This report was automatically generated with the R package knitr (version 1.5).
library(faraway)
data(sexab, package = "faraway")
sexab
cpa ptsd csa
1 2.0479 9.7136 Abused
2 0.8389 6.1693 Abused
3 -0.2414 15.1593 Abused
4 -1.1146 11.3128 Abused
5 2.0147 9.9538 Abused
6 6.7113 9.8388 Abused
7 1.2081 5.9849 Abused
8 2.3428 11.1105 Abused
9 0.9119 6.2553 Abused
10 -0.8531 7.0411 Abused
11 7.3567 13.8885 Abused
12 2.0936 14.9834 Abused
13 1.9457 10.8248 Abused
14 -0.4222 13.9145 Abused
15 1.4146 18.1686 Abused
16 6.0776 12.9163 Abused
17 6.0170 15.0832 Abused
18 3.7334 8.3089 Abused
19 2.6275 9.2801 Abused
20 0.4626 9.2912 Abused
21 7.0184 11.9274 Abused
22 3.1466 13.5168 Abused
23 2.3464 8.5351 Abused
24 8.6469 18.9925 Abused
25 4.3169 17.2038 Abused
26 2.1305 10.3740 Abused
27 4.0521 8.4799 Abused
28 1.5741 14.0794 Abused
29 3.7637 13.4460 Abused
30 5.1835 9.3743 Abused
31 1.1756 16.1314 Abused
32 2.7040 12.0746 Abused
33 1.1442 10.8378 Abused
34 4.1316 17.4098 Abused
35 1.4230 14.9013 Abused
36 6.3523 12.7361 Abused
37 7.6147 15.4218 Abused
38 1.9970 17.6411 Abused
39 3.2510 11.8932 Abused
40 3.0090 7.3264 Abused
41 3.0775 10.7658 Abused
42 5.2679 15.2489 Abused
43 3.4114 11.0824 Abused
44 1.3532 7.6223 Abused
45 5.1192 11.1280 Abused
46 1.4918 6.1420 NotAbused
47 0.6096 0.7446 NotAbused
48 1.4333 3.4596 NotAbused
49 -0.3366 6.9122 NotAbused
50 -3.1204 4.5417 NotAbused
51 2.6534 6.3393 NotAbused
52 3.7544 7.2969 NotAbused
53 1.5115 -3.3492 NotAbused
54 1.7539 4.4676 NotAbused
55 -0.4586 4.0182 NotAbused
56 0.7026 6.2162 NotAbused
57 5.0497 5.7945 NotAbused
58 0.7319 8.5371 NotAbused
59 -0.4164 -0.3755 NotAbused
60 2.8093 4.7527 NotAbused
61 2.9337 6.3937 NotAbused
62 -0.2278 6.8331 NotAbused
63 4.8204 10.4699 NotAbused
64 1.3216 1.1115 NotAbused
65 0.5622 7.4012 NotAbused
66 1.2230 6.1747 NotAbused
67 3.0495 5.4923 NotAbused
68 3.7686 10.9145 NotAbused
69 -2.1188 4.8386 NotAbused
70 3.6357 3.6294 NotAbused
71 -0.3140 -0.1434 NotAbused
72 2.1763 6.5728 NotAbused
73 -0.2321 -3.1162 NotAbused
74 -1.8575 -0.4700 NotAbused
75 2.8525 6.8430 NotAbused
76 0.8114 7.1292 NotAbused
by(sexab, sexab$csa, summary)
sexab$csa: Abused
cpa ptsd csa
Min. :-1.11 Min. : 5.99 Abused :45
1st Qu.: 1.42 1st Qu.: 9.37 NotAbused: 0
Median : 2.63 Median :11.31
Mean : 3.08 Mean :11.94
3rd Qu.: 4.32 3rd Qu.:14.90
Max. : 8.65 Max. :18.99
--------------------------------------------------------
sexab$csa: NotAbused
cpa ptsd csa
Min. :-3.12 Min. :-3.35 Abused : 0
1st Qu.:-0.23 1st Qu.: 3.54 NotAbused:31
Median : 1.32 Median : 5.79
Mean : 1.31 Mean : 4.70
3rd Qu.: 2.83 3rd Qu.: 6.84
Max. : 5.05 Max. :10.91
plot(ptsd ~ csa, sexab)
plot(ptsd ~ cpa, pch = as.character(csa), sexab)
t.test(ptsd ~ csa, sexab, var.equal = TRUE)
Two Sample t-test
data: ptsd by csa
t = 8.939, df = 74, p-value = 2.172e-13
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
5.63 8.86
sample estimates:
mean in group Abused mean in group NotAbused
11.941 4.696
d1 <- ifelse(sexab$csa == "Abused", 1, 0)
d2 <- ifelse(sexab$csa == "NotAbused", 1, 0)
lmod <- lm(ptsd ~ d1 + d2, sexab)
sumary(lmod)
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.696 0.624 7.53 1.0e-10
d1 7.245 0.811 8.94 2.2e-13
n = 76, p = 2, Residual SE = 3.47, R-Squared = 0.52
model.matrix(lmod)
(Intercept) d1 d2
1 1 1 0
2 1 1 0
3 1 1 0
4 1 1 0
5 1 1 0
6 1 1 0
7 1 1 0
8 1 1 0
9 1 1 0
10 1 1 0
11 1 1 0
12 1 1 0
13 1 1 0
14 1 1 0
15 1 1 0
16 1 1 0
17 1 1 0
18 1 1 0
19 1 1 0
20 1 1 0
21 1 1 0
22 1 1 0
23 1 1 0
24 1 1 0
25 1 1 0
26 1 1 0
27 1 1 0
28 1 1 0
29 1 1 0
30 1 1 0
31 1 1 0
32 1 1 0
33 1 1 0
34 1 1 0
35 1 1 0
36 1 1 0
37 1 1 0
38 1 1 0
39 1 1 0
40 1 1 0
41 1 1 0
42 1 1 0
43 1 1 0
44 1 1 0
45 1 1 0
46 1 0 1
47 1 0 1
48 1 0 1
49 1 0 1
50 1 0 1
51 1 0 1
52 1 0 1
53 1 0 1
54 1 0 1
55 1 0 1
56 1 0 1
57 1 0 1
58 1 0 1
59 1 0 1
60 1 0 1
61 1 0 1
62 1 0 1
63 1 0 1
64 1 0 1
65 1 0 1
66 1 0 1
67 1 0 1
68 1 0 1
69 1 0 1
70 1 0 1
71 1 0 1
72 1 0 1
73 1 0 1
74 1 0 1
75 1 0 1
76 1 0 1
attr(,"assign")
[1] 0 1 2
lmod <- lm(ptsd ~ d2, sexab)
sumary(lmod)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.941 0.518 23.07 < 2e-16
d2 -7.245 0.811 -8.94 2.2e-13
n = 76, p = 2, Residual SE = 3.47, R-Squared = 0.52
lmod <- lm(ptsd ~ d1 + d2 - 1, sexab)
sumary(lmod)
Estimate Std. Error t value Pr(>|t|)
d1 11.941 0.518 23.07 <2e-16
d2 4.696 0.624 7.53 1e-10
n = 76, p = 2, Residual SE = 3.47, R-Squared = 0.89
lmod <- lm(ptsd ~ csa, sexab)
sumary(lmod)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.941 0.518 23.07 < 2e-16
csaNotAbused -7.245 0.811 -8.94 2.2e-13
n = 76, p = 2, Residual SE = 3.47, R-Squared = 0.52
class(sexab$csa)
[1] "factor"
sexab$csa <- relevel(sexab$csa, ref = "NotAbused")
lmod <- lm(ptsd ~ csa, sexab)
sumary(lmod)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.696 0.624 7.53 1.0e-10
csaAbused 7.245 0.811 8.94 2.2e-13
n = 76, p = 2, Residual SE = 3.47, R-Squared = 0.52
lmod4 <- lm(ptsd ~ cpa + csa + cpa:csa, sexab)
sumary(lmod4)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.696 0.711 5.20 1.8e-06
cpa 0.764 0.304 2.51 0.014
csaAbused 6.861 1.075 6.38 1.5e-08
cpa:csaAbused -0.314 0.368 -0.85 0.397
n = 76, p = 4, Residual SE = 3.28, R-Squared = 0.58
model.matrix(lmod4)
(Intercept) cpa csaAbused cpa:csaAbused
1 1 2.0479 1 2.0479
2 1 0.8389 1 0.8389
3 1 -0.2414 1 -0.2414
4 1 -1.1146 1 -1.1146
5 1 2.0147 1 2.0147
6 1 6.7113 1 6.7113
7 1 1.2081 1 1.2081
8 1 2.3428 1 2.3428
9 1 0.9119 1 0.9119
10 1 -0.8531 1 -0.8531
11 1 7.3567 1 7.3567
12 1 2.0936 1 2.0936
13 1 1.9457 1 1.9457
14 1 -0.4222 1 -0.4222
15 1 1.4146 1 1.4146
16 1 6.0776 1 6.0776
17 1 6.0170 1 6.0170
18 1 3.7334 1 3.7334
19 1 2.6275 1 2.6275
20 1 0.4626 1 0.4626
21 1 7.0184 1 7.0184
22 1 3.1466 1 3.1466
23 1 2.3464 1 2.3464
24 1 8.6469 1 8.6469
25 1 4.3169 1 4.3169
26 1 2.1305 1 2.1305
27 1 4.0521 1 4.0521
28 1 1.5741 1 1.5741
29 1 3.7637 1 3.7637
30 1 5.1835 1 5.1835
31 1 1.1756 1 1.1756
32 1 2.7040 1 2.7040
33 1 1.1442 1 1.1442
34 1 4.1316 1 4.1316
35 1 1.4230 1 1.4230
36 1 6.3523 1 6.3523
37 1 7.6147 1 7.6147
38 1 1.9970 1 1.9970
39 1 3.2510 1 3.2510
40 1 3.0090 1 3.0090
41 1 3.0775 1 3.0775
42 1 5.2679 1 5.2679
43 1 3.4114 1 3.4114
44 1 1.3532 1 1.3532
45 1 5.1192 1 5.1192
46 1 1.4918 0 0.0000
47 1 0.6096 0 0.0000
48 1 1.4333 0 0.0000
49 1 -0.3366 0 0.0000
50 1 -3.1204 0 0.0000
51 1 2.6534 0 0.0000
52 1 3.7544 0 0.0000
53 1 1.5115 0 0.0000
54 1 1.7539 0 0.0000
55 1 -0.4586 0 0.0000
56 1 0.7026 0 0.0000
57 1 5.0497 0 0.0000
58 1 0.7319 0 0.0000
59 1 -0.4164 0 0.0000
60 1 2.8093 0 0.0000
61 1 2.9337 0 0.0000
62 1 -0.2278 0 0.0000
63 1 4.8204 0 0.0000
64 1 1.3216 0 0.0000
65 1 0.5622 0 0.0000
66 1 1.2230 0 0.0000
67 1 3.0495 0 0.0000
68 1 3.7686 0 0.0000
69 1 -2.1188 0 0.0000
70 1 3.6357 0 0.0000
71 1 -0.3140 0 0.0000
72 1 2.1763 0 0.0000
73 1 -0.2321 0 0.0000
74 1 -1.8575 0 0.0000
75 1 2.8525 0 0.0000
76 1 0.8114 0 0.0000
attr(,"assign")
[1] 0 1 2 3
attr(,"contrasts")
attr(,"contrasts")$csa
[1] "contr.treatment"
plot(ptsd ~ cpa, sexab, pch = as.numeric(csa))
abline(3.96, 0.764)
abline(3.96 + 6.86, 0.764 - 0.314, lty = 2)
lmod3 <- lm(ptsd ~ cpa + csa, sexab)
sumary(lmod3)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.975 0.629 6.32 1.9e-08
cpa 0.551 0.172 3.21 0.002
csaAbused 6.273 0.822 7.63 6.9e-11
n = 76, p = 3, Residual SE = 3.27, R-Squared = 0.58
plot(ptsd ~ cpa, sexab, pch = as.numeric(csa))
abline(3.98, 0.551)
abline(3.98 + 6.27, 0.551, lty = 2)
confint(lmod3)[3, ]
2.5 % 97.5 %
4.635 7.911
plot(fitted(lmod3), residuals(lmod3), pch = as.numeric(sexab$csa), xlab = "Fitted",
ylab = "Residuals")
lmod1 <- lm(ptsd ~ cpa, sexab)
sumary(lmod1)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.552 0.707 9.26 5.3e-14
cpa 1.033 0.212 4.87 6.3e-06
n = 76, p = 2, Residual SE = 4.36, R-Squared = 0.24
data(whiteside, package = "MASS")
require(ggplot2)
ggplot(aes(x = Temp, y = Gas), data = whiteside) + geom_point() + facet_grid(~Insul) +
geom_smooth(method = "lm")
lmod <- lm(Gas ~ Temp * Insul, whiteside)
sumary(lmod)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.8538 0.1360 50.41 < 2e-16
Temp -0.3932 0.0225 -17.49 < 2e-16
InsulAfter -2.1300 0.1801 -11.83 2.3e-16
Temp:InsulAfter 0.1153 0.0321 3.59 0.00073
n = 56, p = 4, Residual SE = 0.32, R-Squared = 0.93
mean(whiteside$Temp)
[1] 4.875
whiteside$ctemp <- whiteside$Temp - mean(whiteside$Temp)
lmodc <- lm(Gas ~ ctemp * Insul, whiteside)
sumary(lmodc)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.9368 0.0642 76.85 < 2e-16
ctemp -0.3932 0.0225 -17.49 < 2e-16
InsulAfter -1.5679 0.0877 -17.87 < 2e-16
ctemp:InsulAfter 0.1153 0.0321 3.59 0.00073
n = 56, p = 4, Residual SE = 0.32, R-Squared = 0.93
data(fruitfly, package = "faraway")
plot(longevity ~ thorax, fruitfly, pch = unclass(activity))
legend(0.63, 100, levels(fruitfly$activity), pch = 1:5)
require(ggplot2)
ggplot(aes(x = thorax, y = longevity), data = fruitfly) + geom_point() + facet_wrap(~activity)
lmod <- lm(longevity ~ thorax * activity, fruitfly)
sumary(lmod)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -50.242 21.801 -2.30 0.023
thorax 136.127 25.952 5.25 7.3e-07
activityone 6.517 33.871 0.19 0.848
activitylow -7.750 33.969 -0.23 0.820
activitymany -1.139 32.530 -0.04 0.972
activityhigh -11.038 31.287 -0.35 0.725
thorax:activityone -4.677 40.652 -0.12 0.909
thorax:activitylow 0.874 40.425 0.02 0.983
thorax:activitymany 6.548 39.360 0.17 0.868
thorax:activityhigh -11.127 38.120 -0.29 0.771
n = 124, p = 10, Residual SE = 10.71, R-Squared = 0.65
model.matrix(lmod)
(Intercept) thorax activityone activitylow activitymany activityhigh
1 1 0.68 0 0 1 0
2 1 0.68 0 0 1 0
3 1 0.72 0 0 1 0
4 1 0.72 0 0 1 0
5 1 0.76 0 0 1 0
6 1 0.76 0 0 1 0
7 1 0.76 0 0 1 0
8 1 0.76 0 0 1 0
9 1 0.76 0 0 1 0
10 1 0.80 0 0 1 0
11 1 0.80 0 0 1 0
12 1 0.80 0 0 1 0
13 1 0.84 0 0 1 0
14 1 0.84 0 0 1 0
15 1 0.84 0 0 1 0
16 1 0.84 0 0 1 0
17 1 0.84 0 0 1 0
18 1 0.84 0 0 1 0
19 1 0.88 0 0 1 0
20 1 0.88 0 0 1 0
21 1 0.92 0 0 1 0
22 1 0.92 0 0 1 0
23 1 0.92 0 0 1 0
24 1 0.94 0 0 1 0
25 1 0.64 0 0 0 0
26 1 0.70 0 0 0 0
27 1 0.72 0 0 0 0
28 1 0.72 0 0 0 0
29 1 0.72 0 0 0 0
30 1 0.76 0 0 0 0
31 1 0.78 0 0 0 0
32 1 0.80 0 0 0 0
33 1 0.84 0 0 0 0
34 1 0.84 0 0 0 0
35 1 0.84 0 0 0 0
36 1 0.84 0 0 0 0
37 1 0.84 0 0 0 0
38 1 0.88 0 0 0 0
39 1 0.88 0 0 0 0
40 1 0.88 0 0 0 0
41 1 0.88 0 0 0 0
42 1 0.88 0 0 0 0
43 1 0.92 0 0 0 0
44 1 0.92 0 0 0 0
45 1 0.92 0 0 0 0
46 1 0.92 0 0 0 0
47 1 0.92 0 0 0 0
48 1 0.92 0 0 0 0
49 1 0.94 0 0 0 0
50 1 0.64 1 0 0 0
51 1 0.68 1 0 0 0
52 1 0.72 1 0 0 0
53 1 0.76 1 0 0 0
54 1 0.76 1 0 0 0
55 1 0.80 1 0 0 0
56 1 0.80 1 0 0 0
57 1 0.80 1 0 0 0
58 1 0.82 1 0 0 0
59 1 0.82 1 0 0 0
60 1 0.84 1 0 0 0
61 1 0.84 1 0 0 0
62 1 0.84 1 0 0 0
63 1 0.84 1 0 0 0
64 1 0.84 1 0 0 0
65 1 0.84 1 0 0 0
66 1 0.88 1 0 0 0
67 1 0.88 1 0 0 0
68 1 0.88 1 0 0 0
69 1 0.88 1 0 0 0
70 1 0.88 1 0 0 0
71 1 0.88 1 0 0 0
72 1 0.88 1 0 0 0
73 1 0.92 1 0 0 0
74 1 0.92 1 0 0 0
75 1 0.68 0 1 0 0
76 1 0.68 0 1 0 0
77 1 0.72 0 1 0 0
78 1 0.76 0 1 0 0
79 1 0.78 0 1 0 0
80 1 0.80 0 1 0 0
81 1 0.80 0 1 0 0
82 1 0.80 0 1 0 0
83 1 0.84 0 1 0 0
84 1 0.84 0 1 0 0
85 1 0.84 0 1 0 0
86 1 0.84 0 1 0 0
87 1 0.84 0 1 0 0
88 1 0.84 0 1 0 0
89 1 0.88 0 1 0 0
90 1 0.88 0 1 0 0
91 1 0.88 0 1 0 0
92 1 0.90 0 1 0 0
93 1 0.90 0 1 0 0
94 1 0.90 0 1 0 0
95 1 0.90 0 1 0 0
96 1 0.90 0 1 0 0
97 1 0.90 0 1 0 0
98 1 0.92 0 1 0 0
99 1 0.92 0 1 0 0
100 1 0.64 0 0 0 1
101 1 0.64 0 0 0 1
102 1 0.68 0 0 0 1
103 1 0.72 0 0 0 1
104 1 0.72 0 0 0 1
105 1 0.74 0 0 0 1
106 1 0.76 0 0 0 1
107 1 0.76 0 0 0 1
108 1 0.76 0 0 0 1
109 1 0.78 0 0 0 1
110 1 0.80 0 0 0 1
111 1 0.80 0 0 0 1
112 1 0.82 0 0 0 1
113 1 0.82 0 0 0 1
114 1 0.84 0 0 0 1
115 1 0.84 0 0 0 1
116 1 0.84 0 0 0 1
117 1 0.84 0 0 0 1
118 1 0.88 0 0 0 1
119 1 0.88 0 0 0 1
120 1 0.88 0 0 0 1
121 1 0.88 0 0 0 1
122 1 0.88 0 0 0 1
123 1 0.88 0 0 0 1
124 1 0.92 0 0 0 1
thorax:activityone thorax:activitylow thorax:activitymany
1 0.00 0.00 0.68
2 0.00 0.00 0.68
3 0.00 0.00 0.72
4 0.00 0.00 0.72
5 0.00 0.00 0.76
6 0.00 0.00 0.76
7 0.00 0.00 0.76
8 0.00 0.00 0.76
9 0.00 0.00 0.76
10 0.00 0.00 0.80
11 0.00 0.00 0.80
12 0.00 0.00 0.80
13 0.00 0.00 0.84
14 0.00 0.00 0.84
15 0.00 0.00 0.84
16 0.00 0.00 0.84
17 0.00 0.00 0.84
18 0.00 0.00 0.84
19 0.00 0.00 0.88
20 0.00 0.00 0.88
21 0.00 0.00 0.92
22 0.00 0.00 0.92
23 0.00 0.00 0.92
24 0.00 0.00 0.94
25 0.00 0.00 0.00
26 0.00 0.00 0.00
27 0.00 0.00 0.00
28 0.00 0.00 0.00
29 0.00 0.00 0.00
30 0.00 0.00 0.00
31 0.00 0.00 0.00
32 0.00 0.00 0.00
33 0.00 0.00 0.00
34 0.00 0.00 0.00
35 0.00 0.00 0.00
36 0.00 0.00 0.00
37 0.00 0.00 0.00
38 0.00 0.00 0.00
39 0.00 0.00 0.00
40 0.00 0.00 0.00
41 0.00 0.00 0.00
42 0.00 0.00 0.00
43 0.00 0.00 0.00
44 0.00 0.00 0.00
45 0.00 0.00 0.00
46 0.00 0.00 0.00
47 0.00 0.00 0.00
48 0.00 0.00 0.00
49 0.00 0.00 0.00
50 0.64 0.00 0.00
51 0.68 0.00 0.00
52 0.72 0.00 0.00
53 0.76 0.00 0.00
54 0.76 0.00 0.00
55 0.80 0.00 0.00
56 0.80 0.00 0.00
57 0.80 0.00 0.00
58 0.82 0.00 0.00
59 0.82 0.00 0.00
60 0.84 0.00 0.00
61 0.84 0.00 0.00
62 0.84 0.00 0.00
63 0.84 0.00 0.00
64 0.84 0.00 0.00
65 0.84 0.00 0.00
66 0.88 0.00 0.00
67 0.88 0.00 0.00
68 0.88 0.00 0.00
69 0.88 0.00 0.00
70 0.88 0.00 0.00
71 0.88 0.00 0.00
72 0.88 0.00 0.00
73 0.92 0.00 0.00
74 0.92 0.00 0.00
75 0.00 0.68 0.00
76 0.00 0.68 0.00
77 0.00 0.72 0.00
78 0.00 0.76 0.00
79 0.00 0.78 0.00
80 0.00 0.80 0.00
81 0.00 0.80 0.00
82 0.00 0.80 0.00
83 0.00 0.84 0.00
84 0.00 0.84 0.00
85 0.00 0.84 0.00
86 0.00 0.84 0.00
87 0.00 0.84 0.00
88 0.00 0.84 0.00
89 0.00 0.88 0.00
90 0.00 0.88 0.00
91 0.00 0.88 0.00
92 0.00 0.90 0.00
93 0.00 0.90 0.00
94 0.00 0.90 0.00
95 0.00 0.90 0.00
96 0.00 0.90 0.00
97 0.00 0.90 0.00
98 0.00 0.92 0.00
99 0.00 0.92 0.00
100 0.00 0.00 0.00
101 0.00 0.00 0.00
102 0.00 0.00 0.00
103 0.00 0.00 0.00
104 0.00 0.00 0.00
105 0.00 0.00 0.00
106 0.00 0.00 0.00
107 0.00 0.00 0.00
108 0.00 0.00 0.00
109 0.00 0.00 0.00
110 0.00 0.00 0.00
111 0.00 0.00 0.00
112 0.00 0.00 0.00
113 0.00 0.00 0.00
114 0.00 0.00 0.00
115 0.00 0.00 0.00
116 0.00 0.00 0.00
117 0.00 0.00 0.00
118 0.00 0.00 0.00
119 0.00 0.00 0.00
120 0.00 0.00 0.00
121 0.00 0.00 0.00
122 0.00 0.00 0.00
123 0.00 0.00 0.00
124 0.00 0.00 0.00
thorax:activityhigh
1 0.00
2 0.00
3 0.00
4 0.00
5 0.00
6 0.00
7 0.00
8 0.00
9 0.00
10 0.00
11 0.00
12 0.00
13 0.00
14 0.00
15 0.00
16 0.00
17 0.00
18 0.00
19 0.00
20 0.00
21 0.00
22 0.00
23 0.00
24 0.00
25 0.00
26 0.00
27 0.00
28 0.00
29 0.00
30 0.00
31 0.00
32 0.00
33 0.00
34 0.00
35 0.00
36 0.00
37 0.00
38 0.00
39 0.00
40 0.00
41 0.00
42 0.00
43 0.00
44 0.00
45 0.00
46 0.00
47 0.00
48 0.00
49 0.00
50 0.00
51 0.00
52 0.00
53 0.00
54 0.00
55 0.00
56 0.00
57 0.00
58 0.00
59 0.00
60 0.00
61 0.00
62 0.00
63 0.00
64 0.00
65 0.00
66 0.00
67 0.00
68 0.00
69 0.00
70 0.00
71 0.00
72 0.00
73 0.00
74 0.00
75 0.00
76 0.00
77 0.00
78 0.00
79 0.00
80 0.00
81 0.00
82 0.00
83 0.00
84 0.00
85 0.00
86 0.00
87 0.00
88 0.00
89 0.00
90 0.00
91 0.00
92 0.00
93 0.00
94 0.00
95 0.00
96 0.00
97 0.00
98 0.00
99 0.00
100 0.64
101 0.64
102 0.68
103 0.72
104 0.72
105 0.74
106 0.76
107 0.76
108 0.76
109 0.78
110 0.80
111 0.80
112 0.82
113 0.82
114 0.84
115 0.84
116 0.84
117 0.84
118 0.88
119 0.88
120 0.88
121 0.88
122 0.88
123 0.88
124 0.92
attr(,"assign")
[1] 0 1 2 2 2 2 3 3 3 3
attr(,"contrasts")
attr(,"contrasts")$activity
[1] "contr.treatment"
plot(lmod)
anova(lmod)
Analysis of Variance Table
Response: longevity
Df Sum Sq Mean Sq F value Pr(>F)
thorax 1 15003 15003 130.73 < 2e-16
activity 4 9635 2409 20.99 5.5e-13
thorax:activity 4 24 6 0.05 0.99
Residuals 114 13083 115
lmodp <- lm(longevity ~ thorax + activity, fruitfly)
drop1(lmodp, test = "F")
Single term deletions
Model:
longevity ~ thorax + activity
Df Sum of Sq RSS AIC F value Pr(>F)
<none> 13107 590
thorax 1 12368 25476 670 111.3 <2e-16
activity 4 9635 22742 650 21.7 2e-13
sumary(lmodp)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -48.75 10.85 -4.49 1.6e-05
thorax 134.34 12.73 10.55 < 2e-16
activityone 2.64 2.98 0.88 0.38
activitylow -7.01 2.98 -2.35 0.02
activitymany 4.14 3.03 1.37 0.17
activityhigh -20.00 3.02 -6.63 1.0e-09
n = 124, p = 6, Residual SE = 10.54, R-Squared = 0.65
plot(residuals(lmodp) ~ fitted(lmodp), pch = unclass(fruitfly$activity), xlab = "Fitted",
ylab = "Residuals")
abline(h = 0)
lmodl <- lm(log(longevity) ~ thorax + activity, fruitfly)
plot(residuals(lmodl) ~ fitted(lmodl), pch = unclass(fruitfly$activity), xlab = "Fitted",
ylab = "Residuals")
abline(h = 0)
sumary(lmodl)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.8442 0.1988 9.28 1.0e-15
thorax 2.7215 0.2333 11.67 < 2e-16
activityone 0.0517 0.0547 0.95 0.346
activitylow -0.1239 0.0546 -2.27 0.025
activitymany 0.0879 0.0555 1.59 0.116
activityhigh -0.4193 0.0553 -7.59 8.4e-12
n = 124, p = 6, Residual SE = 0.19, R-Squared = 0.7
exp(coef(lmodl)[3:6])
activityone activitylow activitymany activityhigh
1.0531 0.8835 1.0919 0.6575
lmodh <- lm(thorax ~ activity, fruitfly)
anova(lmodh)
Analysis of Variance Table
Response: thorax
Df Sum Sq Mean Sq F value Pr(>F)
activity 4 0.026 0.00639 1.11 0.36
Residuals 119 0.685 0.00576
lmodu <- lm(log(longevity) ~ activity, fruitfly)
sumary(lmodu)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.1193 0.0564 72.99 < 2e-16
activityone 0.0234 0.0798 0.29 0.77
activitylow -0.1195 0.0798 -1.50 0.14
activitymany 0.0240 0.0806 0.30 0.77
activityhigh -0.5172 0.0798 -6.48 2.2e-09
n = 124, p = 5, Residual SE = 0.28, R-Squared = 0.36
contr.treatment(4)
2 3 4
1 0 0 0
2 1 0 0
3 0 1 0
4 0 0 1
contr.helmert(4)
[,1] [,2] [,3]
1 -1 -1 -1
2 1 -1 -1
3 0 2 -1
4 0 0 3
contr.sum(4)
[,1] [,2] [,3]
1 1 0 0
2 0 1 0
3 0 0 1
4 -1 -1 -1
contrasts(sexab$csa) <- contr.sum(2)
sumary(lm(ptsd ~ csa, sexab))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.318 0.405 20.53 < 2e-16
csa1 -3.623 0.405 -8.94 2.2e-13
n = 76, p = 2, Residual SE = 3.47, R-Squared = 0.52
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 14:02:29 BST"