Uogólnione modele liniowe w R
Richard Erickson
Instructor
glm()print() zazwyczaj domyślnaprint(poisson_out)
Call: glm(formula = y ~ x, family = "poisson", data = dat)
Coefficients:
(Intercept) x
-1.43036 0.05815
Degrees of Freedom: 29 Total (i.e. Null); 28 Residual
Null Deviance: 35.63
Residual Deviance: 30.92 AIC: 66.02
summary() dostarcza więcej szczegółówsummary(poisson_out)
#... Deviance Residuals: Min 1Q Median 3Q Max -1.6547 -0.9666 -0.7226 0.3830 2.3022Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.43036 0.59004 -2.424 0.0153 * x 0.05815 0.02779 2.093 0.0364 *Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(Dispersion parameter for poisson family taken to be 1)Null deviance: 35.627 on 29 degrees of freedom Residual deviance: 30.918 on 28 degrees of freedom AIC: 66.024Number of Fisher Scoring iterations: 5
tidy() z pakietu Broomlibrary(broom)
tidy(poisson_out)
term estimate std.error statistic p.value
1 (Intercept) -1.43035579 0.59003923 -2.424171 0.01534339
2 x 0.05814858 0.02778801 2.092578 0.03638686
coef() wyświetla współczynniki regresjicoef(poisson_out)
(Intercept) x
-1.43035579 0.05814858
confint() szacuje przedziały ufnościconfint(poisson_out)
Waiting for profiling to be done...
2.5 % 97.5 %
(Intercept) -2.725545344 -0.3897748
x 0.005500767 0.1155564
predict(model, new_data)new_data:predict() zwraca predykcje na podstawie oryginalnych danych.predict() zwraca predykcje dla new_data.
Uogólnione modele liniowe w R