> d <- read.csv("data3a.csv")
> d
y x f
1 6 8.31 C
2 6 9.44 C
3 6 9.50 C
99 7 10.86 T
100 9 9.97 T
> d$x
[1] 8.31 9.44 9.50 9.07 10.16 8.32 10.61 10.06 9.93 10.43 10.36 10.15
[13] 10.92 8.85 9.42 11.11 8.02 11.93 8.55 7.19 9.83 10.79 8.89 10.09
[25] 11.63 10.21 9.45 10.44 9.44 10.48 9.43 10.32 10.33 8.50 9.41 8.96
[85] 9.73 10.78 10.21 10.51 10.73 8.85 11.20 9.86 11.54 10.03 11.88 9.15
[97] 8.52 10.24 10.86 9.97
> d$y
[1] 6 6 6 12 10 4 9 9 9 11 6 10 6 10 11 8 3 8 5 5 4 11 5 10
[97] 6 8 7 9
> d$f
[1] C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C
[37] C C C C C C C C C C C C C C T T T T T T T T T T T T T T T T T T T T T T
[73] T T T T T T T T T T T T T T T T T T T T T T T T T T T T
Levels: C T
> class(d)
[1] "data.frame"
> class(d$y)
[1] "integer"
> class(d$x)
[1] "numeric"
> class(d$f)
[1] "factor"
> summary(d)
y x f
Min. : 2.00 Min. : 7.190 C:50
1st Qu.: 6.00 1st Qu.: 9.428 T:50
Median : 8.00 Median :10.155
Mean : 7.83 Mean :10.089
3rd Qu.:10.00 3rd Qu.:10.685
Max. :15.00 Max. :12.400
> plot(d$x, d$y, pch = c(21,19)[d$f])
> legend("topleft", legend = c("C", "T"), pch = c(21,19))
> fit <- glm(y ~ x, data ~ d, family = poisson)
以下にエラー as.data.frame.default(data) :
cannot coerce class ""formula"" to a data.frame
> fit <- glm(y ~ x, data = d, family = poisson)
> fit
Call: glm(formula = y ~ x, family = poisson, data = d)
Coefficients:
(Intercept) x
1.29172 0.07566
Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
Null Deviance: 89.51
Residual Deviance: 84.99 AIC: 474.8
> summary(fit)
Call:
glm(formula = y ~ x, family = poisson, data = d)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3679 -0.7348 -0.1775 0.6987 2.3760
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.29172 0.36369 3.552 0.000383 ***
x 0.07566 0.03560 2.125 0.033580 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 89.507 on 99 degrees of freedom
Residual deviance: 84.993 on 98 degrees of freedom
AIC: 474.77
Number of Fisher Scoring iterations: 4
> logLik(fit)
'log Lik.' -235.3863 (df=2)
> plot(d$x, d$y, pch = c(21, 19)[d$f])
> xx <- seq(min(d$x), max(d$x), length = 100)
> lines(xx, exp(1.29 + 0.0757 * xx), lwd = 2)
> fit.f <- glm(y ~ f, data = d, family = poisson)
> fit.f
Call: glm(formula = y ~ f, family = poisson, data = d)
Coefficients:
(Intercept) fT
2.05156 0.01277
Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
Null Deviance: 89.51
Residual Deviance: 89.48 AIC: 479.3
> fit.all <- glm(y ~ x + f, data = d, family = poisson)
> fit.all
Call: glm(formula = y ~ x + f, family = poisson, data = d)
Coefficients:
(Intercept) x fT
1.26311 0.08007 -0.03200
Degrees of Freedom: 99 Total (i.e. Null); 97 Residual
Null Deviance: 89.51
Residual Deviance: 84.81 AIC: 476.6
> logLik(fit.all)
'log Lik.' -235.2937 (df=3)
