Question

概述：

我有一个名为'Tree_diameter'的因变量，和一个名为'Stand_density_index'的自变量（参见下面的数据帧1和2d）。

标准密度索引包含四个类别：

独自站立
几棵靠近其他树木的树
站在10到20棵树之内
大林分或林地

如果有人可以在这里建议哪种是正确的线性回归方法：

方法1
方法2
方法3

我将非常感激。

问题的总体目标：

使用来自完整数据库的数据（请参见下面的数据框2）和适当的统计检验的结果，以5％的显着性水平接受或拒绝以下假设。

假设：

H（0）：不同林分密度指数之间的栎类茎径无差异

从整个数据库STATE

使用的统计检验-线性回归
独立（树直径）和因变量（Stand_density_index）
根据此测试合理化您的结论

方法1-使用数据框1构建

首先，我总结了数据框架，以找到 Stand_density_index 的每个类别的 Mean_Tree_Diameter （请参见上面的类别）。

问题：

运行线性回归后，会将NA插入结果类别。

如果有人可以帮助我理解为什么我会非常感激。

        ##Reformat the vectors correctly
        ##Stand_density_index = as.factor

        Summarised_QuercusRobur1NewData$Stand_density_index<-as.factor(Summarised_QuercusRobur1NewData$Stand_density_index)

        ##Recheck the structure of the data frame
        str(Summarised_QuercusRobur1NewData

        ##Linear Regression equation

        SpeciesStemDensity<-lm(Mean_Tree_Diameter~Stand_density_index, data=Summarised_QuercusRobur1NewData)

        ##Summary Statistics
          summary(SpeciesStemDensity)

        ##Summary Statistics Results

方法2-使用数据框2构建

在这种情况下，我使用了整个数据库（请参见数据框2），并将'Stand_density_index'重新设置为一个因子，然后运行线性回归模型。

##as.factor
##Reformat stand_density_index vector into a categorical vector
        QuercusRobur1$Stand_density_index<-as.factor(QuercusRobur1$Stand_density_index)

        ##Linear Regression
        StemDensityStand<-lm(Tree_diameter~Stand_density_index, data=QuercusRobur1)

        ##Summary Statistics
        summary(StemDensityStand)

        ##Results

方法3-从数据框2构造

我使用整个数据库运行线性回归模型，但是“ Stand_density_index” 是数字。

##as numeric
    ##Reformat stand_density_index into a categorical vector
    QuercusRobur1$Stand_density_index<-as.numeric(QuercusRobur1$Stand_density_index)

    ##Linear Regression
    StemDensityStand<-lm(Tree_diameter~Stand_density_index, data=QuercusRobur1)

    ##Summary Statistics
    summary(StemDensityStand)

    ##Results

数据框1

    structure(list(Stand_density_index = structure(1:4, .Label = c("1", 
    "2", "3", "4"), class = "factor"), Species = structure(c(1L, 
    1L, 1L, 1L), .Label = "Quercus robur", class = "factor"), Obs_no = c(9L, 
    82L, 40L, 58L), Mean_Tree_Diameter = c(86.9222222222222, 121.717073170732, 
    82, 72.4275862068965), SD_Tree_Diameter = c(57.2766046867693, 
    134.510951231506, 60.202253131019, 61.1575440200358)), row.names = c(NA, 
    -4L), class = "data.frame")

数据框2

structure(list(Obs_.no = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 19L, 
20L, 21L, 22L, 23L, 24L, 25L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 
35L, 36L, 37L, 38L, 39L, 44L, 45L, 46L, 47L, 57L, 58L, 59L, 60L, 
61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 74L, 
75L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 93L, 
102L, 103L, 104L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 
120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 
131L, 135L, 136L, 137L, 138L, 143L, 144L, 145L, 146L, 147L, 148L, 
149L, 150L, 151L, 152L, 153L, 154L, 155L, 158L, 159L, 160L, 161L, 
162L, 163L, 164L, 165L, 169L, 170L, 171L, 172L, 177L, 178L, 179L, 
180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 
191L, 192L, 193L, 194L, 195L, 196L, 200L, 201L, 202L, 203L, 204L, 
205L, 206L, 207L, 208L, 210L, 212L, 214L, 215L, 216L, 217L, 218L, 
219L, 220L, 221L, 233L, 234L, 235L, 237L, 239L, 246L, 255L, 256L, 
257L, 258L, 260L, 261L, 262L, 263L, 264L, 265L, 266L, 277L, 278L, 
279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 
290L, 291L, 292L, 293L, 294L, 295L, 296L), Date_observed = structure(c(4L, 
15L, 6L, 6L, 6L, 6L, 2L, 2L, 8L, 8L, 8L, 8L, 8L, 8L, 6L, 6L, 
6L, 6L, 6L, 6L, 11L, 11L, 11L, 11L, 12L, 7L, 7L, 9L, 9L, 9L, 
9L, 5L, 5L, 5L, 5L, 14L, 14L, 14L, 14L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 6L, 6L, 5L, 5L, 9L, 9L, 9L, 9L, 3L, 3L, 3L, 3L, 4L, 4L, 
1L, 1L, 11L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 3L, 3L, 3L, 3L, 11L, 
11L, 11L, 4L, 4L, 4L, 4L, 8L, 8L, 10L, 10L, 10L, 10L, 9L, 9L, 
9L, 9L, 3L, 3L, 3L, 3L, 9L, 9L, 9L, 9L, 2L, 2L, 2L, 2L, 13L, 
13L, 13L, 13L, 8L, 8L, 8L, 8L, 10L, 10L, 10L, 10L, 3L, 3L, 3L, 
3L, 13L, 13L, 13L, 13L, 9L, 9L, 10L, 10L, 10L, 2L, 2L, 3L, 3L, 
3L, 3L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 5L, 5L, 11L, 9L, 9L, 9L, 
9L, 10L, 10L, 10L, 10L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 11L, 11L, 11L, 11L, 6L, 6L, 6L, 6L, 11L, 11L, 11L, 11L), .Label = c("10/1/18", 
"10/19/18", "10/20/18", "10/21/18", "10/22/18", "10/23/18", "10/24/18", 
"10/25/18", "10/26/18", "10/27/18", "10/28/18", "10/28/19", "10/29/18", 
"12/9/18", "8/20/18"), class = "factor"), Latitude = c(51.4175, 
52.12087, 52.0269, 52.0269, 52.0269, 52.0269, 52.947709, 52.947709, 
51.491811, 51.491811, 52.59925, 52.59925, 52.59925, 52.59925, 
51.60157, 51.60157, 52.6888, 52.6888, 52.6888, 52.6888, 50.697802, 
50.697802, 50.697802, 50.697802, 53.62417, 50.446841, 50.446841, 
53.959679, 53.959679, 53.959679, 53.959679, 51.78375, 51.78375, 
51.78375, 51.78375, 51.456965, 51.456965, 51.456965, 51.456965, 
51.3651, 51.3651, 51.3651, 51.3651, 52.01182, 52.01182, 52.01182, 
52.01182, 50.114277, 50.114277, 51.43474, 51.43474, 51.10676, 
51.10676, 51.10676, 51.10676, 50.435984, 50.435984, 50.435984, 
50.435984, 51.78666, 51.78666, 52.441088, 52.441088, 52.552344, 
49.259471, 49.259471, 49.259471, 49.259471, 50.461625, 50.461625, 
50.461625, 50.461625, 51.746642, 51.746642, 51.746642, 51.746642, 
52.2501, 52.2501, 52.2501, 52.2501, 52.423336, 52.423336, 52.423336, 
52.423336, 53.615575, 53.615575, 53.615575, 53.615575, 51.08474, 
51.08474, 51.08474, 53.19329, 53.19329, 53.19329, 53.19329, 55.96785, 
55.96785, 56.52664, 56.52664, 56.52664, 56.52664, 51.8113, 51.8113, 
51.8113, 51.8113, 52.580157, 52.580157, 52.580157, 52.580157, 
50.52008, 50.52008, 50.52008, 50.52008, 51.48417, 51.48417, 51.48417, 
51.48417, 54.58243, 54.58243, 54.58243, 54.58243, 52.58839, 52.58839, 
52.58839, 52.58839, 52.717283, 52.717283, 52.717283, 52.717283, 
50.740764, 50.740764, 50.740764, 50.740764, 52.57937, 52.57937, 
52.57937, 52.57937, 50.736531, 50.736531, 50.79926, 50.79926, 
50.79926, 53.675996, 53.675996, 48.35079, 48.35079, 48.35079, 
48.35079, 51.36445, 51.36445, 51.36445, 51.36445, 52.122402, 
52.122402, 52.122402, 52.16104, 52.16104, 55.91913, 51.6528, 
51.6528, 51.6528, 51.6528, 51.88485, 51.88485, 51.88485, 51.88485, 
52.34015, 52.34015, 52.34015, 52.026042, 52.026042, 52.026042, 
52.026042, 51.319032, 51.319032, 51.319032, 51.319032, 51.51357, 
51.51357, 51.51357, 51.51357, 53.43202, 53.43202, 53.43202, 53.43202, 
51.50823, 51.50823, 51.50823, 51.50823), Longitude = c(-0.32118, 
-0.29293, -0.7078, -0.7078, -0.7078, -0.7078, -1.435407, -1.435407, 
-3.210324, -3.210324, 1.33011, 1.33011, 1.33011, 1.33011, -3.67111, 
-3.67111, -3.30909, -3.30909, -3.30909, -3.30909, -2.11692, -2.11692, 
-2.11692, -2.11692, -2.43155, -3.706923, -3.706923, -1.061008, 
-1.061008, -1.061008, -1.061008, -0.65046, -0.65046, -0.65046, 
-0.65046, -2.624917, -2.624917, -2.624917, -2.624917, 0.70706, 
0.70706, 0.70706, 0.70706, -0.70082, -0.70082, -0.70082, -0.70082, 
-5.541128, -5.541128, 0.45981, 0.45981, -2.32071, -2.32071, -2.32071, 
-2.32071, -4.105617, -4.105617, -4.105617, -4.105617, -0.71433, 
-0.71433, -0.176158, -0.176158, -1.337177, -123.107788, -123.107788, 
-123.107788, -123.107788, 3.560973, 3.560973, 3.560973, 3.560973, 
0.486416, 0.486416, 0.486416, 0.486416, -0.8825, -0.8825, -0.8825, 
-0.8825, -1.787563, -1.787563, -1.787563, -1.787563, -2.432959, 
-2.432959, -2.432959, -2.432959, -0.73645, -0.73645, -0.73645, 
-0.63793, -0.63793, -0.63793, -0.63793, -3.18084, -3.18084, -3.40313, 
-3.40313, -3.40313, -3.40313, -0.22894, -0.22894, -0.22894, -0.22894, 
-1.948571, -1.948571, -1.948571, -1.948571, -4.20756, -4.20756, 
-4.20756, -4.20756, -0.34854, -0.34854, -0.34854, -0.34854, -5.93229, 
-5.93229, -5.93229, -5.93229, -1.96843, -1.96843, -1.96843, -1.96843, 
-2.410575, -2.410575, -2.410575, -2.410575, -2.361234, -2.361234, 
-2.361234, -2.361234, -1.89325, -1.89325, -1.89325, -1.89325, 
-2.011143, -2.011143, -3.19446, -3.19446, -3.19446, -1.272824, 
-1.272824, 10.91812, 10.91812, 10.91812, 10.91812, -0.23106, 
-0.23106, -0.23106, -0.23106, -0.487443, -0.487443, -0.487443, 
0.18702, 0.18702, -3.20987, -1.57361, -1.57361, -1.57361, -1.57361, 
-0.17844, -0.17844, -0.17844, -0.17844, -1.27795, -1.27795, -1.27795, 
-0.503114, -0.503114, -0.503114, -0.503114, -0.472994, -0.472994, 
-0.472994, -0.472994, -3.18738, -3.18738, -3.18738, -3.18738, 
-2.27968, -2.27968, -2.27968, -2.27968, -0.25847, -0.25847, -0.25847, 
-0.25847), Altitude = c(5L, 0L, 68L, 68L, 68L, 68L, 104L, 104L, 
15L, 15L, 23L, 23L, 23L, 23L, 184L, 184L, 176L, 176L, 176L, 176L, 
12L, 12L, 12L, 12L, 178L, 36L, 36L, 11L, 11L, 11L, 11L, 210L, 
210L, 210L, 210L, 97L, 97L, 97L, 97L, 23L, 23L, 23L, 23L, 0L, 
0L, 0L, 0L, 9L, 9L, 4L, 4L, 200L, 200L, 200L, 200L, 160L, 160L, 
160L, 160L, 166L, 166L, 0L, 0L, 0L, 47L, 47L, 47L, 47L, 58L, 
58L, 58L, 58L, 43L, 43L, 43L, 43L, 97L, 97L, 97L, 97L, 133L, 
133L, 133L, 133L, 123L, 123L, 123L, 123L, 128L, 128L, 128L, 15L, 
15L, 15L, 15L, 14L, 14L, 65L, 65L, 65L, 65L, 129L, 129L, 129L, 
129L, 140L, 140L, 140L, 140L, 18L, 18L, 18L, 18L, 30L, 30L, 30L, 
30L, 19L, 19L, 19L, 19L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 96L, 
96L, 96L, 96L, 169L, 169L, 169L, 169L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 43L, 43L, 43L, 75L, 75L, 
109L, 110L, 110L, 110L, 110L, 95L, 95L, 95L, 95L, 112L, 112L, 
112L, 0L, 0L, 0L, 0L, 24L, 24L, 24L, 24L, 38L, 38L, 38L, 38L, 
29L, 29L, 29L, 29L, 20L, 20L, 20L, 20L), Species = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Quercus robur", class = "factor"), 
    Tree_diameter = c(68.8, 10, 98.5, 97, 32.5, 45.1, 847, 817, 
    62, 71, 140, 111.4, 114.6, 167.1, 29, 40.1, 68, 45, 60, 54, 
    104, 122, 85, 71, 81, 39.8, 43.6, 20.1, 17.8, 15.6, 12.1, 
    81.8, 102.5, 75.5, 57.3, 0.3, 0.2, 0.3, 0.3, 70, 36, 53, 
    44, 31.5, 27.1, 23.3, 22, 69.4, 37.3, 19.9, 14.6, 196, 122, 
    118, 180, 58.6, 54.1, 58, 61.5, 58.4, 61, 134, 64, 52.2, 
    170, 114, 127, 158, 147.4, 135.3, 122.9, 104.1, 263, 237, 
    322, 302, 175, 182, 141, 155, 89, 41, 70, 83, 141, 86.5, 
    82, 114.5, 129, 127, 143, 125, 92, 68, 90, 24.5, 20.1, 63.7, 
    39.8, 66.2, 112.4, 124.5, 94.1, 68.6, 74.4, 23.6, 27.7, 22.9, 
    25.2, 24.2, 54.7, 43, 33.1, 306, 274, 56, 60, 72.5, 128.5, 
    22, 16, 143, 103, 53, 130, 48.4, 69.8, 6.4, 18.6, 129.2, 
    41.7, 57.6, 14, 41.7, 30.2, 39.5, 24.2, 320, 352, 120.9, 
    108.3, 53.2, 274, 85, 52, 43, 38, 37, 219, 215, 216, 175, 
    85.9, 49.7, 97.1, 40.8, 62.4, 80.3, 43, 50.3, 28.7, 31.9, 
    181.5, 149.7, 122, 143.6, 148, 145, 99, 28, 32, 54, 54, 169, 
    152, 160, 138, 90.8, 87.9, 77.4, 81.2, 91.7, 62.7, 50, 72.9, 
    23.7, 58, 80.7, 73.7), Urbanisation_index = c(2L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 4L, 4L, 
    4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
    4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 
    4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 
    4L, 2L, 2L, 2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 4L, 4L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 4L, 4L, 
    4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 1L), Stand_density_index = c(3, 1, 2, 
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 2, 2, 
    2, 2, 4, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 
    2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 4, 4, 3, 3, 3, 3, 4, 
    3, 4, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
    2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 2, 2, 2, 2, 2, 3, 4, 
    4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 1, 4, 4, 4, 4, 2, 2, 2, 2, 
    2, 2, 3, 3, 2, 2, 2, 2, 3, 3, 3, 2, 4, 4, 4, 4, 4, 4, 4, 
    4, 4, 4, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 
    4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 2, 1, 1, 1, 4, 4, 4, 4, 3, 
    3, 3, 3, 4, 4, 4, 2, 3, 3, 3, 3, 2, 2, 2, 2), Canopy_Index = c(85L, 
    85L, 85L, 75L, 45L, 25L, 75L, 65L, 75L, 75L, 95L, 95L, 95L, 
    95L, 95L, 65L, 85L, 65L, 95L, 85L, 85L, 85L, 75L, 75L, 65L, 
    85L, 85L, 75L, 75L, 85L, 65L, 95L, 85L, 95L, 95L, 75L, 75L, 
    85L, 85L, 85L, 85L, 85L, 75L, 85L, 85L, 85L, 85L, 75L, 75L, 
    85L, 85L, 65L, 75L, 85L, 75L, 95L, 95L, 95L, 95L, 75L, 65L, 
    95L, 95L, 55L, 75L, 65L, 75L, 65L, 85L, 95L, 95L, 75L, 95L, 
    75L, 95L, 65L, 75L, 75L, 85L, 85L, 65L, 95L, 65L, 65L, 65L, 
    65L, 65L, 65L, 85L, 85L, 75L, 95L, 85L, 85L, 75L, 45L, 55L, 
    35L, 35L, 25L, 25L, 95L, 85L, 75L, 85L, 85L, 75L, 75L, 65L, 
    75L, 85L, 65L, 45L, 95L, 95L, 95L, 95L, 65L, 75L, 45L, 35L, 
    75L, 95L, 95L, 85L, 75L, 65L, 85L, 95L, 75L, 85L, 85L, 95L, 
    65L, 65L, 45L, 65L, 85L, 35L, 95L, 85L, 85L, 85L, 85L, 75L, 
    65L, 65L, 65L, 65L, 55L, 75L, 85L, 85L, 95L, 85L, 75L, 75L, 
    85L, 65L, 45L, 75L, 75L, 65L, 65L, 75L, 65L, 95L, 95L, 95L, 
    85L, 65L, 75L, 75L, 75L, 65L, 75L, 35L, 75L, 75L, 75L, 75L, 
    25L, 45L, 45L, 35L, 85L, 95L, 85L, 95L), Phenological_Index = c(2L, 
    4L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
    2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
    1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    4L, 4L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 
    2L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 4L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 
    3L, 3L, 4L, 3L, 2L, 3L, 2L, 2L, 2L, 1L, 3L, 1L, 4L, 2L, 4L, 
    3L, 3L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L)), row.names = c(NA, -189L
), class = "data.frame")

Answer 1

爱丽丝！

线性回归模型的问题是您没有足够的数据来执行线性回归。

因为您有一个因变量来解释每个自变量，所以您不需要模型，只需四个方程式即可解决四个变量。

这就是为什么Mean_Tree_Diameter的截距等于Stand_density_index==1，intercept + Stand_density_index_2的{{1}}等于Mean_Tree_Diameter的原因。您的Stand_density_index==2是1。您的模型是完美的！

因此，您要么在模型中不使用Multiple R Squared，要么包含更多数据（同一Stand_density_index的多个Mean_Tree_Diameter值），否则您将始终得到此结果。

如果您使用以下数据尝试模型：

Mean_Tree_Diameter

您将获得一些结果，因为现在只有2个不同的因变量，您有4个不同的自变量结果。

线性回归-为自变量的每个类别插入不适用

1 个答案: