diff --git a/vignettes/vignette1.Rmd b/vignettes/vignette1.Rmd index cdbf153a..4871117d 100644 --- a/vignettes/vignette1.Rmd +++ b/vignettes/vignette1.Rmd @@ -126,15 +126,17 @@ List of available distributions with the mean, $E(y_{ij})$, and mean-variance, $ | | | | | $V(\mu_{ij}) = \mu_{ij}(1-p_j) (1+\mu_{ij}p_j)$| | | ZINB | VA/LA |log | $E(y_{ij}) = (1-p_j)\mu_{ij}$, $P(y_{ij}=0)=p_j$,| | | | | | $V(\mu_{ij}) = \mu_{ij}(1-p_j)(1+\mu_{ij}(p_j+\phi_j))$| -|Binary | Bernoulli | VA/LA |probit | $E(y_{ij}) = \mu_{ij}$, $V(\mu_{ij}) = \mu_{ij}(1-\mu_{ij})$| -| | | LA |logit | | +| | binomial | VA/LA |probit | $E(y_{ij}) = N_j\mu_{ij}$, $V(\mu_{ij}) = N_j\mu_{ij}(1-\mu_{ij})$| +| | binomial | LA |logit | +|Binary | Bernoulli | EVA/VA/LA |probit | $E(y_{ij}) = \mu_{ij}$, $V(\mu_{ij}) = \mu_{ij}(1-\mu_{ij})$| +| | | EVA/LA |logit | | |Biomass | Tweedie | LA/EVA |log | $E(y_{ij}) = \mu_{ij}$, $V(\mu_{ij}) = \phi_j\mu_{ij}^\nu$,| | | | | | where $1<\nu<2$ is a power parameter and $\phi_j>0$ is a dispersion parameter | |Ordinal | Multinomial | VA |probit | Cumulative probit model| |Normal | Gaussian | VA/LA |identity| $E(y_{ij}) = \mu_{ij}$, $V(y_{ij}) = \phi_j^2$| |Positive continuous| Gamma | VA/LA |log| $E(y_{ij}) = \mu_{ij}$, $V(y_{ij}) = \mu_{ij}^2/\phi_j$| | | | | | where $\phi_j$ is a shape parameter | -|Non-negative continuous| Exponential | VA/LA |log| $E(y_{ij}) = \mu_{ij}$, $V(y_{ij}) = \mu_{ij}^2$| +|Positive continuous | Exponential | VA/LA |log| $E(y_{ij}) = \mu_{ij}$, $V(y_{ij}) = \mu_{ij}^2$| |Percent cover| beta | LA/EVA | probit/logit | $E(y_{ij}) = \mu_{ij}$, $V(\mu_{ij}) = \mu_{ij}(1-\mu_{ij})/(1+\phi_j)$ | |Percent cover with zeros/ones| ordered beta | EVA | probit | details in Korhonen et al (2024) |