TY - JOUR
T1 - Generalized linear modelling for parasitologists
AU - Wilson, Kenneth
AU - Grenfell, Bryan T.
PY - 1997/1/1
Y1 - 1997/1/1
N2 - Typically, the distribution of macroparasites over their host population is highly aggregated and empirically best described by the negative binomial distribution. For parasitologists, this poses a statistical problem, which is often tackled by log-transforming the parasite data prior to analysis by parametric tests. Here, Ken Wilson and Bryan Grenfell show that this method is particularly prone to type 1 errors, and highlight a much more powerful and flexible alternative: generalized linear modelling.
AB - Typically, the distribution of macroparasites over their host population is highly aggregated and empirically best described by the negative binomial distribution. For parasitologists, this poses a statistical problem, which is often tackled by log-transforming the parasite data prior to analysis by parametric tests. Here, Ken Wilson and Bryan Grenfell show that this method is particularly prone to type 1 errors, and highlight a much more powerful and flexible alternative: generalized linear modelling.
UR - http://www.scopus.com/inward/record.url?scp=0031025135&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0031025135&partnerID=8YFLogxK
U2 - 10.1016/S0169-4758(96)40009-6
DO - 10.1016/S0169-4758(96)40009-6
M3 - Article
C2 - 15275165
AN - SCOPUS:0031025135
SN - 1471-4922
VL - 13
SP - 33
EP - 38
JO - Trends in Parasitology
JF - Trends in Parasitology
IS - 1
ER -