小编MYa*_*208的帖子

我想复制并通过一些特殊格式的RStudio版本0.99.834中的一些代码.见下文MWE.

ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)


lm.D9 <- 
        lm(
          formula=weight ~ group
          )
lm.D90 <- 
          lm(
            formula =weight ~ group - 1
            ) # omitting intercept

现在,如果我复制以下代码

lm.D9 <- 
        lm(
          formula=weight ~ group
          )
lm.D90 <- 
          lm(
            formula =weight ~ group - 1
            ) # omitting intercept

进入RStudio Version 0.99.834它给出以下输出

   lm.D9 <- 
      lm(
        formula=weight ~ group
      )
    lm.D90 <- 
      lm(
        formula =weight ~ group - …

这是中提到broom的网站，它可以被用于TukeyHSD和multcomp（见这里）。但是，我无法弄清楚如何使用broomforTukeyHSD和multcomp。

请参阅下面给出的 MWE。

df1 <- data.frame(
  Rep = factor(rep(1:3, each = 4, times = 2)),
  Trt = rep(paste0("T", 1:4), times = 6),
  Loc = rep(paste0("Loc", 1:2), each = 12), 
  Y   = rnorm(24)
)

library(dplyr)
df2 <- filter(df1, Loc=="Loc1")

fm1 <- aov(Y ~ Rep + Trt , data = df2)
anova(fm1)

library(multcompView)

fm1Tukey1 <- 
  data.frame(Letter = multcompLetters(TukeyHSD(fm1)$Trt[, "p adj"])$Letters)
fm1Tukey <- data.frame(Trt = row.names(fm1Tukey1), fm1Tukey1)

fm1Means1 <- …

R 3.5.0于2018年4月25日发布.我正在尝试在Ubuntu 18.04 LTS上安装R 3.5.0.我尝试了以下代码

echo "deb http://cran.stat.ucla.edu/bin/linux/ubuntu `lsb_release -sc`/" | sudo tee --append /etc/apt/sources.list.d/cran.list
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9
sudo apt update
sudo apt install r-base

Reading package lists... Done
Building dependency tree       
Reading state information... Done
r-base is already the newest version (3.4.4-1ubuntu1).
0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.

想知道如何在Ubuntu 18.04 LTS上安装R 3.5.0.

我从R和得到不同的多项式回归系数Python.

[R

    X <- c(0,0, 10, 10, 20, 20)
    Y <- c(5, 7, 15, 17, 9, 11)
    fm1 <- lm(Y~X+I(X^2))
    summary(fm1)
    Call:
    lm(formula = Y ~ X + I(X^2))

    Residuals:
     1  2  3  4  5  6 
    -1  1 -1  1 -1  1 

    Coefficients:
                Estimate Std. Error t value Pr(>|t|)   
    (Intercept)  6.00000    1.00000   6.000  0.00927 **
    X            1.80000    0.25495   7.060  0.00584 **
    I(X^2)      -0.08000    0.01225  -6.532  0.00729 **
    ---
    Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ …

我不知道如何来模拟实验设计数据,如完全随机设计,随机区组设计,拉丁方设计等,[R .任何帮助将受到高度赞赏.谢谢

我想NAs在输出中省略与执行knitr::kable相同的输出xtable（请参阅下面的kable和xtable输出）。

fm12anova <-
  structure(list(SOV = c("Days", "Residuals"), Df2 = c(1L, 178L
  ), Sum.Sq2 = c(162702.651909532, 405251.617480465), Mean.Sq2 = c(162702.651909532, 
  2276.69448022733), F.value2 = c(71.4644205986244, NA), Pr..F.2 = c(9.89409632221545e-15, 
  NA), Sum.Sq1 = c(30031.0139578738, NA), Mean.Sq1 = c(30031.0139578738, 
  NA), NumDF1 = c(1L, NA), DenDF1 = c(17.0000010028433, NA), F.value1 = c(45.8530037127448, 
  NA), Pr..F.1 = c(3.26378804516025e-06, NA)), class = "data.frame", row.names = c(NA, 
  -2L), .Names = c("SOV", "Df2", "Sum.Sq2", "Mean.Sq2", "F.value2", 
  "Pr..F.2", "Sum.Sq1", "Mean.Sq1", "NumDF1", …

我要在以下数据框中创建两列(OPop和WPop)Data:

OState  OCounty WState  WCounty OYear
AL  Autauga AL  Baldwin 2004
AL  Barbour AL  Bibb    2001
CA  Amador  WY  Weston  1985
CA  Alameda CA  Amador  1999

的值OPop和WPop基于所述四个列(的值Pop1980,Pop1990, Pop2000 和Pop2010另一数据帧的)Data1

State   County  Pop1980 Pop1990 Pop2000 Pop2010
AL  Autauga 32259   34222   43671   54571
AL  Baldwin 78556   98280   140415  182265
AK  Aleutians East  7768    2464    2697    3141
AK  Aleutians West  NA  9478    5465    5561
CA  Alameda …

我可以使用该anova函数来获得线性模型的ANOVA表.然而,这给出了每个解释变量对响应变量的单独影响.我想知道R中是否有任何函数可以获得overall ANOVA所有解释变量对响应变量的总体影响.在此先感谢您的帮助.

MWE

set.seed(12345)
X1 <- 10*abs(rnorm(n=10, mean = 5, sd = 1))
X2 <- 20*abs(rnorm(n=10, mean = 4, sd = 2))
Y <- 50*abs(rnorm(n=10, mean = 10, sd = 4))

df <- data.frame(X1, X2, Y)

fm1 <- lm(formula=Y~X1+X2, data=df)
anova(fm1)

Analysis of Variance Table

Response: Y
          Df Sum Sq Mean Sq F value Pr(>F)
X1         1  37842   37842  0.7815  0.406
X2         1 115774  115774  2.3910  0.166
Residuals  7 338941   48420  

编辑(2017-10-23)

所需的输出是

Analysis of Variance …