| y,x1,x2 0,3.6,60.1 1,4.1,52.0 0,3.7,62.5 0,4.9,60.6 1,4.4,54.1 0,3.6,63.6 1,4.9,68.0 0,4.8,38.2 1,4.1,59.5 0,4.3,47.3 |
| admit,gre,gpa,rank 0,380,3.61,3 1,660,3.67,3 1,800,4,1 1,640,3.19,4 0,520,2.93,4 1,760,3,2 1,560,2.98,1 0,400,3.08,2 1,540,3.39,3 0,700,3.92,2 0,800,4,4 0,440,3.22,1 1,760,4,1 0,700,3.08,2 1,700,4,1 0,480,3.44,3 0,780,3.87,4 0,360,2.56,3 0,800,3.75,2 1,540,3.81,1 0,500,3.17,3 1,660,3.63,2 0,600,2.82,4 0,680,3.19,4 1,760,3.35,2 1,800,3.66,1 1,620,3.61,1 1,520,3.74,4 1,780,3.22,2 0,520,3.29,1 0,540,3.78,4 0,760,3.35,3 0,600,3.4,3 1,800,4,3 0,360,3.14,1 0,400,3.05,2 0,580,3.25,1 0,520,2.9,3 1,500,3.13,2 1,520,2.68,3 0,560,2.42,2 1,580,3.32,2 1,600,3.15,2 0,500,3.31,3 0,700,2.94,2 1,460,3.45,3 1,580,3.46,2 0,500,2.97,4 0,440,2.48,4 0,400,3.35,3 0,640,3.86,3 0,440,3.13,4 0,740,3.37,4 1,680,3.27,2 0,660,3.34,3 1,740,4,3 0,560,3.19,3 0,380,2.94,3 0,400,3.65,2 0,600,2.82,4 1,620,3.18,2 0,560,3.32,4 0,640,3.67,3 1,680,3.85,3 0,580,4,3 0,600,3.59,2 0,740,3.62,4 0,620,3.3,1 0,580,3.69,1 0,800,3.73,1 0,640,4,3 0,300,2.92,4 0,480,3.39,4 0,580,4,2 0,720,3.45,4 0,720,4,3 0,560,3.36,3 1,800,4,3 0,540,3.12,1 1,620,4,1 0,700,2.9,4 0,620,3.07,2 0,500,2.71,2 0,380,2.91,4 1,500,3.6,3 0,520,2.98,2 0,600,3.32,2 0,600,3.48,2 0,700,3.28,1 1,660,4,2 0,700,3.83,2 1,720,3.64,1 0,800,3.9,2 0,580,2.93,2 1,660,3.44,2 0,660,3.33,2 0,640,3.52,4 0,480,3.57,2 0,700,2.88,2 0,400,3.31,3 0,340,3.15,3 0,580,3.57,3 0,380,3.33,4 0,540,3.94,3 1,660,3.95,2 1,740,2.97,2 1,700,3.56,1 0,480,3.13,2 0,400,2.93,3 0,480,3.45,2 0,680,3.08,4 0,420,3.41,4 0,360,3,3 0,600,3.22,1 0,720,3.84,3 0,620,3.99,3 1,440,3.45,2 0,700,3.72,2 1,800,3.7,1 0,340,2.92,3 1,520,3.74,2 1,480,2.67,2 0,520,2.85,3 0,500,2.98,3 0,720,3.88,3 0,540,3.38,4 1,600,3.54,1 0,740,3.74,4 0,540,3.19,2 0,460,3.15,4 1,620,3.17,2 0,640,2.79,2 0,580,3.4,2 0,500,3.08,3 0,560,2.95,2 0,500,3.57,3 0,560,3.33,4 0,700,4,3 0,620,3.4,2 1,600,3.58,1 0,640,3.93,2 1,700,3.52,4 0,620,3.94,4 0,580,3.4,3 0,580,3.4,4 0,380,3.43,3 0,480,3.4,2 0,560,2.71,3 1,480,2.91,1 0,740,3.31,1 1,800,3.74,1 0,400,3.38,2 1,640,3.94,2 0,580,3.46,3 0,620,3.69,3 1,580,2.86,4 0,560,2.52,2 1,480,3.58,1 0,660,3.49,2 0,700,3.82,3 0,600,3.13,2 0,640,3.5,2 1,700,3.56,2 0,520,2.73,2 0,580,3.3,2 0,700,4,1 0,440,3.24,4 0,720,3.77,3 0,500,4,3 0,600,3.62,3 0,400,3.51,3 0,540,2.81,3 0,680,3.48,3 1,800,3.43,2 0,500,3.53,4 1,620,3.37,2 0,520,2.62,2 1,620,3.23,3 0,620,3.33,3 0,300,3.01,3 0,620,3.78,3 0,500,3.88,4 0,700,4,2 1,540,3.84,2 0,500,2.79,4 0,800,3.6,2 0,560,3.61,3 0,580,2.88,2 0,560,3.07,2 0,500,3.35,2 1,640,2.94,2 0,800,3.54,3 0,640,3.76,3 0,380,3.59,4 1,600,3.47,2 0,560,3.59,2 0,660,3.07,3 1,400,3.23,4 0,600,3.63,3 0,580,3.77,4 0,800,3.31,3 1,580,3.2,2 1,700,4,1 0,420,3.92,4 1,600,3.89,1 1,780,3.8,3 0,740,3.54,1 1,640,3.63,1 0,540,3.16,3 0,580,3.5,2 0,740,3.34,4 0,580,3.02,2 0,460,2.87,2 0,640,3.38,3 1,600,3.56,2 1,660,2.91,3 0,340,2.9,1 1,460,3.64,1 0,460,2.98,1 1,560,3.59,2 0,540,3.28,3 0,680,3.99,3 1,480,3.02,1 0,800,3.47,3 0,800,2.9,2 1,720,3.5,3 0,620,3.58,2 0,540,3.02,4 0,480,3.43,2 1,720,3.42,2 0,580,3.29,4 0,600,3.28,3 0,380,3.38,2 0,420,2.67,3 1,800,3.53,1 0,620,3.05,2 1,660,3.49,2 0,480,4,2 0,500,2.86,4 0,700,3.45,3 0,440,2.76,2 1,520,3.81,1 1,680,2.96,3 0,620,3.22,2 0,540,3.04,1 0,800,3.91,3 0,680,3.34,2 0,440,3.17,2 0,680,3.64,3 0,640,3.73,3 0,660,3.31,4 0,620,3.21,4 1,520,4,2 1,540,3.55,4 1,740,3.52,4 0,640,3.35,3 1,520,3.3,2 1,620,3.95,3 0,520,3.51,2 0,640,3.81,2 0,680,3.11,2 0,440,3.15,2 1,520,3.19,3 1,620,3.95,3 1,520,3.9,3 0,380,3.34,3 0,560,3.24,4 1,600,3.64,3 1,680,3.46,2 0,500,2.81,3 1,640,3.95,2 0,540,3.33,3 1,680,3.67,2 0,660,3.32,1 0,520,3.12,2 1,600,2.98,2 0,460,3.77,3 1,580,3.58,1 1,680,3,4 1,660,3.14,2 0,660,3.94,2 0,360,3.27,3 0,660,3.45,4 0,520,3.1,4 1,440,3.39,2 0,600,3.31,4 1,800,3.22,1 1,660,3.7,4 0,800,3.15,4 0,420,2.26,4 1,620,3.45,2 0,800,2.78,2 0,680,3.7,2 0,800,3.97,1 0,480,2.55,1 0,520,3.25,3 0,560,3.16,1 0,460,3.07,2 0,540,3.5,2 0,720,3.4,3 0,640,3.3,2 1,660,3.6,3 1,400,3.15,2 1,680,3.98,2 0,220,2.83,3 0,580,3.46,4 1,540,3.17,1 0,580,3.51,2 0,540,3.13,2 0,440,2.98,3 0,560,4,3 0,660,3.67,2 0,660,3.77,3 1,520,3.65,4 0,540,3.46,4 1,300,2.84,2 1,340,3,2 1,780,3.63,4 1,480,3.71,4 0,540,3.28,1 0,460,3.14,3 0,460,3.58,2 0,500,3.01,4 0,420,2.69,2 0,520,2.7,3 0,680,3.9,1 0,680,3.31,2 1,560,3.48,2 0,580,3.34,2 0,500,2.93,4 0,740,4,3 0,660,3.59,3 0,420,2.96,1 0,560,3.43,3 1,460,3.64,3 1,620,3.71,1 0,520,3.15,3 0,620,3.09,4 0,540,3.2,1 1,660,3.47,3 0,500,3.23,4 1,560,2.65,3 0,500,3.95,4 0,580,3.06,2 0,520,3.35,3 0,500,3.03,3 0,600,3.35,2 0,580,3.8,2 0,400,3.36,2 0,620,2.85,2 1,780,4,2 0,620,3.43,3 1,580,3.12,3 0,700,3.52,2 1,540,3.78,2 1,760,2.81,1 0,700,3.27,2 0,720,3.31,1 1,560,3.69,3 0,720,3.94,3 1,520,4,1 1,540,3.49,1 0,680,3.14,2 0,460,3.44,2 1,560,3.36,1 0,480,2.78,3 0,460,2.93,3 0,620,3.63,3 0,580,4,1 0,800,3.89,2 1,540,3.77,2 1,680,3.76,3 1,680,2.42,1 1,620,3.37,1 0,560,3.78,2 0,560,3.49,4 0,620,3.63,2 1,800,4,2 0,640,3.12,3 0,540,2.7,2 0,700,3.65,2 1,540,3.49,2 0,540,3.51,2 0,660,4,1 1,480,2.62,2 0,420,3.02,1 1,740,3.86,2 0,580,3.36,2 0,640,3.17,2 0,640,3.51,2 1,800,3.05,2 1,660,3.88,2 1,600,3.38,3 1,620,3.75,2 1,460,3.99,3 0,620,4,2 0,560,3.04,3 0,460,2.63,2 0,700,3.65,2 0,600,3.89,3 |
0_runme.txt
########## R: Logistic Regression ##### Run this script on your R Console # Set your working directory on your R Console # The following directory is dummy - set to your own directory where you save all the r files below. #setwd('/Users/XXX/Downloads/') #source('1_install_packages.r') # You have to run this r script only for the first time. #source('2_library.r') source('3_logistics_regression.r') source('4_logistic_regression.r') |
Source: https://qiita.com/katsu1110/items/e4ef613559f02f183af5
1_install_packages.r
| # |
| # |
3_logistics_regression.r
| # data loading data1 <- read.csv("data1.csv", header=TRUE) # Logistic Regression mylogit = glm(y ~ x1 + x2, data=data1, family=binomial(link="logit")) # Summary summary(mylogit) #=> # Call: # glm(formula = y ~ x1 + x2, family = binomial(link ="logit"), data = data1) # Deviance Residuals: # Min 1Q Median 3Q Max # -1.4754 -0.8584 -0.8007 1.1905 1.5719 # Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -9.44589 9.12237 -1.035 0.300 # x1 1.27158 1.49423 0.851 0.395 # x2 0.06424 0.08739 0.735 0.462 # (Dispersion parameter for binomial family taken to be 1) # Null deviance: 13.460 on 9 degrees of freedom # Residual deviance: 12.345 on 7 degrees of freedom # AIC: 18.345 # Number of Fisher Scoring iterations: 4 # See fitted numbers fitted(mylogit) #=> # 1 2 3 4 5 6 7 8 9 10 # 0.2675020 0.2907201 0.3260736 0.6632579 0.4072105 0.3137831 0.7600997 0.2914609 0.3988916 0.2810008 # Fitted (predicted) numbers are calculated by the following equation. # logit(π)=−9.44589+1.27158x1+0.06424x2 # However, as you can see Pr(>|z|) in the result of summary, this is not significant. |
4_logistic_regression.r
| # Data loading #mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv") mydata <- read.csv("binary.csv") mydata$rank <- factor(mydata$rank) # Logistic Regression mylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial") # Summary summary(mylogit) #=> # Call: # glm(formula = admit ~ gre + gpa + rank, family = "binomial", data = mydata) # Deviance Residuals: # Min 1Q Median 3Q Max # -1.6268 -0.8662 -0.6388 1.1490 2.0790 # Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -3.989979 1.139951 -3.500 0.000465 *** # gre 0.002264 0.001094 2.070 0.038465 * # gpa 0.804038 0.331819 2.423 0.015388 * # rank2 -0.675443 0.316490 -2.134 0.032829 * # rank3 -1.340204 0.345306 -3.881 0.000104 *** # rank4 -1.551464 0.417832 -3.713 0.000205 *** # --- # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # (Dispersion parameter for binomial family taken to be 1) # Null deviance: 499.98 on 399 degrees of freedom # Residual deviance: 458.52 on 394 degrees of freedom # AIC: 470.52 # Number of Fisher Scoring iterations: 4 newdata1 <- with(mydata, data.frame(gre = mean(gre), gpa = mean(gpa), rank = factor(1:4))) newdata1$rankP <- predict(mylogit, newdata = newdata1, type = "response") newdata2 <- with(mydata, data.frame(gre = rep(seq(from = 200, to = 800, length.out = 100), 4), gpa = mean(gpa), rank = factor(rep(1:4, each = 100)))) newdata3 <- cbind(newdata2, predict(mylogit, newdata = newdata2, type = "link", se = TRUE)) newdata3 <- within(newdata3, { PredictedProb <- plogis(fit) LL <- plogis(fit - (1.96 * se.fit)) UL <- plogis(fit + (1.96 * se.fit)) }) # Using graphic library library("ggplot2") ggplot(newdata3, aes(x = gre, y = PredictedProb)) + geom_ribbon(aes(ymin = LL, ymax = UL, fill = rank), alpha = 0.2) + geom_line(aes(colour = rank), size = 1) # Save as an image file ggsave("image.png") #Reference #https://stats.idre.ucla.edu/r/dae/logit-regression/ |
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