BaseWPDist {HTSeedGLM} | R Documentation |
Distribution of base seed water potential
Description
This function provides the estimates of stress and uniformity parameters along with respective variances using generalised linear model fitted to observed germination percentage of seed. The model can be fitted under logit, probit and cloglog transformations.
Usage
BaseWPDist(model)
Arguments
model |
Fitted model |
Value
Degrees of freedom
p_value: For testing significance of water potential
stress: Location parameter of the base seed water potential
uniformity: Scale parameter of the base seed water potential
var_stress: Variance of estimator of the location parameter
var_uniformity: Variance of estimator of the scale parameter
References
Bradford, K. J. (2002). Applications of Hydrothermal Time to Quantifying and Modeling Seed Germination and Dormancy. Weed Science, 50(2), 248–260. http://www.jstor.org/stable/4046371
Kebreab, E., & Murdoch, A. J. (1999). Modelling the effects of water stress and temperature on germination rate of Orobanche aegyptiaca seeds. Journal of Experimental Botany, 50(334), 655-664. doi:10.1093/jxb/50.334.655
Dobson, A. J., & Barnett, A. G. (2018). An introduction to generalized linear models. Chapman and Hall/CRC.
Examples
X <- c(0,-0.3,-0.6,-0.9) # Various water potentials
y <- c(44,10,10,4) # Number of germinated seeds
n <- c(100,100,100,100) # Total number of viable seeds
n_y <- n-y
sg.mat <- cbind(y,n_y)
res.glm1 <- glm(sg.mat~ X,family=binomial(link=logit)) # Using logit transformation
my.bdl<- BaseWPDist(res.glm1)
res.glm2 <- glm(sg.mat~ X,family=binomial(link=probit)) # Using probit transformation
my.bdp<- BaseWPDist(res.glm2)
res.glm3 <- glm(sg.mat~ X,family=binomial(link=cloglog))# Using cloglog transformation
my.bdcl<- BaseWPDist(res.glm3)