Hello Talk Stats Members,
I am having a little trouble with interpreting the output from my model can anyone give a helping hand.
Ok so it is a Generalised Linear Model in RStudio. My outcome variable is either 0 or 1.
Here is the output summary from my final model:
Call:
glm(formula = outcome ~ Location + Size + Proximity + Location:Size +
Location:Size
roximity, family = binomial(link = probit),
data = shark2, control = glm.control(50))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5045 -0.6426 -0.4042 0.7594 1.8072
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 21.166 12.298 1.721 0.0852 .
Location2 -12.222 8.318 -1.469 0.1417
Size -34.364 18.093 -1.899 0.0575 .
Proximity -37.200 16.259 -2.288 0.0221 *
Location2:Size 21.437 12.333 1.738 0.0822 .
Location1:Size
roximity 56.707 23.854 2.377 0.0174 *
Location2:Size
roximity 55.816 24.854 2.246 0.0247 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 44.252 on 32 degrees of freedom
Residual deviance: 25.948 on 26 degrees of freedom
AIC: 39.948
Number of Fisher Scoring iterations: 1
and here is a Model summary:
Call: glm(formula = outcome ~ Location + Size + Proximity + Location:Size +
Location:Size
roximity, family = binomial(link = probit),
data = shark2, control = glm.control(50))
Coefficients:
(Intercept) Location2
21.17 -12.22
Size Proximity
-34.36 -37.20
Location2:Size Location1:Size
roximity
21.44 56.71
Location2:Size
roximity
55.82
Degrees of Freedom: 32 Total (i.e. Null); 26 Residual
Null Deviance: 44.25
Residual Deviance: 25.95 AIC: 39.95
Can anyone help me get my head around what I need to understand to make my write up? I can see that some effects and interactions are significant and they make sense but I am not sure what to do next with the output.
thank you for any guidance.
Darren.
I am having a little trouble with interpreting the output from my model can anyone give a helping hand.
Ok so it is a Generalised Linear Model in RStudio. My outcome variable is either 0 or 1.
Here is the output summary from my final model:
Call:
glm(formula = outcome ~ Location + Size + Proximity + Location:Size +
Location:Size
data = shark2, control = glm.control(50))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5045 -0.6426 -0.4042 0.7594 1.8072
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 21.166 12.298 1.721 0.0852 .
Location2 -12.222 8.318 -1.469 0.1417
Size -34.364 18.093 -1.899 0.0575 .
Proximity -37.200 16.259 -2.288 0.0221 *
Location2:Size 21.437 12.333 1.738 0.0822 .
Location1:Size
Location2:Size
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 44.252 on 32 degrees of freedom
Residual deviance: 25.948 on 26 degrees of freedom
AIC: 39.948
Number of Fisher Scoring iterations: 1
and here is a Model summary:
Call: glm(formula = outcome ~ Location + Size + Proximity + Location:Size +
Location:Size
data = shark2, control = glm.control(50))
Coefficients:
(Intercept) Location2
21.17 -12.22
Size Proximity
-34.36 -37.20
Location2:Size Location1:Size
21.44 56.71
Location2:Size
55.82
Degrees of Freedom: 32 Total (i.e. Null); 26 Residual
Null Deviance: 44.25
Residual Deviance: 25.95 AIC: 39.95
Can anyone help me get my head around what I need to understand to make my write up? I can see that some effects and interactions are significant and they make sense but I am not sure what to do next with the output.
thank you for any guidance.
Darren.