Means, Covariances and Correlations among Blood Lead Levels
> library(foreign)
> ds <- read.dta("tlc.dta")
> str(ds)
'data.frame': 100 obs. of 6 variables:
$ id : num 1 2 3 4 5 6 7 8 9 10 ...
$ trt: Factor w/ 2 levels "Placebo","Succimer": 1 2 2 1 2 2 1 1 1 1 ...
$ y0 : num 30.8 26.5 25.8 24.7 20.4 ...
$ y1 : num 26.9 14.8 23 24.5 2.8 ...
$ y4 : num 25.8 19.5 19.1 22 3.2 ...
$ y6 : num 23.8 21 23.2 22.5 9.4 ...
<output deleted>
> plac <- subset(ds, ds$trt=="Placebo")
> succ <- subset(ds, ds$trt=="Succimer")
> summary(plac[, 3:6])
y0 y1 y4 y6
Min. :19.70 Min. :14.90 Min. :15.30 Min. :13.50
1st Qu.:21.87 1st Qu.:20.92 1st Qu.:19.83 1st Qu.:19.95
Median :25.25 Median :24.10 Median :22.45 Median :22.35
Mean :26.27 Mean :24.66 Mean :24.07 Mean :23.65
3rd Qu.:29.72 3rd Qu.:27.82 3rd Qu.:27.45 3rd Qu.:27.50
Max. :38.10 Max. :40.80 Max. :38.60 Max. :43.30
> cor(plac[, 3:6])
y0 y1 y4 y6
y0 1.0000000 0.8291363 0.8393547 0.7558796
y1 0.8291363 1.0000000 0.8606685 0.7592246
y4 0.8393547 0.8606685 1.0000000 0.8697065
y6 0.7558796 0.7592246 0.8697065 1.0000000
> cov(plac[, 3:6])
y0 y1 y4 y6
y0 25.24165 22.74947 24.26098 21.41784
y1 22.74947 29.82449 27.04122 23.38412
y4 24.26098 27.04122 33.09847 28.21896
y6 21.41784 23.38412 28.21896 31.80743
> summary(succ[, 3:6])
y0 y1 y4 y6
Min. :19.70 Min. : 2.800 Min. : 3.000 Min. : 4.10
1st Qu.:22.13 1st Qu.: 7.225 1st Qu.: 9.125 1st Qu.:15.40
Median :26.20 Median :12.250 Median :15.350 Median :18.85
Mean :26.54 Mean :13.522 Mean :15.514 Mean :20.76
3rd Qu.:29.55 3rd Qu.:17.500 3rd Qu.:19.725 3rd Qu.:23.75
Max. :41.10 Max. :39.000 Max. :40.400 Max. :63.90
> cor(succ[, 3:6])
y0 y1 y4 y6
y0 1.0000000 0.4014589 0.3839654 0.4951063
y1 0.4014589 1.0000000 0.7308221 0.5069743
y4 0.3839654 0.7308221 1.0000000 0.4548224
y6 0.4951063 0.5069743 0.4548224 1.0000000
> cov(succ[, 3:6])
y0 y1 y4 y6
y0 25.20979 15.46543 15.13800 22.98543
y1 15.46543 58.86706 44.02907 35.96595
y4 15.13800 44.02907 61.65715 33.02197
y6 22.98543 35.96595 33.02197 85.49465