temp = c(66, 70, 69, 80, 68, 67, 72, 73, 70, 57, 63, 70, 78,
67, 53, 67, 75, 70, 81, 76, 79, 75, 76, 58)
damage = c(0, 1, 0, NA, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 1)
dat = data.frame(temp, damage)
dat
## temp damage
## 1 66 0
## 2 70 1
## 3 69 0
## 4 80 NA
## 5 68 0
## 6 67 0
## 7 72 0
## 8 73 0
## 9 70 0
## 10 57 1
## 11 63 1
## 12 70 1
## 13 78 0
## 14 67 0
## 15 53 1
## 16 67 0
## 17 75 0
## 18 70 0
## 19 81 0
## 20 76 0
## 21 79 0
## 22 75 1
## 23 76 0
## 24 58 1
library(ggplot2)
p = ggplot(data=dat, aes(x = temp, y = damage))
p + geom_point(alpha = 0.15) + geom_smooth(method = "glm",
method.args = list(family = "binomial"))
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
# mô hình hồi qui logistic
m = glm(damage ~ temp, family=binomial, data=dat)
summary(m)
##
## Call:
## glm(formula = damage ~ temp, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0611 -0.7613 -0.3783 0.4524 2.2175
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 15.0429 7.3786 2.039 0.0415 *
## temp -0.2322 0.1082 -2.145 0.0320 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28.267 on 22 degrees of freedom
## Residual deviance: 20.315 on 21 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 24.315
##
## Number of Fisher Scoring iterations: 5
library(GGally); library(ggplot2)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
ggcoef(m, exponentiate=T, exclude_intercept=T, vline_color = "red", errorbar_color = "blue", errorbar_height = 0.10)
ggcoef(m, exponentiate = TRUE, exclude_intercept=T, mapping = aes(x = estimate, y = term, size = p.value)) + scale_size_continuous(trans = "reverse")
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