Attributes | Values |
---|
rdf:type
| |
Description
| - Generalized linear models (GLMs) are increasingly used in modern statistical analyses of sex ratio variation because they are able to determine variable design effects on binary response data. However, in applying GLMs, authors frequently neglect the hierarchical structure of sex ratio data, thereby increasing the likelihood of committing 'type I' error. Here, we argue that whenever clustered (e.g., brood) sex ratios represent the desired level of statistical inference, the clustered data structure ought to be taken into account to avoid invalid conclusions. Neglecting the between-cluster variation and the finite number of clusters in determining test statistics, as implied by using likelihood ratio-based chi (2)-statistics in conventional GLM, results in biased (usually overestimated) test statistics and pseudoreplication of the sample. Random variation in the sex ratio between clusters (broods) can often be accommodated by scaling residual binomial (error) variance for overdispersion, and using F-te
- Generalized linear models (GLMs) are increasingly used in modern statistical analyses of sex ratio variation because they are able to determine variable design effects on binary response data. However, in applying GLMs, authors frequently neglect the hierarchical structure of sex ratio data, thereby increasing the likelihood of committing 'type I' error. Here, we argue that whenever clustered (e.g., brood) sex ratios represent the desired level of statistical inference, the clustered data structure ought to be taken into account to avoid invalid conclusions. Neglecting the between-cluster variation and the finite number of clusters in determining test statistics, as implied by using likelihood ratio-based chi (2)-statistics in conventional GLM, results in biased (usually overestimated) test statistics and pseudoreplication of the sample. Random variation in the sex ratio between clusters (broods) can often be accommodated by scaling residual binomial (error) variance for overdispersion, and using F-te (en)
|
Title
| - Analysis of brood sex ratios: implications of offspring clustering.
- Analysis of brood sex ratios: implications of offspring clustering. (en)
|
skos:prefLabel
| - Analysis of brood sex ratios: implications of offspring clustering.
- Analysis of brood sex ratios: implications of offspring clustering. (en)
|
skos:notation
| - RIV/68081766:_____/01:67010007!RIV/2003/AV0/A67003/N
|
http://linked.open.../vavai/riv/strany
| |
http://linked.open...avai/riv/aktivita
| |
http://linked.open...avai/riv/aktivity
| - P(GA524/01/1316), P(GA524/96/1095), Z(AV0Z6093917)
|
http://linked.open...iv/cisloPeriodika
| |
http://linked.open...vai/riv/dodaniDat
| |
http://linked.open...aciTvurceVysledku
| |
http://linked.open.../riv/druhVysledku
| |
http://linked.open...iv/duvernostUdaju
| |
http://linked.open...titaPredkladatele
| |
http://linked.open...dnocenehoVysledku
| |
http://linked.open...ai/riv/idVysledku
| - RIV/68081766:_____/01:67010007
|
http://linked.open...riv/jazykVysledku
| |
http://linked.open.../riv/klicovaSlova
| - generalized linear mixed models; random coefficients; multilevel analysis (en)
|
http://linked.open.../riv/klicoveSlovo
| |
http://linked.open...odStatuVydavatele
| - DE - Spolková republika Německo
|
http://linked.open...ontrolniKodProRIV
| |
http://linked.open...i/riv/nazevZdroje
| - Behavioral Ecology and Sociobiology
|
http://linked.open...in/vavai/riv/obor
| |
http://linked.open...ichTvurcuVysledku
| |
http://linked.open...cetTvurcuVysledku
| |
http://linked.open...ocetUcastnikuAkce
| |
http://linked.open...nichUcastnikuAkce
| |
http://linked.open...vavai/riv/projekt
| |
http://linked.open...UplatneniVysledku
| |
http://linked.open...v/svazekPeriodika
| |
http://linked.open...iv/tvurceVysledku
| - Tkadlec, Emil
- Krackow, S.
|
http://linked.open...n/vavai/riv/zamer
| |
issn
| |
number of pages
| |