語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
From experimental network to meta-an...
~
Makowski, David.
FindBook
Google Book
Amazon
博客來
From experimental network to meta-analysis = methods and applications with R for agronomic and environmental sciences /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
From experimental network to meta-analysis/ by David Makowski, Francois Piraux, Francois Brun.
其他題名:
methods and applications with R for agronomic and environmental sciences /
作者:
Makowski, David.
其他作者:
Piraux, Francois.
出版者:
Dordrecht :Springer Netherlands : : 2019.,
面頁冊數:
x, 155 p. :ill., digital ;24 cm.
內容註:
Chapter 1. Introduction and examples -- Part I. Analysis of experimental networks -- Chapter 2. Basic Concepts -- Chapter 3. Analysis of network of experiments in blocks of complete randomness as a studied factor -- Chapter 4. Advanced Methods for Network Analysis -- Chapter 5. Planning an Experimental Network -- Part II. The meta-analysis -- Chapter 6. Basics for meta-analysis -- Chapter 7. Specific statistical problems for the meta-analysis -- Annex. R resources to implement the methods of analysis networks and meta-analysis -- Package Codes.
Contained By:
Springer eBooks
標題:
System analysis. -
電子資源:
https://doi.org/10.1007/978-94-024-1696-1
ISBN:
9789402416961
From experimental network to meta-analysis = methods and applications with R for agronomic and environmental sciences /
Makowski, David.
From experimental network to meta-analysis
methods and applications with R for agronomic and environmental sciences /[electronic resource] :by David Makowski, Francois Piraux, Francois Brun. - Dordrecht :Springer Netherlands :2019. - x, 155 p. :ill., digital ;24 cm.
Chapter 1. Introduction and examples -- Part I. Analysis of experimental networks -- Chapter 2. Basic Concepts -- Chapter 3. Analysis of network of experiments in blocks of complete randomness as a studied factor -- Chapter 4. Advanced Methods for Network Analysis -- Chapter 5. Planning an Experimental Network -- Part II. The meta-analysis -- Chapter 6. Basics for meta-analysis -- Chapter 7. Specific statistical problems for the meta-analysis -- Annex. R resources to implement the methods of analysis networks and meta-analysis -- Package Codes.
Data analysis plays an increasing role in research, scientific expertise and prospective studies. Multiple data sources are often available to estimate a key parameter or to test a hypothesis of scientific or societal interest. These data, obtained under different environmental conditions or based on different experimental protocols, are generally heterogeneous. Sometimes they are not even directly accessible and should be extracted from scientific articles or reports. However, a comprehensive analysis of the available data is essential to increase the accuracy of estimates, assess the validity of research conclusions and understand the origin of the variability of the experimental results. A quantitative synthesis of the data set available allows for a better understanding of the effects of explanatory factors and for evidence-based recommendations. Designed as a methodological guide, this book shows the interests and limitations of different statistical methods to analyze data from experimental networks and to perform meta-analyses. It is intended for engineers, students and researchers involved in data analysis in agronomy and environmental science. Our objective is to present the main statistical methods to analyze data from experimental networks and scientific publications. Each chapter exposes one or more methods and illustrates them with examples processed with the R software. Data and R codes are provided and commented in order to facilitate their adaptation to other situations. The codes can be reused from the KenSyn R package associated with this book.
ISBN: 9789402416961
Standard No.: 10.1007/978-94-024-1696-1doiSubjects--Topical Terms:
545616
System analysis.
LC Class. No.: QA402 / .M356 2019
Dewey Class. No.: 003
From experimental network to meta-analysis = methods and applications with R for agronomic and environmental sciences /
LDR
:03204nmm a2200325 a 4500
001
2191113
003
DE-He213
005
20191016153750.0
006
m d
007
cr nn 008maaau
008
200504s2019 ne s 0 eng d
020
$a
9789402416961
$q
(electronic bk.)
020
$a
9789402416954
$q
(paper)
024
7
$a
10.1007/978-94-024-1696-1
$2
doi
035
$a
978-94-024-1696-1
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA402
$b
.M356 2019
072
7
$a
TVB
$2
bicssc
072
7
$a
TEC003000
$2
bisacsh
072
7
$a
TVB
$2
thema
082
0 4
$a
003
$2
23
090
$a
QA402
$b
.M235 2019
100
1
$a
Makowski, David.
$3
3410197
245
1 0
$a
From experimental network to meta-analysis
$h
[electronic resource] :
$b
methods and applications with R for agronomic and environmental sciences /
$c
by David Makowski, Francois Piraux, Francois Brun.
260
$a
Dordrecht :
$b
Springer Netherlands :
$b
Imprint: Springer,
$c
2019.
300
$a
x, 155 p. :
$b
ill., digital ;
$c
24 cm.
505
0
$a
Chapter 1. Introduction and examples -- Part I. Analysis of experimental networks -- Chapter 2. Basic Concepts -- Chapter 3. Analysis of network of experiments in blocks of complete randomness as a studied factor -- Chapter 4. Advanced Methods for Network Analysis -- Chapter 5. Planning an Experimental Network -- Part II. The meta-analysis -- Chapter 6. Basics for meta-analysis -- Chapter 7. Specific statistical problems for the meta-analysis -- Annex. R resources to implement the methods of analysis networks and meta-analysis -- Package Codes.
520
$a
Data analysis plays an increasing role in research, scientific expertise and prospective studies. Multiple data sources are often available to estimate a key parameter or to test a hypothesis of scientific or societal interest. These data, obtained under different environmental conditions or based on different experimental protocols, are generally heterogeneous. Sometimes they are not even directly accessible and should be extracted from scientific articles or reports. However, a comprehensive analysis of the available data is essential to increase the accuracy of estimates, assess the validity of research conclusions and understand the origin of the variability of the experimental results. A quantitative synthesis of the data set available allows for a better understanding of the effects of explanatory factors and for evidence-based recommendations. Designed as a methodological guide, this book shows the interests and limitations of different statistical methods to analyze data from experimental networks and to perform meta-analyses. It is intended for engineers, students and researchers involved in data analysis in agronomy and environmental science. Our objective is to present the main statistical methods to analyze data from experimental networks and scientific publications. Each chapter exposes one or more methods and illustrates them with examples processed with the R software. Data and R codes are provided and commented in order to facilitate their adaptation to other situations. The codes can be reused from the KenSyn R package associated with this book.
650
0
$a
System analysis.
$3
545616
650
0
$a
R (Computer program language)
$3
784593
650
0
$a
Environmental sciences
$x
Research
$x
Methodology.
$3
3410199
650
0
$a
Agriculture
$x
Research
$x
Methodology.
$3
3410200
650
1 4
$a
Agriculture.
$3
518588
650
2 4
$a
Plant Sciences.
$3
894664
650
2 4
$a
Statistical Theory and Methods.
$3
891074
650
2 4
$a
Environmental Science and Engineering.
$3
1569104
700
1
$a
Piraux, Francois.
$3
3410198
700
1
$a
Brun, Francois.
$3
2151822
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer eBooks
856
4 0
$u
https://doi.org/10.1007/978-94-024-1696-1
950
$a
Biomedical and Life Sciences (Springer-11642)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9373757
電子資源
11.線上閱覽_V
電子書
EB QA402 .M356 2019
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入