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多元线性回归 multiple linear regression ##例1:new.eg1 rm(list=ls()) setwd("/Users/sifan/R/datasets") dat <- read.csv("new.eg1.csv",header=T) dat ## x1 x2 x3 x4 y ## 1 5.68 1.90 4.53 8.2 11.2 ## 2 3.79 1.64 7.32 6.9 8.8 ## 3 6.02 3.56 6.95 10.8 12.3 ## 4 4.85 1.0...
简单线性回归 simple linear regression x <- c(60,62,64,65,66,67,68,70,72,74) y <- c(63.6,65.2,66,65.5,66.9,67.1,67.4,68.3,70.1,70) dat <- data.frame(x=x,y=y) plot(dat) fit <- lm(y~x) summary(fit) ## ## Call: ## lm(formula = y ~ x) ## ## Residuals: ## Mi...
test_data = rentaldata[rentaldata$Year ==2015,];#Use the RentalCount column to check the quality of the prediction against actual valuesactual_counts <- test_data$RentalCount;#Model 1: Use lm to create a linear regression model, trained with the training data setmodel_lm <- lm(RentalCoun...
> boxplot(modeldat$total_exp) > 1. 2. 3. 4. 如上,数据集modeldat中没有缺失值,但是明显有离群点,而且因变量total_exp分布明显偏离正太。 我们需要删除离群点,然后对因变量进行对数变换 我们用Z分值的方法查找并删除离群点。 > y=modeldat$total_exp ...
rxLogit * Fits a logistic regression model to data. rxGlm * Fits a generalized linear model to data. rxCovCor * Calculate the covariance, correlation, or sum of squares / cross-product matrix for a set of variables. rxDTree * Fits a classification or regression tree to data. rxBTrees ...
An Appendix to An R Companion to Applied Regression, Second Edition John Fox & Sanford Weisberg Linear Mixed-Effects Models Using R(一本教材,进阶选用) A Step-by-Step Approach Andrzej Ga?ecki ? Tomasz Burzykowski 3.R中的线性混合模型介绍(简单了解不同的包) ...
It can be used to carry out regression, single stratum analysis of variance and analysis of covariance (stasts) model.matrix: creates a design matrix (stasts) predict: Predicted values based on linear model object (stasts) residuals: is a generic function which extracts model residuals from ...
An Appendix to An R Companion to Applied Regression, Second Edition John Fox & Sanford Weisberg Linear Mixed-Effects Models Using R(一本教材,进阶选用) A Step-by-Step Approach Andrzej Ga?ecki ? Tomasz Burzykowski 3.R中的线性混合模型介绍(简单了解不同的包) ...
拿常见的 lm,glm 模型来说,y ˜model 是一种特定的格式,表示以 y 为响应变量,模型为 model。 其中model 中的变量由 + 来连接,或者由: 来表示变量间的 “交互作用”。除了 + 和 : ,我们使用 ∗ 来表示 ′a+ b+ a : b′。(a+ b+ c)∧2 表示 (a+ b+ c) ∗ (a+ b+ c),即主因素...