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  • 1
    UID:
    almahu_9948104817202882
    Format: XX, 638 p. 207 illus., 127 illus. in color. , online resource.
    ISBN: 9781484228722
    Content: Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. You will: Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability .
    Note: 1 Univariate Data Visualization -- 2 Multivariate Data Visualization -- 3 Generalized Linear Models 1 -- 4 Generalized Linear Models 2 -- 5 Generalized Additive Models -- 6 Machine Learning: Introduction -- 7 Machine Learning: Unsupervised -- 8 Machine Learning: Supervised -- 9 Missing Data -- 10 Generalized Linear Mixed Models: Introduction -- 11 Generalized Linear Mixed Models: Linear -- 12 Generalized Linear Mixed Models: Advanced -- 13 Modeling IIV -- Bibliography.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9781484228715
    Additional Edition: Printed edition: ISBN 9781484228739
    Language: English
    Subjects: Computer Science , Economics , Mathematics
    RVK:
    RVK:
    RVK:
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    almafu_9959043116802883
    Format: 1 online resource (XX, 638 p. 207 illus., 127 illus. in color.)
    Edition: 1st ed. 2019.
    ISBN: 9781523150311 , 1523150319 , 9781484228722 , 1484228723
    Content: Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. You will: Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability .
    Note: 1 Univariate Data Visualization -- 2 Multivariate Data Visualization -- 3 Generalized Linear Models 1 -- 4 Generalized Linear Models 2 -- 5 Generalized Additive Models -- 6 Machine Learning: Introduction -- 7 Machine Learning: Unsupervised -- 8 Machine Learning: Supervised -- 9 Missing Data -- 10 Generalized Linear Mixed Models: Introduction -- 11 Generalized Linear Mixed Models: Linear -- 12 Generalized Linear Mixed Models: Advanced -- 13 Modeling IIV -- Bibliography.
    Additional Edition: ISBN 9781484228715
    Additional Edition: ISBN 1484228715
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    UID:
    b3kat_BV046182060
    Format: xx,638 Seiten , Diagramme , 25.4 cm x 17.8 cm
    ISBN: 9781484228715 , 1484228715
    Additional Edition: Elektronische Reproduktion ISBN 9781484228722
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-4842-2872-2
    Language: English
    Keywords: Statistik ; R ; Programmierung ; Datenanalyse ; Maschinelles Lernen ; Visualisierung
    Library Location Call Number Volume/Issue/Year Availability
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