LEADER 00932nam a2200241 i 4500 001 991003877259707536 005 20020509133646.0 008 000215s1993 it ||| | ita 020 $a882211275X 035 $ab11228131-39ule_inst 035 $aPARLA190410$9ExL 040 $aDip.to Filosofia$bita 100 1 $aDe Luna, Giovanni$0142943 245 12$aL'occhio e l'orecchio dello storico :$ble fonti audiovisive nella ricerca e nella didattica della storia /$cGiovanni de Luna 260 $aScandicci (Fi) :$bLa nuova italia,$c1993 300 $a206 p. ;$c21 cm. 490 0 $aBiblioteca di storia ;$v45 907 $a.b11228131$b23-02-17$c01-07-02 912 $a991003877259707536 945 $aLE005 MF 51 E 19$g1$i2005000060878$lle005$o-$pE0.00$q-$rl$s- $t0$u2$v2$w2$x0$y.i1138315x$z01-07-02 996 $aOcchio e l'orecchio dello storico$9553262 997 $aUNISALENTO 998 $ale005$b01-01-00$cm$da $e-$fita$git $h2$i1 LEADER 00614nam a2200181 u 4500 001 991004297436107536 005 20231123164042.0 008 231123s1964 xxk r 000 0 eng d 040 $aBibl. Interfacoltà T. Pellegrino$bita 041 0 $aeng 100 1 $aFrançon, Marcel$0412752 245 10$aTwo Notes on "Gargantua and Pantagruel" /$cMarcel Françon 260 $a[Great Britain] :$b[s n.],$c1964 300 $aP. 371-374 ;$c23 cm 500 $aEstr. da: The Modern Language Review, v. 59, n. 3, 1964 912 $a991004297436107536 996 $aTwo Notes on "Gargantua and Pantagruel"$93597193 997 $aUNISALENTO LEADER 05122nam 22005653u 450 001 9910779568203321 005 20210114035523.0 010 $a1-118-56252-6 010 $a1-299-46461-0 010 $a1-118-56253-4 035 $a(CKB)2550000001019379 035 $a(EBL)1165084 035 $a(OCoLC)841908107 035 $a(MiAaPQ)EBC1165084 035 $a(PPN)178195472 035 $a(EXLCZ)992550000001019379 100 $a20131230d2013|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 200 10$aData Analysis in Vegetation Ecology$b[electronic resource] 205 $a2nd ed. 210 $aHoboken $cWiley$d2013 215 $a1 online resource (332 p.) 300 $aDescription based upon print version of record. 311 $a1-118-56254-2 311 $a1-118-38403-2 327 $aCover; Title Page; Copyright; Contents; Preface to the second edition; Preface to the first edition; List of figures; List of tables; About the companion website; Chapter 1 Introduction; Chapter 2 Patterns in vegetation ecology; 2.1 Pattern recognition; 2.2 Interpretation of patterns; 2.3 Sampling for pattern recognition; 2.3.1 Getting a sample; 2.3.2 Organizing the data; 2.4 Pattern recognition in R; Chapter 3 Transformation; 3.1 Data types; 3.2 Scalar transformation and the species enigma; 3.3 Vector transformation; 3.4 Example: Transformation of plant cover data 327 $aChapter 4 Multivariate comparison4.1 Resemblance in multivariate space; 4.2 Geometric approach; 4.3 Contingency measures; 4.4 Product moments; 4.5 The resemblance matrix; 4.6 Assessing the quality of classifications; Chapter 5 Classification; 5.1 Group structures; 5.2 Linkage clustering; 5.3 Average linkage clustering; 5.4 Minimum-variance clustering; 5.5 Forming groups; 5.6 Silhouette plot and fuzzy representation; Chapter 6 Ordination; 6.1 Why ordination?; 6.2 Principal component analysis; 6.3 Principal coordinates analysis; 6.4 Correspondence analysis; 6.5 Heuristic ordination 327 $a6.5.1 The horseshoe or arch effect6.5.2 Flexible shortest path adjustment; 6.5.3 Nonmetric multidimensional scaling; 6.5.4 Detrended correspondence analysis; 6.6 How to interpret ordinations; 6.7 Ranking by orthogonal components; 6.7.1 RANK method; 6.7.2 A sampling design based on RANK (example); Chapter 7 Ecological patterns; 7.1 Pattern and ecological response; 7.2 Evaluating groups; 7.2.1 Variance testing; 7.2.2 Variance ranking; 7.2.3 Ranking by indicator values; 7.2.4 Contingency tables; 7.3 Correlating spaces; 7.3.1 The Mantel test; 7.3.2 Correlograms 327 $a7.3.3 More trends: `Schlaenggli' data revisited7.4 Multivariate linear models; 7.4.1 Constrained ordination; 7.4.2 Nonparametric multiple analysis of variance; 7.5 Synoptic vegetation tables; 7.5.1 The aim of ordering tables; 7.5.2 Steps involved in sorting tables; 7.5.3 Example: ordering Ellenberg's data; Chapter 8 Static predictive modelling; 8.1 Predictive or explanatory?; 8.2 Evaluating environmental predictors; 8.3 Generalized linear models; 8.4 Generalized additive models; 8.5 Classification and regression trees; 8.6 Building scenarios; 8.7 Modelling vegetation types 327 $a8.8 Expected wetland vegetation (example)Chapter 9 Vegetation change in time; 9.1 Coping with time; 9.2 Temporal autocorrelation; 9.3 Rate of change and trend; 9.4 Markov models; 9.5 Space-for-time substitution; 9.5.1 Principle and method; 9.5.2 The Swiss National Park succession (example); 9.6 Dynamics in pollen diagrams (example); Chapter 10 Dynamic modelling; 10.1 Simulating time processes; 10.2 Simulating space processes; 10.3 Processes in the Swiss National Park; 10.3.1 The temporal model; 10.3.2 The spatial model; Chapter 11 Large data sets: wetland patterns; 11.1 Large data sets differ 327 $a11.2 Phytosociology revisited 330 $a The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author's extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only describing how to analyse data, but also enabling readers to follow the step-by-step case studies themselves using the freely available statistical package R. The addition of R in this new edition has allowed coverage of additional methods for classification and ordination, and also logistic regression, GLM 606 $aPlant communities -- Data processing 606 $aPlant communities -- Mathematical models 606 $aPlant ecology -- Data processing 606 $aPlant ecology -- Mathematical models 615 4$aPlant communities -- Data processing. 615 4$aPlant communities -- Mathematical models. 615 4$aPlant ecology -- Data processing. 615 4$aPlant ecology -- Mathematical models. 676 $a581.70285 700 $aWildi$b Otto$0953188 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910779568203321 996 $aData analysis in vegetation ecology$92154905 997 $aUNINA