library("dbreg")
mod = dbreg(Temp ~ Wind | Month, data = airquality)
# coefficients
coef(mod)
coef(mod, fe = TRUE) # include fixed effects
(Intercept) Wind Month6 Month7 Month8 Month9
74.188474 -0.743388 12.543643 16.362079 16.316286 10.279216
# confidence intervals
confint(mod)
2.5 % 97.5 %
Wind -1.037433 -0.4493427
# variance-covariance matrix
vcov(mod)
(Intercept) Wind Month6 Month7 Month8
(Intercept) 4.2205624 -0.25730874 -1.57885933 -1.91972424 -1.95790554
Wind -0.2573087 0.02213869 0.03001816 0.05934598 0.06263108
Month6 -1.5788593 0.03001816 2.54164270 1.31043885 1.31489316
Month7 -1.9197242 0.05934598 1.31043885 2.61902713 1.39786250
Month8 -1.9579055 0.06263108 1.31489316 1.39786250 2.63712696
Month9 -1.6011594 0.03193685 1.27327443 1.31558217 1.32032119
Month9
(Intercept) -1.60115942
Wind 0.03193685
Month6 1.27327443
Month7 1.31558217
Month8 1.32032119
Month9 2.54701213
attr(,"type")
[1] "iid"
attr(,"rss")
[1] 5604.977
attr(,"tss")
[1] 13617.88
# predictions
head(predict(mod, newdata = airquality))
[1] 68.68740 68.24137 64.82179 65.63951 63.55803 63.11199
head(predict(mod, newdata = airquality, interval = "confidence"))
fit lwr upr
1 68.68740 66.16842 71.20639
2 68.24137 65.80451 70.67823
3 64.82179 62.61130 67.03227
4 65.63951 63.44749 67.83153
5 63.55803 61.22919 65.88686
6 63.11199 60.71775 65.50623