041 0.051 0.065
23 16 0 0.041 0.051 0.065
24 16 0 0.041 0.051 0.065
25 16 0 0.041 0.051 0.065
26 16 0 0.041 0.051 0.065
27 16 0 0.041 0.051 0.065
28 16 0 0.041 0.051 0.065
29 16 0 0.041 0.051 0.065
30 16 0 0.041 0.051 0.065
31 16 0 0.041 0.051 0.065
32 16 0 0.041 0.051 0.065
33 16 0 0.041 0.051 0.065
34 16 0 0.041 0.051 0.065
35 16 0 0.041 0.051 0.065
36 16 0 0.041 0.051 0.065
37 16 0 0.041 0.051 0.065
38 16 0 0.041 0.051 0.065
39 17 0 0.059 0.071 0.089
40 17 0 0.059 0.071 0.089
41 17 0 0.059 0.071 0.089
42 17 0 0.059 0.071 0.089
43 17 0 0.059 0.071 0.089
44 17 0 0.059 0.071 0.089
45 17 0 0.059 0.071 0.089
46 17 0 0.059 0.071 0.089
47 17 0 0.059 0.071 0.089
48 17 0 0.059 0.071 0.089
49 17 0 0.059 0.071 0.089
50 17 0 0.059 0.071 0.089
51 17 0 0.059 0.071 0.089
52 17 0 0.059 0.071 0.089
53 17 0 0.059 0.071 0.089
54 17 0 0.059 0.071 0.089
55 17 0 0.059 0.071 0.089
56 17 0 0.059 0.071 0.089
57 17 0 0.059 0.071 0.089
58 17 0 0.059 0.071 0.089
59 17 0 0.059 0.071 0.089
60 17 0 0.059 0.071 0.089
61 17 0 0.059 0.071 0.089
62 17 0 0.059 0.071 0.089
63 17 0 0.059 0.071 0.089
64 17 0 0.059 0.071 0.089
65 17 0 0.059 0.071 0.089
66 17 0 0.059 0.071 0.089
67 17 0 0.059 0.071 0.089
68 18 0 0.084 0.099 0.120
69 18 0 0.084 0.099 0.120
70 18 0 0.084 0.099 0.120
71 18 0 0.084 0.099 0.120
72 18 0 0.084 0.099 0.120
73 18 0 0.084 0.099 0.120
74 18 0 0.084 0.099 0.120
75 18 0 0.084 0.099 0.120
76 18 0 0.084 0.099 0.120
77 18 0 0.084 0.099 0.120
78 18 0 0.084 0.099 0.120
79 18 0 0.084 0.099 0.120
80 18 0 0.084 0.099 0.120
81 18 0 0.084 0.099 0.120
82 18 0 0.084 0.099 0.120
83 18 0 0.084 0.099 0.120
84 18 0 0.084 0.099 0.120
85 18 0 0.084 0.099 0.120
86 18 0 0.084 0.099 0.120
87 18 0 0.084 0.099 0.120
88 18 0 0.084 0.099 0.120
89 18 0 0.084 0.099 0.120
90 18 0 0.084 0.099 0.120
91 18 0 0.084 0.099 0.120
92 18 0 0.084 0.099 0.120
93 19 0 0.118 0.135 0.161
94 19 0 0.118 0.135 0.161
95 19 0 0.118 0.135 0.161
96 19 0 0.118 0.135 0.161
97 19 0 0.118 0.135 0.161
98 19 0 0.118 0.135 0.161
99 19 0 0.118 0.135 0.161
100 19 0 0.118 0.135 0.161
101 19 0 0.118 0.135 0.161
102 19 0 0.118 0.135 0.161
103 19 0 0.118 0.135 0.161
104 19 0 0.118 0.135 0.161
105 19 0 0.118 0.135 0.161
106 19 0 0.118 0.135 0.161
107 19 0 0.118 0.135 0.161
108 19 0 0.118 0.135 0.161
109 19 0 0.118 0.135 0.161
110 19 0 0.118 0.135 0.161
111 19 0 0.118 0.135 0.161
112 19 0 0.118 0.135 0.161
113 19 0 0.118 0.135 0.161
114 19 0 0.118 0.135 0.161
115 19 0 0.118 0.135 0.161
116 19 0 0.118 0.135 0.161
117 19 0 0.118 0.135 0.161
118 19 0 0.118 0.135 0.161
119 19 0 0.118 0.135 0.161
120 19 0 0.118 0.135 0.161
121 19 0 0.118 0.135 0.161
122 19 0 0.118 0.135 0.161
123 19 0 0.118 0.135 0.161
124 19 0 0.118 0.135 0.161
125 19 0 0.118 0.135 0.161
126 20 0 0.162 0.181 0.212
127 20 0 0.162 0.181 0.212
128 20 0 0.162 0.181 0.212
129 20 0 0.162 0.181 0.212
130 20 0 0.162 0.181 0.212
131 20 0 0.162 0.181 0.212
132 20 0 0.162 0.181 0.212
133 20 0 0.162 0.181 0.212
134 20 0 0.162 0.181 0.212
135 20 0 0.162 0.181 0.212
136 20 0 0.162 0.181 0.212
137 20 0 0.162 0.181 0.212
138 20 0 0.162 0.181 0.212
139 20 0 0.162 0.181 0.212
140 20 0 0.162 0.181 0.212
141 20 0 0.162 0.181 0.212
142 20 0 0.162 0.181 0.212
143 20 0 0.162 0.181 0.212
144 20 0 0.162 0.181 0.212
145 20 0 0.162 0.181 0.212
146 20 0 0.162 0.181 0.212
147 20 0 0.162 0.181 0.212
148 20 0 0.162 0.181 0.212
149 20 0 0.162 0.181 0.212
150 20 0 0.162 0.181 0.212
151 20 0 0.162 0.181 0.212
152 20 0 0.162 0.181 0.212
153 20 0 0.162 0.181 0.212
154 20 0 0.162 0.181 0.212
155 20 0 0.162 0.181 0.212
156 20 0 0.162 0.181 0.212
157 20 0 0.162 0.181 0.212
158 20 0 0.162 0.181 0.212
159 21 0 0.220 0.241 0.273
160 21 0 0.220 0.241 0.273
161 21 0 0.220 0.241 0.273
162 21 0 0.220 0.241 0.273
163 21 0 0.220 0.241 0.273
164 21 0 0.220 0.241 0.273
165 21 0 0.220 0.241 0.273
166 21 0 0.220 0.241 0.273
167 21 0 0.220 0.241 0.273
168 21 0 0.220 0.241 0.273
169 21 0 0.220 0.241 0.273
170 21 0 0.220 0.241 0.273
171 21 0 0.220 0.241 0.273
172 21 0 0.220 0.241 0.273
173 21 0 0.220 0.241 0.273
174 21 0 0.220 0.241 0.273
175 21 0 0.220 0.241 0.273
176 21 0 0.220 0.241 0.273
177 21 0 0.220 0.241 0.273
178 21 0 0.220 0.241 0.273
179 21 0 0.220 0.241 0.273
180 21 0 0.220 0.241 0.273
181 21 0 0.220 0.241 0.273
182 21 0 0.220 0.241 0.273
183 21 0 0.220 0.241 0.273
184 21 0 0.220 0.241 0.273
185 21 0 0.220 0.241 0.273
186 21 0 0.220 0.241 0.273
187 21 0 0.220 0.241 0.273
188 21 0 0.220 0.241 0.273
189 21 0 0.220 0.241 0.273
190 21 0 0.220 0.241 0.273
191 21 0 0.220 0.241 0.273
192 21 0 0.220 0.241 0.273
193 21 0 0.220 0.241 0.273
194 21 0 0.220 0.241 0.273
195 21 0 0.220 0.241 0.273
196 21 0 0.220 0.241 0.273
197 21 0 0.220 0.241 0.273
198 21 0 0.220 0.241 0.273
199 21 0 0.220 0.241 0.273
200 21 0 0.220 0.241 0.273
201 21 0 0.220 0.241 0.273
202 21 0 0.220 0.241 0.273
203 21 0 0.220 0.241 0.273
204 21 0 0.220 0.241 0.273
205 21 0 0.220 0.241 0.273
206 21 0 0.220 0.241 0.273
207 21 0 0.220 0.241 0.273
208 22 0 0.286 0.313 0.344
209 22 0 0.286 0.313 0.344
210 22 0 0.286 0.313 0.344
211 22 0 0.286 0.313 0.344
212 22 0 0.286 0.313 0.344
213 22 0 0.286 0.313 0.344
214 22 0 0.286 0.313 0.344
215 22 0 0.286 0.313 0.344
216 22 0 0.286 0.313 0.344
217 22 0 0.286 0.313 0.344
218 22 0 0.286 0.313 0.344
219 22 0 0.286 0.313 0.344
220 22 0 0.286 0.313 0.344
221 22 0 0.286 0.313 0.344
222 22 0 0.286 0.313 0.344
223 22 0 0.286 0.313 0.344
224 22 0 0.286 0.313 0.344
225 22 0 0.286 0.313 0.344
226 22 0 0.286 0.313 0.344
227 22 0 0.286 0.313 0.344
228 22 0 0.286 0.313 0.344
229 22 0 0.286 0.313 0.344
230 22 0 0.286 0.313 0.344
231 22 0 0.286 0.313 0.344
232 22 0 0.286 0.313 0.344
233 22 0 0.286 0.313 0.344
234 22 0 0.286 0.313 0.344
235 22 0 0.286 0.313 0.344
236 22 0 0.286 0.313 0.344
237 22 0 0.286 0.313 0.344
238 22 0 0.286 0.313 0.344
239 22 0 0.286 0.313 0.344
240 22 0 0.286 0.313 0.344
241 22 0 0.286 0.313 0.344
242 22 0 0.286 0.313 0.344
243 22 0 0.286 0.313 0.344
244 22 0 0.286 0.313 0.344
245 22 0 0.286 0.313 0.344
246 22 0 0.286 0.313 0.344
247 22 0 0.286 0.313 0.344
248 22 0 0.286 0.313 0.344
249 22 0 0.286 0.313 0.344
250 22 0 0.286 0.313 0.344
251 22 0 0.286 0.313 0.344
252 22 0 0.286 0.313 0.344
253 22 0 0.286 0.313 0.344
254 22 0 0.286 0.313 0.344
255 22 0 0.286 0.313 0.344
256 22 0 0.286 0.313 0.344
257 22 0 0.286 0.313 0.344
258 22 0 0.286 0.313 0.344
259 22 0 0.286 0.313 0.344
260 22 0 0.286 0.313 0.344
261 22 0 0.286 0.313 0.344
262 22 0 0.286 0.313 0.344
263 22 0 0.286 0.313 0.344
264 22 0 0.286 0.313 0.344
265 22 0 0.286 0.313 0.344
266 22 0 0.286 0.313 0.344
267 22 0 0.286 0.313 0.344
268 22 0 0.286 0.313 0.344
269 22 0 0.286 0.313 0.344
270 23 0 0.362 0.393 0.425
271 23 0 0.362 0.393 0.425
272 23 0 0.362 0.393 0.425
273 23 0 0.362 0.393 0.425
274 23 0 0.362 0.393 0.425
275 23 0 0.362 0.393 0.425
276 23 0 0.362 0.393 0.425
277 23 0 0.362 0.393 0.425
278 23 0 0.362 0.393 0.425
279 23 0 0.362 0.393 0.425
280 23 0 0.362 0.393 0.425
281 23 0 0.362 0.393 0.425
282 23 0 0.362 0.393 0.425
283 23 0 0.362 0.393 0.425
284 23 0 0.362 0.393 0.425
285 23 0 0.362 0.393 0.425
286 23 0 0.362 0.393 0.425
287 23 0 0.362 0.393 0.425
288 23 0 0.362 0.393 0.425
289 23 0 0.362 0.393 0.425
290 23 0 0.362 0.393 0.425
291 23 0 0.362 0.393 0.425
292 23 0 0.362 0.393 0.425
293 23 0 0.362 0.393 0.425
294 23 0 0.362 0.393 0.425
295 23 0 0.362 0.393 0.425
296 23 0 0.362 0.393 0.425
297 23 0 0.362 0.393 0.425
298 23 0 0.362 0.393 0.425
299 23 0 0.362 0.393 0.425
300 23 0 0.362 0.393 0.425
301 23 0 0.362 0.393 0.425
302 23 0 0.362 0.393 0.425
303 23 0 0.362 0.393 0.425
304 23 0 0.362 0.393 0.425
305 23 0 0.362 0.393 0.425
306 23 0 0.362 0.393 0.425
307 23 0 0.362 0.393 0.425
308 23 0 0.362 0.393 0.425
309 23 0 0.362 0.393 0.425
310 23 0 0.362 0.393 0.425
311 23 0 0.362 0.393 0.425
312 23 0 0.362 0.393 0.425
313 23 0 0.362 0.393 0.425
314 23 0 0.362 0.393 0.425
315 23 0 0.362 0.393 0.425
316 23 0 0.362 0.393 0.425
317 23 0 0.362 0.393 0.425
318 23 0 0.362 0.393 0.425
319 23 0 0.362 0.393 0.425
320 23 0 0.362 0.393 0.425
321 23 0 0.362 0.393 0.425
322 23 0 0.362 0.393 0.425
323 23 0 0.362 0.393 0.425
324 23 0 0.362 0.393 0.425
325 23 0 0.362 0.393 0.425
326 23 0 0.362 0.393 0.425
327 23 0 0.362 0.393 0.425
328 23 0 0.362 0.393 0.425
329 23 0 0.362 0.393 0.425
330 23 0 0.362 0.393 0.425
331 23 0 0.362 0.393 0.425
332 23 0 0.362 0.393 0.425
333 24 0 0.444 0.481 0.515
334 24 0 0.444 0.481 0.515
335 24 0 0.444 0.481 0.515
336 24 0 0.444 0.481 0.515
337 24 0 0.444 0.481 0.515
338 24 0 0.444 0.481 0.515
339 24 0 0.444 0.481 0.515
340 24 0 0.444 0.481 0.515
341 24 0 0.444 0.481 0.515
342 24 0 0.444 0.481 0.515
343 24 0 0.444 0.481 0.515
344 24 0 0.444 0.481 0.515
345 24 0 0.444 0.481 0.515
346 24 0 0.444 0.481 0.515
347 24 0 0.444 0.481 0.515
348 24 0 0.444 0.481 0.515
349 24 0 0.444 0.481 0.515
350 24 0 0.444 0.481 0.515
351 24 0 0.444 0.481 0.515
352 24 0 0.444 0.481 0.515
353 24 0 0.444 0.481 0.515
354 24 0 0.444 0.481 0.515
355 24 0 0.444 0.481 0.515
356 24 0 0.444 0.481 0.515
357 24 0 0.444 0.481 0.515
358 24 0 0.444 0.481 0.515
359 24 0 0.444 0.481 0.515
360 24 0 0.444 0.481 0.515
361 24 0 0.444 0.481 0.515
362 24 0 0.444 0.481 0.515
363 24 0 0.444 0.481 0.515
364 24 0 0.444 0.481 0.515
365 24 0 0.444 0.481 0.515
366 24 0 0.444 0.481 0.515
367 24 0 0.444 0.481 0.515
368 24 0 0.444 0.481 0.515
369 24 0 0.444 0.481 0.515
370 24 0 0.444 0.481 0.515
371 24 0 0.444 0.481 0.515
372 24 0 0.444 0.481 0.515
373 24 0 0.444 0.481 0.515
374 24 0 0.444 0.481 0.515
375 25 0 0.530 0.566 0.606
376 25 0 0.530 0.566 0.606
377 25 0 0.530 0.566 0.606
378 25 0 0.530 0.566 0.606
379 25 0 0.530 0.566 0.606
380 25 0 0.530 0.566 0.606
381 25 0 0.530 0.566 0.606
382 25 0 0.530 0.566 0.606
383 25 0 0.530 0.566 0.606
384 25 0 0.530 0.566 0.606
385 25 0 0.530 0.566 0.606
386 25 0 0.530 0.566 0.606
387 25 0 0.530 0.566 0.606
388 25 0 0.530 0.566 0.606
389 25 0 0.530 0.566 0.606
390 25 0 0.530 0.566 0.606
391 25 0 0.530 0.566 0.606
392 25 0 0.530 0.566 0.606
393 25 0 0.530 0.566 0.606
394 25 0 0.530 0.566 0.606
395 25 0 0.530 0.566 0.606
396 25 0 0.530 0.566 0.606
397 25 0 0.530 0.566 0.606
398 25 0 0.530 0.566 0.606
399 25 0 0.530 0.566 0.606
400 25 0 0.530 0.566 0.606
401 25 0 0.530 0.566 0.606
402 25 0 0.530 0.566 0.606
403 25 0 0.530 0.566 0.606
404 25 0 0.530 0.566 0.606
405 25 0 0.530 0.566 0.606
406 25 0 0.530 0.566 0.606
407 25 0 0.530 0.566 0.606
408 25 0 0.530 0.566 0.606
409 25 0 0.530 0.566 0.606
410 25 0 0.530 0.566 0.606
411 25 0 0.530 0.566 0.606
412 25 0 0.530 0.566 0.606
413 25 0 0.530 0.566 0.606
414 26 0 0.614 0.651 0.694
415 26 0 0.614 0.651 0.694
416 26 0 0.614 0.651 0.694
417 26 0 0.614 0.651 0.694
418 26 0 0.614 0.651 0.694
419 26 0 0.614 0.651 0.694
420 26 0 0.614 0.651 0.694
421 26 0 0.614 0.651 0.694
422 26 0 0.614 0.651 0.694
423 26 0 0.614 0.651 0.694
424 26 0 0.614 0.651 0.694
425 26 0 0.614 0.651 0.694
426 26 0 0.614 0.651 0.694
427 26 0 0.614 0.651 0.694
428 26 0 0.614 0.651 0.694
429 26 0 0.614 0.651 0.694
430 26 0 0.614 0.651 0.694
431 26 0 0.614 0.651 0.694
432 26 0 0.614 0.651 0.694
433 26 0 0.614 0.651 0.694
434 26 0 0.614 0.651 0.694
435 26 0 0.614 0.651 0.694
436 26 0 0.614 0.651 0.694
437 26 0 0.614 0.651 0.694
438 26 0 0.614 0.651 0.694
439 26 0 0.614 0.651 0.694
440 26 0 0.614 0.651 0.694
441 26 0 0.614 0.651 0.694
442 26 0 0.614 0.651 0.694
443 27 0 0.691 0.729 0.769
444 27 0 0.691 0.729 0.769
445 27 0 0.691 0.729 0.769
446 27 0 0.691 0.729 0.769
447 27 0 0.691 0.729 0.769
448 27 0 0.691 0.729 0.769
449 27 0 0.691 0.729 0.769
450 27 0 0.691 0.729 0.769
451 27 0 0.691 0.729 0.769
452 27 0 0.691 0.729 0.769
453 27 0 0.691 0.729 0.769
454 27 0 0.691 0.729 0.769
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1521 39 1 0.992 0.995 0.997
1522 39 1 0.992 0.995 0.997
1523 39 1 0.992 0.995 0.997
1524 39 1 0.992 0.995 0.997
1525 39 1 0.992 0.995 0.997
1526 39 1 0.992 0.995 0.997
1527 39 1 0.992 0.995 0.997
1528 39 1 0.992 0.995 0.997
1529 39 1 0.992 0.995 0.997
1530 39 1 0.992 0.995 0.997
1531 39 1 0.992 0.995 0.997
1532 39 1 0.992 0.995 0.997
1533 39 1 0.992 0.995 0.997
1534 39 1 0.992 0.995 0.997
1535 39 1 0.992 0.995 0.997
1536 39 1 0.992 0.995 0.997
1537 39 1 0.992 0.995 0.997
1538 39 1 0.992 0.995 0.997
1539 39 1 0.992 0.995 0.997
1540 39 1 0.992 0.995 0.997
1541 39 1 0.992 0.995 0.997
1542 39 1 0.992 0.995 0.997
1543 39 1 0.992 0.995 0.997
1544 39 1 0.992 0.995 0.997
1545 39 1 0.992 0.995 0.997
1546 39 1 0.992 0.995 0.997
1547 39 1 0.992 0.995 0.997
1548 39 1 0.992 0.995 0.997
1549 39 1 0.992 0.995 0.997
1550 39 1 0.992 0.995 0.997
1551 39 1 0.992 0.995 0.997
1552 39 1 0.992 0.995 0.997
1553 39 1 0.992 0.995 0.997
1554 40 1 0.994 0.996 0.998
1555 40 1 0.994 0.996 0.998
1556 40 1 0.994 0.996 0.998
1557 40 1 0.994 0.996 0.998
1558 40 1 0.994 0.996 0.998
1559 40 1 0.994 0.996 0.998
1560 40 1 0.994 0.996 0.998
1561 40 1 0.994 0.996 0.998
1562 40 1 0.994 0.996 0.998
1563 40 1 0.994 0.996 0.998
1564 40 1 0.994 0.996 0.998
1565 40 1 0.994 0.996 0.998
1566 40 1 0.994 0.996 0.998
1567 40 1 0.994 0.996 0.998
1568 40 1 0.994 0.996 0.998
1569 40 1 0.994 0.996 0.998
1570 40 1 0.994 0.996 0.998
1571 40 1 0.994 0.996 0.998
1572 40 1 0.994 0.996 0.998
1573 40 1 0.994 0.996 0.998
1574 40 1 0.994 0.996 0.998
1575 40 1 0.994 0.996 0.998
1576 40 1 0.994 0.996 0.998
1577 40 1 0.994 0.996 0.998
1578 40 1 0.994 0.996 0.998
1579 40 1 0.994 0.996 0.998
1580 40 1 0.994 0.996 0.998
1581 40 1 0.994 0.996 0.998
1582 40 1 0.994 0.996 0.998
1583 40 1 0.994 0.996 0.998
1584 40 1 0.994 0.996 0.998
1585 41 1 0.996 0.997 0.999
1586 41 1 0.996 0.997 0.999
1587 41 1 0.996 0.997 0.999
1588 41 1 0.996 0.997 0.999
1589 41 1 0.996 0.997 0.999
1590 41 1 0.996 0.997 0.999
1591 41 1 0.996 0.997 0.999
1592 41 1 0.996 0.997 0.999
1593 41 1 0.996 0.997 0.999
1594 41 1 0.996 0.997 0.999
1595 41 1 0.996 0.997 0.999
1596 41 1 0.996 0.997 0.999
1597 41 1 0.996 0.997 0.999
1598 41 1 0.996 0.997 0.999
1599 41 1 0.996 0.997 0.999
1600 41 1 0.996 0.997 0.999
1601 41 1 0.996 0.997 0.999
1602 41 1 0.996 0.997 0.999
1603 41 1 0.996 0.997 0.999
1604 42 1 0.997 0.998 0.999
1605 42 1 0.997 0.998 0.999
1606 42 1 0.997 0.998 0.999
1607 42 1 0.997 0.998 0.999
1608 42 1 0.997 0.998 0.999
1609 42 1 0.997 0.998 0.999
1610 42 1 0.997 0.998 0.999
1611 42 1 0.997 0.998 0.999
1612 42 1 0.997 0.998 0.999
1613 42 1 0.997 0.998 0.999
1614 42 1 0.997 0.998 0.999
1615 42 1 0.997 0.998 0.999
1616 42 1 0.997 0.998 0.999
1617 42 1 0.997 0.998 0.999
1618 42 1 0.997 0.998 0.999
1619 42 1 0.997 0.998 0.999
1620 42 1 0.997 0.998 0.999
1621 42 1 0.997 0.998 0.999
1622 42 1 0.997 0.998 0.999
1623 42 1 0.997 0.998 0.999
1624 42 1 0.997 0.998 0.999
1625 42 1 0.997 0.998 0.999
1626 43 1 0.998 0.999 0.999
1627 43 1 0.998 0.999 0.999
1628 43 1 0.998 0.999 0.999
1629 43 1 0.998 0.999 0.999
1630 43 1 0.998 0.999 0.999
1631 43 1 0.998 0.999 0.999
1632 43 1 0.998 0.999 0.999
1633 43 1 0.998 0.999 0.999
1634 43 1 0.998 0.999 0.999
1635 43 1 0.998 0.999 0.999
1636 43 1 0.998 0.999 0.999
1637 43 1 0.998 0.999 0.999
1638 43 1 0.998 0.999 0.999
1639 43 1 0.998 0.999 0.999
1640 43 1 0.998 0.999 0.999
1641 43 1 0.998 0.999 0.999
1642 43 1 0.998 0.999 0.999
1643 44 1 0.998 0.999 1.000
1644 44 1 0.998 0.999 1.000
1645 44 1 0.998 0.999 1.000
1646 44 1 0.998 0.999 1.000
1647 44 1 0.998 0.999 1.000
1648 44 1 0.998 0.999 1.000
1649 44 1 0.998 0.999 1.000
1650 44 1 0.998 0.999 1.000
1651 44 1 0.998 0.999 1.000
1652 44 1 0.998 0.999 1.000
1653 44 1 0.998 0.999 1.000
1654 44 1 0.998 0.999 1.000
1655 44 1 0.998 0.999 1.000
1656 44 1 0.998 0.999 1.000
1657 44 1 0.998 0.999 1.000
1658 44 1 0.998 0.999 1.000
1659 45 1 0.999 0.999 1.000
1660 45 1 0.999 0.999 1.000
1661 45 1 0.999 0.999 1.000
1662 45 1 0.999 0.999 1.000
1663 45 1 0.999 0.999 1.000
1664 45 1 0.999 0.999 1.000
1665 45 1 0.999 0.999 1.000
1666 45 1 0.999 0.999 1.000
1667 45 1 0.999 0.999 1.000
1668 45 1 0.999 0.999 1.000
1669 45 1 0.999 0.999 1.000
1670 45 1 0.999 0.999 1.000
1671 45 1 0.999 0.999 1.000
1672 46 1 0.999 1.000 1.000
1673 46 1 0.999 1.000 1.000
1674 46 1 0.999 1.000 1.000
1675 46 1 0.999 1.000 1.000
1676 46 1 0.999 1.000 1.000
1677 46 1 0.999 1.000 1.000
1678 46 1 0.999 1.000 1.000
1679 46 1 0.999 1.000 1.000
1680 46 1 0.999 1.000 1.000
1681 46 1 0.999 1.000 1.000
1682 46 1 0.999 1.000 1.000
1683 46 1 0.999 1.000 1.000
1684 46 1 0.999 1.000 1.000
1685 46 1 0.999 1.000 1.000
1686 47 1 0.999 1.000 1.000
1687 47 1 0.999 1.000 1.000
1688 47 1 0.999 1.000 1.000
1689 47 1 0.999 1.000 1.000
1690 47 1 0.999 1.000 1.000
1691 47 1 0.999 1.000 1.000
1692 47 1 0.999 1.000 1.000
1693 47 1 0.999 1.000 1.000
1694 47 1 0.999 1.000 1.000
1695 48 1 1.000 1.000 1.000
1696 48 1 1.000 1.000 1.000
1697 48 1 1.000 1.000 1.000
1698 48 1 1.000 1.000 1.000
1699 48 1 1.000 1.000 1.000
1700 48 1 1.000 1.000 1.000
1701 48 1 1.000 1.000 1.000
1702 48 1 1.000 1.000 1.000
1703 49 1 1.000 1.000 1.000
1704 49 1 1.000 1.000 1.000
1705 49 1 1.000 1.000 1.000
1706 49 1 1.000 1.000 1.000
1707 49 1 1.000 1.000 1.000
1708 49 1 1.000 1.000 1.000
1709 49 1 1.000 1.000 1.000
1710 49 1 1.000 1.000 1.000
1711 49 1 1.000 1.000 1.000
1712 50 1 1.000 1.000 1.000
1713 50 1 1.000 1.000 1.000
1714 50 1 1.000 1.000 1.000
1715 51 1 1.000 1.000 1.000
1716 51 1 1.000 1.000 1.000
1717 51 1 1.000 1.000 1.000
1718 51 1 1.000 1.000 1.000
1719 51 1 1.000 1.000 1.000
1720 51 1 1.000 1.000 1.000
1721 51 1 1.000 1.000 1.000
1722 53 1 1.000 1.000 1.000
1723 53 1 1.000 1.000 1.000
1724 54 1 1.000 1.000 1.000
1725 54 1 1.000 1.000 1.000
1726 55 1 1.000 1.000 1.000
1727 56 1 1.000 1.000 1.000
1728 56 1 1.000 1.000 1.000
1729 57 1 1.000 1.000 1.000
1730 58 1 1.000 1.000 1.000
1731 59 1 1.000 1.000 1.000
1732 62 1 1.000 1.000 1.000
1733 63 1 1.000 1.000 1.000
sizeMat documentation built on July 8, 2020, 7:27 p.m.
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Description Usage Arguments Details Value Examples
View source: R/gonadMat-main.R
Description
Estimate size at gonad maturity.
Usage
1 2 3 4 5 6 7 8 9 | gonad_mature(
data,
varNames = c("allometric", "stage"),
inmName = "inm",
matName = "mad",
method = "fq",
niter = 999,
seed = 70388
)
|
Arguments
data |
data.frame with allometric variables and stage of sexual maturity (gonad maturation stages). |
varNames |
a character string indicating the name of the allometric and the stage of sexual maturity variables to be used for analysis. |
inmName |
a character string indicating the name or names of the immaturity stage. |
matName |
a character string indicating the name or names of the maturity stage. |
method |
a character string indicating the method to be applied, |
niter |
number of iterations (bootstrap resampling). |
seed |
a single value, interpreted as an integer. |
Details
Estimate the size at gonad maturity using a logistic regression with X variable and stages of sexual maturity (two categories: immature and mature).
The function requires a data.frame with the X (allometric variable) and the stage of sexual maturity (gonad maturation stage).
The argument varNames requires a character string indicating the name of one allometric and the stage
of sexual maturity variable to be used for analysis (i.e. varNames = c("total_length", "stage_mat")).
So the argument varNames must contain two character strings only, the first is the allometric variable
and the second is the stage of sexual maturity.
The arguments inmName and matName require a character string indicating the name
of the stages of sexual maturity in the data.frame. The argument could contain one character string
or should be a vector (i.e. inmName = "I", matName = c("II", "III", "IV")).
The argument method requires a character string indicating which regression will be used for the test.
If method = "fq" the logistic regression is based on GLM (frequentist), if method = "bayes" a sample from
the posterior distribution of a logistic regression model using a random walk Metropolis algorithm is generated (see MCMClogit function).
The argument niter requires a number. For the GLM regression (method = "fq"), a non-parametric bootstrap method consists
in generate B bootstrap samples, by resampling with replacement the original data. Then all statistics for each parameter
can be calculated from each bootstrap sample (median and confidence intervals).
For the method = "bayes", the argument niter is related to the number of Metropolis iterations for the sampler.
Value
An object of class 'gonadMat'.
model the summary statistics of the model.
A_boot the 'n iter' values of parameter A.
B_boot the 'n iter' values of parameter B.
L50 the 'n iter' values of parameter L50 (size at gonad maturity).
out a dataframe with the allometric variable "X", stage of sexual maturity, the fitted values for
logistic regression and confidence intervals (95%). Also the summary statistics of the model is provided.
Examples
1 2 3 4 5 6 7 8 9 10 |
Example output
[1] -8.627030 -8.792177 -9.348871 -9.087148 -9.006368 -8.264212 -8.258821
[8] -8.566862 -8.931090 -8.421382 -8.594158 -9.422902 -8.159287 -7.924324
[15] -7.816051 -8.649295 -8.441946 -7.897699 -8.527981 -9.050438 -8.726487
[22] -8.885475 -8.698325 -9.182506 -8.658300 -8.759841 -8.032643 -8.024464
[29] -8.925118 -8.583064 -8.753981 -7.831774 -8.502496 -8.475411 -8.597212
[36] -8.345696 -8.702437 -8.949270 -8.950208 -8.671547 -8.777397 -8.749831
[43] -8.070171 -9.388672 -8.190737 -8.157404 -8.741321 -8.232945 -8.610317
[50] -8.588924
[1] 0.3568461 0.3668552 0.3869750 0.3733971 0.3689496 0.3452642 0.3410802
[8] 0.3530922 0.3744906 0.3456040 0.3550986 0.3888834 0.3370025 0.3274486
[15] 0.3226383 0.3554968 0.3417472 0.3262718 0.3523094 0.3713010 0.3577442
[22] 0.3670733 0.3632974 0.3772265 0.3540632 0.3617494 0.3359198 0.3351658
[29] 0.3677076 0.3528702 0.3598843 0.3230251 0.3496224 0.3414643 0.3511483
[36] 0.3435701 0.3643522 0.3761087 0.3714209 0.3571104 0.3602560 0.3659014
[43] 0.3371633 0.3927777 0.3428081 0.3423900 0.3631556 0.3430552 0.3550637
[50] 0.3513234
[1] 24.17577 23.96634 24.15885 24.33642 24.41084 23.93591 24.21372 24.26239
[9] 23.84864 24.36715 24.20218 24.23066 24.21135 24.20021 24.22543 24.33016
[17] 24.70231 24.20589 24.20594 24.37494 24.39309 24.20627 23.94271 24.34215
[25] 24.45411 24.21522 23.91238 23.94177 24.27232 24.32357 24.32443 24.24509
[33] 24.31908 24.82078 24.48314 24.29110 23.88468 23.79437 24.09721 24.28254
[41] 24.36433 23.91309 23.93549 23.90327 23.89307 23.82489 24.07046 23.99890
[49] 24.25006 24.44734
x mature CIlower fitted CIupper
1 12 0 0.009 0.013 0.019
2 12 0 0.009 0.013 0.019
3 13 0 0.013 0.018 0.026
4 14 0 0.019 0.025 0.035
5 14 0 0.019 0.025 0.035
6 14 0 0.019 0.025 0.035
7 14 0 0.019 0.025 0.035
8 14 0 0.019 0.025 0.035
9 14 0 0.019 0.025 0.035
10 15 0 0.028 0.036 0.048
11 15 0 0.028 0.036 0.048
12 15 0 0.028 0.036 0.048
13 15 0 0.028 0.036 0.048
14 15 0 0.028 0.036 0.048
15 15 0 0.028 0.036 0.048
16 15 0 0.028 0.036 0.048
17 15 0 0.028 0.036 0.048
18 15 0 0.028 0.036 0.048
19 15 0 0.028 0.036 0.048
20 16 0 0.041 0.051 0.065
21 16 0 0.041 0.051 0.065
22 16 0 0.041 0.051 0.065
23 16 0 0.041 0.051 0.065
24 16 0 0.041 0.051 0.065
25 16 0 0.041 0.051 0.065
26 16 0 0.041 0.051 0.065
27 16 0 0.041 0.051 0.065
28 16 0 0.041 0.051 0.065
29 16 0 0.041 0.051 0.065
30 16 0 0.041 0.051 0.065
31 16 0 0.041 0.051 0.065
32 16 0 0.041 0.051 0.065
33 16 0 0.041 0.051 0.065
34 16 0 0.041 0.051 0.065
35 16 0 0.041 0.051 0.065
36 16 0 0.041 0.051 0.065
37 16 0 0.041 0.051 0.065
38 16 0 0.041 0.051 0.065
39 17 0 0.059 0.071 0.089
40 17 0 0.059 0.071 0.089
41 17 0 0.059 0.071 0.089
42 17 0 0.059 0.071 0.089
43 17 0 0.059 0.071 0.089
44 17 0 0.059 0.071 0.089
45 17 0 0.059 0.071 0.089
46 17 0 0.059 0.071 0.089
47 17 0 0.059 0.071 0.089
48 17 0 0.059 0.071 0.089
49 17 0 0.059 0.071 0.089
50 17 0 0.059 0.071 0.089
51 17 0 0.059 0.071 0.089
52 17 0 0.059 0.071 0.089
53 17 0 0.059 0.071 0.089
54 17 0 0.059 0.071 0.089
55 17 0 0.059 0.071 0.089
56 17 0 0.059 0.071 0.089
57 17 0 0.059 0.071 0.089
58 17 0 0.059 0.071 0.089
59 17 0 0.059 0.071 0.089
60 17 0 0.059 0.071 0.089
61 17 0 0.059 0.071 0.089
62 17 0 0.059 0.071 0.089
63 17 0 0.059 0.071 0.089
64 17 0 0.059 0.071 0.089
65 17 0 0.059 0.071 0.089
66 17 0 0.059 0.071 0.089
67 17 0 0.059 0.071 0.089
68 18 0 0.084 0.099 0.120
69 18 0 0.084 0.099 0.120
70 18 0 0.084 0.099 0.120
71 18 0 0.084 0.099 0.120
72 18 0 0.084 0.099 0.120
73 18 0 0.084 0.099 0.120
74 18 0 0.084 0.099 0.120
75 18 0 0.084 0.099 0.120
76 18 0 0.084 0.099 0.120
77 18 0 0.084 0.099 0.120
78 18 0 0.084 0.099 0.120
79 18 0 0.084 0.099 0.120
80 18 0 0.084 0.099 0.120
81 18 0 0.084 0.099 0.120
82 18 0 0.084 0.099 0.120
83 18 0 0.084 0.099 0.120
84 18 0 0.084 0.099 0.120
85 18 0 0.084 0.099 0.120
86 18 0 0.084 0.099 0.120
87 18 0 0.084 0.099 0.120
88 18 0 0.084 0.099 0.120
89 18 0 0.084 0.099 0.120
90 18 0 0.084 0.099 0.120
91 18 0 0.084 0.099 0.120
92 18 0 0.084 0.099 0.120
93 19 0 0.118 0.135 0.161
94 19 0 0.118 0.135 0.161
95 19 0 0.118 0.135 0.161
96 19 0 0.118 0.135 0.161
97 19 0 0.118 0.135 0.161
98 19 0 0.118 0.135 0.161
99 19 0 0.118 0.135 0.161
100 19 0 0.118 0.135 0.161
101 19 0 0.118 0.135 0.161
102 19 0 0.118 0.135 0.161
103 19 0 0.118 0.135 0.161
104 19 0 0.118 0.135 0.161
105 19 0 0.118 0.135 0.161
106 19 0 0.118 0.135 0.161
107 19 0 0.118 0.135 0.161
108 19 0 0.118 0.135 0.161
109 19 0 0.118 0.135 0.161
110 19 0 0.118 0.135 0.161
111 19 0 0.118 0.135 0.161
112 19 0 0.118 0.135 0.161
113 19 0 0.118 0.135 0.161
114 19 0 0.118 0.135 0.161
115 19 0 0.118 0.135 0.161
116 19 0 0.118 0.135 0.161
117 19 0 0.118 0.135 0.161
118 19 0 0.118 0.135 0.161
119 19 0 0.118 0.135 0.161
120 19 0 0.118 0.135 0.161
121 19 0 0.118 0.135 0.161
122 19 0 0.118 0.135 0.161
123 19 0 0.118 0.135 0.161
124 19 0 0.118 0.135 0.161
125 19 0 0.118 0.135 0.161
126 20 0 0.162 0.181 0.212
127 20 0 0.162 0.181 0.212
128 20 0 0.162 0.181 0.212
129 20 0 0.162 0.181 0.212
130 20 0 0.162 0.181 0.212
131 20 0 0.162 0.181 0.212
132 20 0 0.162 0.181 0.212
133 20 0 0.162 0.181 0.212
134 20 0 0.162 0.181 0.212
135 20 0 0.162 0.181 0.212
136 20 0 0.162 0.181 0.212
137 20 0 0.162 0.181 0.212
138 20 0 0.162 0.181 0.212
139 20 0 0.162 0.181 0.212
140 20 0 0.162 0.181 0.212
141 20 0 0.162 0.181 0.212
142 20 0 0.162 0.181 0.212
143 20 0 0.162 0.181 0.212
144 20 0 0.162 0.181 0.212
145 20 0 0.162 0.181 0.212
146 20 0 0.162 0.181 0.212
147 20 0 0.162 0.181 0.212
148 20 0 0.162 0.181 0.212
149 20 0 0.162 0.181 0.212
150 20 0 0.162 0.181 0.212
151 20 0 0.162 0.181 0.212
152 20 0 0.162 0.181 0.212
153 20 0 0.162 0.181 0.212
154 20 0 0.162 0.181 0.212
155 20 0 0.162 0.181 0.212
156 20 0 0.162 0.181 0.212
157 20 0 0.162 0.181 0.212
158 20 0 0.162 0.181 0.212
159 21 0 0.220 0.241 0.273
160 21 0 0.220 0.241 0.273
161 21 0 0.220 0.241 0.273
162 21 0 0.220 0.241 0.273
163 21 0 0.220 0.241 0.273
164 21 0 0.220 0.241 0.273
165 21 0 0.220 0.241 0.273
166 21 0 0.220 0.241 0.273
167 21 0 0.220 0.241 0.273
168 21 0 0.220 0.241 0.273
169 21 0 0.220 0.241 0.273
170 21 0 0.220 0.241 0.273
171 21 0 0.220 0.241 0.273
172 21 0 0.220 0.241 0.273
173 21 0 0.220 0.241 0.273
174 21 0 0.220 0.241 0.273
175 21 0 0.220 0.241 0.273
176 21 0 0.220 0.241 0.273
177 21 0 0.220 0.241 0.273
178 21 0 0.220 0.241 0.273
179 21 0 0.220 0.241 0.273
180 21 0 0.220 0.241 0.273
181 21 0 0.220 0.241 0.273
182 21 0 0.220 0.241 0.273
183 21 0 0.220 0.241 0.273
184 21 0 0.220 0.241 0.273
185 21 0 0.220 0.241 0.273
186 21 0 0.220 0.241 0.273
187 21 0 0.220 0.241 0.273
188 21 0 0.220 0.241 0.273
189 21 0 0.220 0.241 0.273
190 21 0 0.220 0.241 0.273
191 21 0 0.220 0.241 0.273
192 21 0 0.220 0.241 0.273
193 21 0 0.220 0.241 0.273
194 21 0 0.220 0.241 0.273
195 21 0 0.220 0.241 0.273
196 21 0 0.220 0.241 0.273
197 21 0 0.220 0.241 0.273
198 21 0 0.220 0.241 0.273
199 21 0 0.220 0.241 0.273
200 21 0 0.220 0.241 0.273
201 21 0 0.220 0.241 0.273
202 21 0 0.220 0.241 0.273
203 21 0 0.220 0.241 0.273
204 21 0 0.220 0.241 0.273
205 21 0 0.220 0.241 0.273
206 21 0 0.220 0.241 0.273
207 21 0 0.220 0.241 0.273
208 22 0 0.286 0.313 0.344
209 22 0 0.286 0.313 0.344
210 22 0 0.286 0.313 0.344
211 22 0 0.286 0.313 0.344
212 22 0 0.286 0.313 0.344
213 22 0 0.286 0.313 0.344
214 22 0 0.286 0.313 0.344
215 22 0 0.286 0.313 0.344
216 22 0 0.286 0.313 0.344
217 22 0 0.286 0.313 0.344
218 22 0 0.286 0.313 0.344
219 22 0 0.286 0.313 0.344
220 22 0 0.286 0.313 0.344
221 22 0 0.286 0.313 0.344
222 22 0 0.286 0.313 0.344
223 22 0 0.286 0.313 0.344
224 22 0 0.286 0.313 0.344
225 22 0 0.286 0.313 0.344
226 22 0 0.286 0.313 0.344
227 22 0 0.286 0.313 0.344
228 22 0 0.286 0.313 0.344
229 22 0 0.286 0.313 0.344
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