137 lines
4.3 KiB
R
137 lines
4.3 KiB
R
# ZAD1
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load(url("http://ls.home.amu.edu.pl/data_sets/liver_data.RData"))
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head(liver_data)
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liver_data$condition <- ifelse(liver_data$condition == "Yes", 1, 0)
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model_1 <- glm(condition ~ bilirubin + ldh, data = liver_data, family = 'binomial')
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model_1
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summary(model_1)
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step(model_1)
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exp(coef(model_1)[2])
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exp(coef(model_1)[3])
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install.packages("ROCR")
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library(ROCR)
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pred_1 <- prediction(model_1$fitted, liver_data$condition)
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plot(performance(pred_1, 'tpr', 'fpr'), main = "Model 1")
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performance(pred_1, 'auc')@y.values
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liver_data_new <- data.frame(bilirubin = c(0.9, 2.1, 3.4), ldh = c(100, 200, 300))
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(predict_glm <- stats::predict(model_1,
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liver_data_new,
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type = 'response'))
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model_1_hat <- coef(model_1)[1] +
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coef(model_1)[2] * liver_data$bilirubin +
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coef(model_1)[3] * liver_data$ldh
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model_1_temp <- seq(min(model_1_hat) - 1, max(model_1_hat) + 2.5, length.out = 100)
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condition_temp <- exp(model_1_temp) / (1 + exp(model_1_temp))
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plot(model_1_temp, condition_temp, type = "l", xlab = "X beta", ylab = "condition",
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xlim = c(-6, 9), ylim = c(-0.1, 1.1))
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points(model_1_hat, liver_data$condition, pch = 16)
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points(coef(model_1)[1] +
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coef(model_1)[2] * liver_data_new$bilirubin +
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coef(model_1)[3] * liver_data_new$ldh,
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predict_glm, pch = 16, col = "red")
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# obserwacje odstajace
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mode_1_hat_c1 <- model_1_hat[liver_data$condition == 1]
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which(model_1_hat == min(mode_1_hat_c1) & liver_data$condition == 1)
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which(model_1_hat == min(mode_1_hat_c1[mode_1_hat_c1 > min(mode_1_hat_c1)])
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& liver_data$condition == 1)
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mode_1_hat_c0 <- model_1_hat[liver_data$condition == 0]
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which(model_1_hat == max(mode_1_hat_c0) & liver_data$condition == 0)
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liver_data_wo <- liver_data[-c(18, 26, 29), ]
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model_1_wo <- glm(condition ~ bilirubin + ldh, data = liver_data_wo, family = 'binomial')
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model_1_wo
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summary(model_1_wo)
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step(model_1_wo)
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exp(coef(model_1_wo)[2])
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exp(coef(model_1_wo)[3])
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pred_1_wo <- prediction(model_1_wo$fitted, liver_data_wo$condition)
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plot(performance(pred_1_wo, 'tpr', 'fpr'), main = "Model 1 (wo)")
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performance(pred_1_wo, 'auc')@y.values
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liver_data_wo_new <- data.frame(bilirubin = c(0.9, 2.1, 3.4), ldh = c(100, 200, 300))
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(predict_glm_wo <- stats::predict(model_1_wo,
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liver_data_wo_new,
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type = 'response'))
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model_1_wo_hat <- coef(model_1_wo)[1] +
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coef(model_1_wo)[2] * liver_data_wo$bilirubin +
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coef(model_1_wo)[3] * liver_data_wo$ldh
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model_1_wo_temp <- seq(min(model_1_wo_hat) - 10, max(model_1_wo_hat) + 20, length.out = 100)
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condition_wo_temp <- exp(model_1_wo_temp) / (1 + exp(model_1_wo_temp))
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plot(model_1_wo_temp, condition_wo_temp, type = "l", xlab = "X beta", ylab = "condition",
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xlim = c(-40, 89), ylim = c(-0.1, 1.1))
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points(model_1_wo_hat, liver_data_wo$condition, pch = 16)
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points(coef(model_1_wo)[1] +
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coef(model_1_wo)[2] * liver_data_wo_new$bilirubin +
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coef(model_1_wo)[3] * liver_data_wo_new$ldh,
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predict_glm_wo, pch = 16, col = "red")
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# ZAD2
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install.packages("DAAG")
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library(DAAG)
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head(moths)
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model_1 <- glm(A ~ log(meters), data = moths, family = 'poisson')
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model_1
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summary(model_1)
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step(model_1)
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data_new <- data.frame(meters = c(3, 20, 100))
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(pred_1 <- stats::predict(model_1, data_new, type = "response"))
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moths$A_hat <- stats::predict(model_1, type = "response")
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moths <- moths[with(moths, order(meters)), ]
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plot(log(moths$meters), moths$A_hat,
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type = "l", col = "red", lwd = 2,
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xlab = "log(moths$meters)", ylab = "A",
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ylim = c(0, 40), main = "Model 1")
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points(log(moths$meters), moths$A, pch = 16)
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points(log(data_new$meters), pred_1, pch = 16, col = "blue", lwd = 4)
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cat("1.", "\n")
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rm(moths)
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model_2 <- glm(P ~ log(meters), data = moths, family = 'poisson')
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model_2
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cat("2.", "\n")
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summary(model_2)
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cat("3.", "\n")
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step(model_2)
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cat("4.", "\n")
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(pred_2 <- stats::predict(model_2, data_new, type = "response"))
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moths$P_hat <- stats::predict(model_2, type = "response")
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moths <- moths[with(moths, order(meters)), ]
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plot(log(moths$meters), moths$P_hat,
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type = "l", col = "red", lwd = 2,
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xlab = "log(moths$meters)", ylab = "P",
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ylim = c(0, 20), main = "Model 2")
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points(log(moths$meters), moths$P, pch = 16)
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points(log(data_new$meters), pred_2, pch = 16, col = "blue", lwd = 4)
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