This report was automatically generated with the R package knitr (version 1.5).
library(faraway)
data(fat, package = "faraway")
lmod <- lm(brozek ~ age + weight + height + neck + chest + abdom + hip + thigh +
knee + ankle + biceps + forearm + wrist, data = fat)
x <- model.matrix(lmod)
(x0 <- apply(x, 2, median))
(Intercept) age weight height neck chest
1.00 43.00 176.50 70.00 38.00 99.65
abdom hip thigh knee ankle biceps
90.95 99.30 59.00 38.50 22.80 32.05
forearm wrist
28.70 18.30
(y0 <- sum(x0 * coef(lmod)))
[1] 17.49
predict(lmod, new = data.frame(t(x0)))
1
17.49
predict(lmod, new = data.frame(t(x0)), interval = "prediction")
fit lwr upr
1 17.49 9.618 25.37
predict(lmod, new = data.frame(t(x0)), interval = "confidence")
fit lwr upr
1 17.49 16.94 18.04
(x1 <- apply(x, 2, function(x) quantile(x, 0.95)))
(Intercept) age weight height neck chest
1.00 67.00 225.65 74.50 41.84 116.34
abdom hip thigh knee ankle biceps
110.76 112.12 68.54 42.65 25.45 37.20
forearm wrist
31.74 19.80
predict(lmod, new = data.frame(t(x1)), interval = "prediction")
fit lwr upr
1 30.02 21.92 38.11
predict(lmod, new = data.frame(t(x1)), interval = "confidence")
fit lwr upr
1 30.02 28.07 31.97
data(airpass, package = "faraway")
plot(pass ~ year, airpass, type = "l", ylab = "Log(Passengers)")
lmod <- lm(log(pass) ~ year, airpass)
lines(exp(predict(lmod)) ~ year, airpass)
lagdf <- embed(log(airpass$pass), 14)
colnames(lagdf) <- c("y", paste0("lag", 1:13))
lagdf <- data.frame(lagdf)
armod <- lm(y ~ lag1 + lag12 + lag13, data.frame(lagdf))
sumary(armod)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1385 0.0536 2.58 0.011
lag1 0.6923 0.0619 11.19 < 2e-16
lag12 0.9215 0.0347 26.53 < 2e-16
lag13 -0.6321 0.0677 -9.34 4.2e-16
n = 131, p = 4, Residual SE = 0.04, R-Squared = 0.99
plot(pass ~ year, airpass, type = "l")
lines(airpass$year[14:144], exp(predict(armod)), lty = 2)
lagdf[nrow(lagdf), ]
y lag1 lag2 lag3 lag4 lag5 lag6 lag7 lag8 lag9 lag10 lag11
131 6.068 5.966 6.133 6.23 6.407 6.433 6.282 6.157 6.133 6.038 5.969 6.033
lag12 lag13
131 6.004 5.892
predict(armod, data.frame(lag1 = 6.0684, lag12 = 6.0331, lag13 = 6.0039), interval = "prediction")
fit lwr upr
1 6.104 6.021 6.187
The R session information (including the OS info, R version and all packages used):
sessionInfo()
R version 3.1.0 (2014-04-10)
Platform: x86_64-apple-darwin13.1.0 (64-bit)
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] graphics grDevices utils datasets methods stats base
other attached packages:
[1] faraway_1.0.6 knitr_1.5 ggplot2_0.9.3.1
loaded via a namespace (and not attached):
[1] colorspace_1.2-4 dichromat_2.0-0 digest_0.6.4
[4] evaluate_0.5.3 formatR_0.10 grid_3.1.0
[7] gtable_0.1.2 labeling_0.2 MASS_7.3-31
[10] munsell_0.4.2 plyr_1.8.1 proto_0.3-10
[13] RColorBrewer_1.0-5 Rcpp_0.11.1 reshape2_1.2.2
[16] scales_0.2.3 stringr_0.6.2 tools_3.1.0
Sys.time()
[1] "2014-06-16 14:01:24 BST"