ACDC 2019 Naturalist |
Analysis 04 : Détails of pre-analyses |
Study type: Point transect, Radial distance, No clustering.
Units used: Meter for distances, Sq. Kilometer for areas.
Note: Most figures have been rounded for readability, but 'CoefVar Density' have been further modified : converted to %
Echant | Espèce | Passage | Adulte | Durée | Mod Key Fn | Mod Adj Ser | ExCod | NObs | Max Dist | Effort | AIC | Chi2 P | KS P | CoefVar Density | Density | Min Density | Max Density | Number | Min Number | Max Number | EDR/ESW | Min EDR/ESW | Max EDR/ESW | PDetec | Min PDetec | Max PDetec | RunFolder | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
04 | 3 | Prunella modularis | a+b | m | 10mn | UNIFORM | COSINE | 2 | 47 | 271.221 | 190 | 494.6 | 0.21 | 0.47 | 24.2 | 6.24 | 3.89 | 9.99 | 150 | 93 | 240 | 112.4 | 95.3 | 132.5 | 0.172 | 0.124 | 0.238 | PrunModu-ab-10mn-m-uni-cos-f3smf1_a |
Study type: Point transect, Radial distance, No clustering.
Units used: Meter for distances, Sq. Kilometer for areas.
Note: All values have been left untouched, as output by MCDS (no rounding, no conversion)
Echant | Espèce | Passage | Adulte | Durée | Abrev. Echant | NTot Obs | Min Dist | Max Dist | Mod Key Fn | Mod Adj Ser | Mod Chc Crit | Conf Interv | ExCod | StartTime | ElapsedTime | RunFolder | NObs | NSamp | Effort | EncRate | CoefVar EncRate | Min EncRate | Max EncRate | DoF EncRate | Left Trunc | Right Trunc | Obs Rate | TotNum Pars | Delta AIC | AIC | Chi2 P | Chi2 P 1 | Chi2 P 2 | Chi2 P 3 | f/h(0) | CoefVar f/h(0) | Min f/h(0) | Max f/h(0) | DoF f/h(0) | PDetec | CoefVar PDetec | Min PDetec | Max PDetec | DoF PDetec | EDR/ESW | CoefVar EDR/ESW | Min EDR/ESW | Max EDR/ESW | DoF EDR/ESW | AICc | BIC | LogLhood | KS P | CvM Uw P | CvM Cw P | Key Fn | Adj Ser | NumPars KeyFn | NumPars AdjSer | Num Covars | EstA(1) | EstA(2) | DensClu | CoefVar DensClu | Min DensClu | Max DensClu | DoF DensClu | Density | Delta CoefVar Density | CoefVar Density | Min Density | Max Density | DoF Density | Number | CoefVar Number | Min Number | Max Number | DoF Number | Qual Bal 1 | Qual Bal 2 | Qual Bal 3 | Qual Chi2+ | Qual KS+ | Qual DCv+ | |
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04 | 3 | Prunella modularis | a+b | m | 10mn | PrunModu-ab-10mn-m | 47.000000 | 10.176839 | 271.221090 | UNIFORM | COSINE | AIC | 95 | 2 | 2023-04-16 18:55:40.254027 | 0.523999 | PrunModu-ab-10mn-m-uni-cos-f3smf1_a | 47.000000 | 96.000000 | 190.000000 | 0.247368 | 0.177070 | 0.174524 | 0.350618 | 95.000000 | 0.000000 | 271.221100 | 100.000000 | 2.000000 | 0.000000 | 494.608800 | 0.212556 | 0.365375 | 0.041433 | 0.212556 | 0.000158 | 0.164232 | 0.000114 | 0.000220 | 45.000000 | 0.171655 | 0.164232 | 0.123582 | 0.238429 | 45.000000 | 112.370500 | 0.082116 | 95.267530 | 132.543800 | 45.000000 | 494.881600 | 498.309100 | -245.304400 | 0.473960 | 0.600000 | 0.600000 | UNIFORM | COSINE | 0.000000 | 2.000000 | 0.000000 | 1.331354 | 0.351025 | 6.235769 | 0.241508 | 3.893096 | 9.988147 | 128.303600 | 6.235769 | 0.000000 | 0.241508 | 3.893096 | 9.988147 | 128.303600 | 150.000000 | 0.241508 | 93.000000 | 240.000000 | 128.303600 | 0.563369 | 0.522783 | 0.516768 | 0.468195 | 0.511827 | 0.487815 |
This is mcds.exe version 6.2.0 Options; Type=Point; Distance=Radial /Measure='Meter'; Area /Units='Sq. Kilometer'; Object=Single; SF=1; Selection=Sequential; Lookahead=1; Maxterms=5; Confidence=95; print=Selection; End; Data /Structure=Flat; Fields=STR_LABEL,STR_AREA,SMP_LABEL,SMP_EFFORT,DISTANCE; Infile=pranlys\230416-180310\PrunModu-ab-10mn-m-uni-cos-f3smf1_a\data.txt /NoEcho; Data will be input from file - [...]MN-M-UNI-COS-F3SMF1_A\DATA.TXT End; Dataset has been stored. Estimate; Distance; Density=All; Encounter=All; Detection=All; Size=All; Estimator /Key=UNIFORM /Adjust=COSINE /Criterion=AIC; Monotone=Strict; Pick=AIC; GOF; Cluster /Bias=GXLOG; VarN=Empirical; End; ** Warning: Parameters are being constrained to obtain monotonicity. ** ** Warning: Parameters are being constrained to obtain monotonicity. **
Parameter Estimation Specification ---------------------------------- Encounter rate for all data combined Detection probability for all data combined Density for all data combined Distances: ---------- Analysis based on exact distances Width: use largest measurement/last interval endpoint Estimators: ----------- Estimator 1 Key: Uniform Adjustments - Function : Cosines - Term selection mode : Sequential - Term selection criterion : Akaike Information Criterion (AIC) - Distances scaled by : W (right truncation distance) Estimator selection: Choose estimator with minimum AIC Estimation functions: constrained to be nearly monotone non-increasing Variances: ---------- Variance of n: Empirical estimate from sample (design-derived estimator R2/P2) Variance of f(0): MLE estimate Goodness of fit: ---------------- Cut points chosen by program Glossary of terms ----------------- Data items: n - number of observed objects (single or clusters of animals) L - total length of transect line(s) k - number of samples K - point transect effort, typically K=k T - length of time searched in cue counting ER - encounter rate (n/L or n/K or n/T) W - width of line transect or radius of point transect x(i) - distance to i-th observation s(i) - cluster size of i-th observation r-p - probability for regression test chi-p- probability for chi-square goodness-of-fit test Parameters or functions of parameters: m - number of parameters in the model A(I) - i-th parameter in the estimated probability density function(pdf) f(0) - 1/u = value of pdf at zero for line transects u - W*p = ESW, effective detection area for line transects h(0) - 2*PI/v v - PI*W*W*p, is the effective detection area for point transects p - probability of observing an object in defined area ESW - for line transects, effective strip width = W*p EDR - for point transects, effective detection radius = W*sqrt(p) rho - for cue counts, the cue rate DS - estimate of density of clusters E(S) - estimate of expected value of cluster size D - estimate of density of animals N - estimate of number of animals in specified area
Effort : 190.0000 # samples : 96 Width : 271.2211 # observations: 47 Model 1 Uniform key, k(y) = 1/W Results: Convergence was achieved with 1 function evaluations. Final Ln(likelihood) value = -285.35577 Akaike information criterion = 570.71155 Bayesian information criterion = 570.71155 AICc = 570.71155 Final parameter values: Model 2 Uniform key, k(y) = 1/W Cosine adjustments of order(s) : 1 Results: Convergence was achieved with 24 function evaluations. Final Ln(likelihood) value = -252.28137 Akaike information criterion = 506.56274 Bayesian information criterion = 508.41290 AICc = 506.65164 Final parameter values: 0.97570701 Likelihood ratio test between models 1 and 2 Likelihood ratio test value = 66.1488 Probability of a greater value = 0.000000 *** Model 2 selected over model 1 based on minimum AIC Model 3 Uniform key, k(y) = 1/W Cosine adjustments of order(s) : 1, 2 Results: Convergence was achieved with 33 function evaluations. Final Ln(likelihood) value = -245.30442 Akaike information criterion = 494.60883 Bayesian information criterion = 498.30914 AICc = 494.88156 Final parameter values: 1.3313540 0.35102447 ** Warning: Parameters are being constrained to obtain monotonicity. ** Likelihood ratio test between models 2 and 3 Likelihood ratio test value = 13.9539 Probability of a greater value = 0.000187 *** Model 3 selected over model 2 based on minimum AIC Model 4 Uniform key, k(y) = 1/W Cosine adjustments of order(s) : 1, 2, 3 Results: Convergence was achieved with 16 function evaluations. Final Ln(likelihood) value = -527.06363 Akaike information criterion = 1060.1273 Bayesian information criterion = 1065.6777 AICc = 1060.6854 Final parameter values: 61575.195 -27718.076 -61572.895 ** Warning: Parameters are being constrained to obtain monotonicity. ** Likelihood ratio test between models 3 and 4 Likelihood ratio test value = -563.5184 Probability of a greater value = 1.000000 *** Model 3 selected over model 4 based on minimum AIC
Effort : 190.0000 # samples : 96 Width : 271.2211 # observations: 47 Model Uniform key, k(y) = 1/W Cosine adjustments of order(s) : 1, 2 Point Standard Percent Coef. 95 Percent Parameter Estimate Error of Variation Confidence Interval --------- ----------- ----------- -------------- ---------------------- A( 1) 1.331 0.1018 A( 2) 0.3510 0.1000 h(0) 0.15839E-03 0.26013E-04 16.42 0.11403E-03 0.22000E-03 p 0.17166 0.28191E-01 16.42 0.12358 0.23843 EDR 112.37 9.2274 8.21 95.268 132.54 --------- ----------- ----------- -------------- ---------------------- Sampling Correlation of Estimated Parameters A( 1) A( 2) A( 1) 1.000 0.981 A( 2) 0.981 1.000
Kolmogorov-Smirnov test ----------------------- D_n = 0.1232 p = 0.4740 Cramer-von Mises family tests ----------------------------- W-sq (uniform weighting) = 0.1046 0.500 < p <= 0.600 Relevant critical values: W-sq crit(alpha=0.600) = 0.0974 W-sq crit(alpha=0.500) = 0.1193 C-sq (cosine weighting) = 0.0668 0.500 < p <= 0.600 Relevant critical values: C-sq crit(alpha=0.600) = 0.0625 C-sq crit(alpha=0.500) = 0.0773
Cell Cut Observed Expected Chi-square i Points Values Values Values ----------------------------------------------------------------- 1 0.000 67.8 16 14.65 0.125 2 67.8 136. 24 22.97 0.047 3 136. 203. 6 8.31 0.642 4 203. 271. 1 1.08 0.006 ----------------------------------------------------------------- Total Chi-square value = 0.8193 Degrees of Freedom = 1.00 Probability of a greater chi-square value, P = 0.36538 The program has limited capability for pooling. The user should judge the necessity for pooling and if necessary, do pooling by hand.
Cell Cut Observed Expected Chi-square i Points Values Values Values ----------------------------------------------------------------- 1 0.000 45.2 7 7.09 0.001 2 45.2 90.4 11 16.02 1.575 3 90.4 136. 22 14.49 3.890 4 136. 181. 6 7.09 0.168 5 181. 226. 0 1.71 1.706 6 226. 271. 1 0.59 0.284 ----------------------------------------------------------------- Total Chi-square value = 7.6250 Degrees of Freedom = 3.00 Probability of a greater chi-square value, P = 0.05443 The program has limited capability for pooling. The user should judge the necessity for pooling and if necessary, do pooling by hand. Goodness of Fit Testing with some Pooling Cell Cut Observed Expected Chi-square i Points Values Values Values ----------------------------------------------------------------- 1 0.000 45.2 7 7.09 0.001 2 45.2 90.4 11 16.02 1.575 3 90.4 136. 22 14.49 3.890 4 136. 181. 6 7.09 0.168 5 181. 271. 1 2.30 0.732 ----------------------------------------------------------------- Total Chi-square value = 6.3673 Degrees of Freedom = 2.00 Probability of a greater chi-square value, P = 0.04143
Cell Cut Observed Expected Chi-square i Points Values Values Values ----------------------------------------------------------------- 1 0.000 27.1 2 2.67 0.168 2 27.1 54.2 9 7.24 0.428 3 54.2 81.4 6 9.80 1.475 4 81.4 108. 10 9.90 0.001 5 108. 136. 13 7.99 3.134 6 136. 163. 4 5.15 0.258 7 163. 190. 2 2.56 0.121 8 190. 217. 0 0.94 0.943 9 217. 244. 0 0.37 0.372 10 244. 271. 1 0.37 1.102 ----------------------------------------------------------------- Total Chi-square value = 8.0034 Degrees of Freedom = 7.00 Probability of a greater chi-square value, P = 0.33229 The program has limited capability for pooling. The user should judge the necessity for pooling and if necessary, do pooling by hand. Goodness of Fit Testing with some Pooling Cell Cut Observed Expected Chi-square i Points Values Values Values ----------------------------------------------------------------- 1 0.000 27.1 2 2.67 0.168 2 27.1 54.2 9 7.24 0.428 3 54.2 81.4 6 9.80 1.475 4 81.4 108. 10 9.90 0.001 5 108. 136. 13 7.99 3.134 6 136. 163. 4 5.15 0.258 7 163. 271. 3 4.24 0.361 ----------------------------------------------------------------- Total Chi-square value = 5.8256 Degrees of Freedom = 4.00 Probability of a greater chi-square value, P = 0.21256
Effort : 190.0000 # samples : 96 Width : 271.2211 # observations: 47 Model 3 Uniform key, k(y) = 1/W Cosine adjustments of order(s) : 1, 2 Point Standard Percent Coef. 95% Percent Parameter Estimate Error of Variation Confidence Interval --------- ----------- ----------- -------------- ---------------------- D 6.2358 1.5060 24.15 3.8931 9.9881 N 150.00 36.226 24.15 93.000 240.00 --------- ----------- ----------- -------------- ---------------------- Measurement Units --------------------------------- Density: Numbers/Sq. kilometers EDR: meters Component Percentages of Var(D) ------------------------------- Detection probability : 46.2 Encounter rate : 53.8
Estimate %CV df 95% Confidence Interval ------------------------------------------------------ n 47.000 k 96.000 K 190.00 n/K 0.24737 17.71 95.00 0.17452 0.35062 Left 0.0000 Width 271.22
Estimate %CV df 95% Confidence Interval ------------------------------------------------------ Uniform/Cosine m 2.0000 LnL -245.30 AIC 494.61 AICc 494.88 BIC 498.31 Chi-p 0.21256 h(0) 0.15839E-03 16.42 45.00 0.11403E-03 0.22000E-03 p 0.17166 16.42 45.00 0.12358 0.23843 EDR 112.37 8.21 45.00 95.268 132.54
Estimate %CV df 95% Confidence Interval ------------------------------------------------------ Uniform/Cosine D 6.2358 24.15 128.30 3.8931 9.9881 N 150.00 24.15 128.30 93.000 240.00