Hi there,
I'm wondering if anyone knows what Pr(>|z|) and the z value means in the summary of a binomial glm of a model represents. Here is the print out and code: Setp is the response variable of settlement of larvae, PLD is a factor of time and habitat is where the larvae settled.
BL1 <- glm(Setp ~ PLD * Habitat, family = binomial,
data = BL)
summary(BL1)
Call:
glm(formula = Setp ~ PLD * Habitat, family = binomial, data = BL)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.50751 -0.34671 -0.07356 0.16767 0.65171
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.4578 0.5132 -0.892 0.3723
PLD60 -1.6939 0.9660 -1.754 0.0795 .
PLD240 -1.7866 0.9923 -1.800 0.0718 .
HabitatDark Smooth -0.3895 0.7490 -0.520 0.6031
HabitatLight Rough -1.9401 1.0400 -1.866 0.0621 .
HabitatLight Smooth -2.4022 1.2178 -1.973 0.0486 *
PLD60:HabitatDark Smooth 0.5567 1.3487 0.413 0.6798
PLD240:HabitatDark Smooth -0.3106 1.6118 -0.193 0.8472
PLD60:HabitatLight Rough 1.7473 1.5919 1.098 0.2724
PLD240:HabitatLight Rough 1.4030 1.7148 0.818 0.4133
PLD60:HabitatLight Smooth 2.3095 1.6953 1.362 0.1731
PLD240:HabitatLight Smooth 2.0075 1.7913 1.121 0.2624
Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 33.011 on 191 degrees of freedom
Residual deviance: 18.402 on 180 degrees of freedom
AIC: 78.458
Number of Fisher Scoring iterations: 5
I'm trying to determine if there are significant differences because the boxplots of the raw data showed there was. Does the Pr(>|z|) need to be <0.05 to be significant?
Thanks in advance