I would like to ask if you can help me in interpreting the results of my analysis or if perhaps you know a paper or a book where this is nicely explained. I am conducting an analysis in order to check whether the experimental method (Field
or pot
) has an influence on the crop response to zinc fertilizations. Here below, I put the model B4 I used: RR
response ratio, RRv
variance of response ratio, mods
experiment type (field
, pot
or aqua
).
Model: B4 = rma.mv(RR,RRv,mods=~factor(exp),random=list(~ 1| Paper), intercept = FALSE, data=DataZn,sparse=TRUE)
Multivariate Meta-Analysis Model (k = 1968; method: REML)
logLik Deviance AIC BIC AICc
-613607.7310 1227215.4621 1227223.4621 1227245.7950 1227223.4825
Variance Components:
estim sqrt nlvls fixed factor
sigma^2 0.0639 0.2529 56 no Paper
Test for Residual Heterogeneity:
QE(df = 1965) = 2169043.4451, p-val < .0001
Test of Moderators (coefficient(s) 2,3):
QM(df = 2) = 12.6162, p-val = 0.0018
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.7342 0.2532 2.8993 0.0037 0.2379 1.2306
factor(exp)Field -0.3896 0.2562 -1.5206 0.1284 -0.8917 0.1126
factor(exp)Pot -0.1063 0.2645 -0.4020 0.6877 -0.6247 0.4121
help(rma.mv)
. To quote: "When specifying a model formula via themods
argument, theintercept
argument is ignored. Instead, the inclusion/exclusion of the intercept term is controlled by the specified formula." $\endgroup$