I'm extremely stuck at the moment as I am trying to figure out how to calculate the probability from my glm output on R. I know the data is very insignificant but I would really love to be shown how to get the probability from an output like this. I was thinking of trying inv.logit but didnt know what variables to put within the brackets.

(This data is showing the effect of non Red squirrel species at a site on the detection of red squirrels)

Thanks so much in advance

glm(RS_sticky~RS_totalmins,data=data, family = binomial (link='logit'))

Call: glm(formula = RS_sticky ~ RS_totalmins, family = binomial(link = "logit"),

data = data)

Coefficients:

(Intercept) RS_totalmins

-3.45891 0.02841

Degrees of Freedom: 33 Total (i.e. Null); 32 Residual

(1 observation deleted due to missingness)

Null Deviance: 34.57

Residual Deviance: 15.43 AIC: 19.43

> RStime<-glm(RS_sticky~RS_totalmins,data=data, family = binomial (link='logit'))

> summary(RStime)

Call:glm(formula = RS_sticky ~ RS_totalmins, family = binomial(link = "logit"), data = data)

Deviance Residuals:

Min 1Q Median 3Q Max

-2.7498 -0.2524 -0.2489 -0.2489 1.2846

Coefficients:Estimate Std. Error z value Pr(>|z|)

(Intercept) -3.458913 1.098717 -3.148 0.00164 **

RS_totalmins 0.028411 0.009888 2.873 0.00406 **

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 34.575 on 33 degrees of freedom

Residual deviance: 15.433 on 32 degrees of freedom

(1 observation deleted due to missingness)

AIC: 19.433

Number of Fisher Scoring iterations: 6

(This data is showing the effect of non Red squirrel species at a site on the detection of red squirrels)

Thanks so much in advance

glm(RS_sticky~RS_totalmins,data=data, family = binomial (link='logit'))

Call: glm(formula = RS_sticky ~ RS_totalmins, family = binomial(link = "logit"),

data = data)

Coefficients:

(Intercept) RS_totalmins

-3.45891 0.02841

Degrees of Freedom: 33 Total (i.e. Null); 32 Residual

(1 observation deleted due to missingness)

Null Deviance: 34.57

Residual Deviance: 15.43 AIC: 19.43

> RStime<-glm(RS_sticky~RS_totalmins,data=data, family = binomial (link='logit'))

> summary(RStime)

Call:glm(formula = RS_sticky ~ RS_totalmins, family = binomial(link = "logit"), data = data)

Deviance Residuals:

Min 1Q Median 3Q Max

-2.7498 -0.2524 -0.2489 -0.2489 1.2846

Coefficients:Estimate Std. Error z value Pr(>|z|)

(Intercept) -3.458913 1.098717 -3.148 0.00164 **

RS_totalmins 0.028411 0.009888 2.873 0.00406 **

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 34.575 on 33 degrees of freedom

Residual deviance: 15.433 on 32 degrees of freedom

(1 observation deleted due to missingness)

AIC: 19.433

Number of Fisher Scoring iterations: 6

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