williams2005/RCPComponentDiffFixtures

# 步骤 1：输入数据 glucose <- c(0, 20, 40, 60, 80, 100) # X 值：已知葡萄糖浓度 (µg/ml) od <- c(0.00, 0.304, 0.463, 0.488, 0.587, 0.596) # Y 值：对应的光密度 unknown_od <- 0.473 # 未知样品的光密度 步骤 2：执行线性回归以获得最佳拟合线 model <- lm(od ~ glucose) # 拟合 O.D. = 截距 + 斜率 * 浓度 # 打印方程和 R 平方（显示拟合优度） summary(model) # 步骤 3：利用直线方程计算未知葡萄糖浓度 slope <- coef(model)["glucose"] # 直线的斜率 intercept <- coef(model)["(Intercept)"] # Y 轴截距 unknown_glucose <- (unknown_od - intercept) / slope cat("Unknown glucose concentration =", round(unknown_glucose, 1), "µg/ml\n") # 步骤 4：绘制图表 # 首先创建基础图表 plot(glucose, od, xlab = "Glucose Concentration (µg/ml)", ylab = "Optical Density (O.D.)", main = "A graph of Optical Density against Glucose Concentration (µg/ml)", pch = 16, # 实心圆点 col = "blue", # 标准品的蓝色点 xlim = c(0, 120), # 稍微延伸 x 轴以容纳未知值 ylim = c(0, 0.7)) # 合适的 y 轴范围 # 添加回归线 abline(model, col = "red", lwd = 2) # 添加未知点 points(unknown_glucose, unknown_od, pch = 17, col = "green", cex = 1.5) # 添加虚线以追踪未知值 segments(0, unknown_od, unknown_glucose, unknown_od, col = "green", lty = 2) segments(unknown_glucose, 0, unknown_glucose, unknown_od, col = "green", lty = 2) # 添加标签 text(unknown_glucose + 8, unknown_od, paste("Unknown ≈", round(unknown_glucose, 1), "µg/ml"), col = "green")

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