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The effect of water deficits on tested variables were examined using a mixed-effects model that included treatment as a fixed effect, with block as a random effect.
The idea is to divide an image into a number of blocks and treat each block as a random variable to be assigned a class label depending on its own features, its neighbours' features, and class labels.
The first two factors were taken as fixed factors and block as a random factor.
Like the previous model, the analysis includes Trial nested within Block as a random factor and two by-subject random slopes, for Trial and for Block.
The ecosystem functions in vegetated modules were initially compared among treatments using general linear models, with block as a random factor.
Additionally, the analysis includes Trial nested within Block as a random factor and two by-subject random slopes, for Trial and for Block.
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Analyses of competitive fertility also included block number as a random effect and an observation-level random effect because the binomial models were over-dispersed (Browne et al. 2005).
First, the pixel intensity value in each face block is treated as a random variable; thus, we can use the histogram of the intensities in each face block to approximate the probability density function (PDF) for computing the information entropy.
Infection status was modeled as a fixed effect, while block was modeled as a random effect.
Infection status was treated as a fixed factor whereas block was treated as a random factor.
In all cases, block was treated as a random factor and the other factors as fixed.
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