>我有一个看起来像
"","OBSERV","DIOX","logDIOX","OXYGEN","LOAD","PRSEK","PLANT","TIME","LAB"
"1",1011,984.06650389,6.89169348002254,"L","H","L","RENO_N","1","KK"
"2",1022,1790.7973641,7.49041625445373,"H","H","L","RENO_N","1","USA"
"3",1031,661.95870145,6.4952031694744,"L","H","H","RENO_N","1","USA"
"4",1042,978.06853583,6.88557974511529,"H","H","H","RENO_N","1","KK"
"5",1051,270.92290942,5.60183431332639,"N","N","N","RENO_N","1","USA"
"6",1062,402.98269729,5.99889362626069,"N","N","N","RENO_N","1","USA"
"7",1071,321.71945701,5.77367991426247,"H","L","L","RENO_N","1","KK"
"8",1082,223.15260359,5.40785585845064,"L","L","L","RENO_N","1","USA"
"9",1091,246.65350151,5.507984523849,"H","L","H","RENO_N","1","USA"
"10",1102,188.48323034,5.23900903921703,"L","L","H","RENO_N","1","KK"
"11",1141,267.34994025,5.58855843790491,"N","N","N","RENO_N","1","KK"
"12",1152,452.10355987,6.11391126834609,"N","N","N","RENO_N","1","KK"
"13",2011,2569.6672555,7.85153169693888,"N","N","N","KARA","1","USA"
"14",2021,604.79620572,6.40489155123453,"N","N","N","KARA","1","KK"
"15",2031,2610.4804449,7.86728956188212,"L","H",NA,"KARA","1","KK"
"16",2032,3789.7097503,8.24004471210954,"L","H",NA,"KARA","1","USA"
"17",2052,338.97054188,5.82591320649553,"L","L","L","KARA","1","KK"
"18",2061,391.09027375,5.96893841249289,"H","L","H","KARA","1","USA"
"19",2092,410.04420258,6.01626496505788,"N","N","N","KARA","1","USA"
"20",2102,313.51882368,5.74785940190679,"N","N","N","KARA","1","KK"
"21",2112,1242.5931417,7.12495571830002,"H","H","H","KARA","1","KK"
"22",2122,1751.4827969,7.46821802066524,"H","H","L","KARA","1","USA"
"23",3011,60.48026048,4.10231703874031,"N","N","N","RENO_S","1","KK"
"24",3012,257.27729731,5.55015448107691,"N","N","N","RENO_S","1","USA"
"25",3021,46.74282552,3.84466077914493,"N","N","N","RENO_S","1","KK"
"26",3022,73.605375516,4.29871805996994,"N","N","N","RENO_S","1","KK"
"27",3031,108.25433812,4.68448344109116,"H","H","L","RENO_S","1","KK"
"28",3032,124.40704234,4.82355878915293,"H","H","L","RENO_S","1","USA"
"29",3042,123.66859296,4.81760535031397,"L","H","L","RENO_S","1","KK"
"30",3051,170.05332632,5.13611207209694,"N","N","N","RENO_S","1","USA"
"31",3052,95.868704018,4.56297958887925,"N","N","N","RENO_S","1","KK"
"32",3061,202.69261215,5.31169060558111,"N","N","N","RENO_S","1","USA"
"33",3062,70.686307069,4.25825187761015,"N","N","N","RENO_S","1","USA"
"34",3071,52.034715526,3.95191110210073,"L","H","H","RENO_S","1","KK"
"35",3072,93.33525462,4.53619789950355,"L","H","H","RENO_S","1","USA"
"36",3081,121.47464906,4.79970559129829,"H","H","H","RENO_S","1","USA"
"37",3082,94.833869239,4.55212661590867,"H","H","H","RENO_S","1","KK"
"38",3091,68.624596439,4.22865101914209,"H","L","L","RENO_S","1","USA"
"39",3092,64.837097371,4.17187792984139,"H","L","L","RENO_S","1","KK"
"40",3101,32.351569811,3.47666254561192,"L","L","L","RENO_S","1","KK"
"41",3102,29.285124102,3.37707967726539,"L","L","L","RENO_S","1","USA"
"42",3111,31.36974463,3.44584388158928,"L","L","H","RENO_S","1","USA"
"43",3112,28.127853881,3.33676032670116,"L","L","H","RENO_S","1","KK"
"44",3121,91.825330102,4.51988818660262,"H","L","H","RENO_S","1","KK"
"45",3122,136.4559307,4.91600171048243,"H","L","H","RENO_S","1","USA"
"46",4011,126.11889968,4.83722511024933,"H","L","H","RENO_N","2","KK"
"47",4022,76.520259821,4.33755554003153,"L","L","L","RENO_N","2","KK"
"48",4032,93.551979795,4.53851721545715,"L","L","H","RENO_N","2","USA"
"49",4041,207.09703422,5.33318744777751,"H","L","L","RENO_N","2","USA"
"50",4052,383.44185307,5.94918798759058,"N","N","N","RENO_N","2","USA"
"51",4061,156.79345897,5.05492939129363,"N","N","N","RENO_N","2","USA"
"52",4071,322.72413197,5.77679787769979,"L","H","L","RENO_N","2","USA"
"53",4082,554.05710342,6.31726775620079,"H","H","H","RENO_N","2","USA"
"54",4091,122.55552697,4.80856420867156,"N","N","N","RENO_N","2","KK"
"55",4102,112.70050456,4.72473389805434,"N","N","N","RENO_N","2","KK"
"56",4111,94.245481423,4.54590288271731,"L","H","H","RENO_N","2","KK"
"57",4122,323.16498582,5.77816298482521,"H","H","L","RENO_N","2","KK"
我在R中使用lm定义线性模型
lm1 <- lm(logDIOX ~ 1 + OXYGEN + LOAD + PLANT + TIME + LAB, data=data)
我想解释估计的系数。但是，当我提取系数时，我得到了多个"NA"(我假设这是由于变量之间的线性依赖关系(。那么我该如何解释系数呢？我只有一个截距，它以某种方式表示模型中每个包含因子的水平之一。是否可以获得每个因子水平的估计值？
> summary(lm1)
Coefficients:
Call:
lm(formula = logDIOX ~ OXYGEN + LOAD + PLANT + TIME + LAB, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.90821 -0.32102 -0.08993 0.27311 0.97758
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.2983 0.2110 34.596 < 2e-16 ***
OXYGENL -0.4086 0.1669 -2.449 0.017953 *
OXYGENN -0.7567 0.1802 -4.199 0.000113 ***
LOADL -1.0645 0.1675 -6.357 6.58e-08 ***
LOADN NA NA NA NA
PLANTRENO_N -0.6636 0.2174 -3.052 0.003664 **
PLANTRENO_S -2.3452 0.1929 -12.158 < 2e-16 ***
TIME2 -0.9160 0.2065 -4.436 5.18e-05 ***
LABUSA 0.3829 0.1344 2.849 0.006392 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5058 on 49 degrees of freedom
Multiple R-squared: 0.8391, Adjusted R-squared: 0.8161
F-statistic: 36.5 on 7 and 49 DF, p-value: < 2.2e-16
对于您问题的NA部分，您可以在此处查看：
[线性回归"NA"估计仅针对最后一个系数，实际上您的变量可以描述为其余变量的线性组合。
对于因子及其水平，r 的工作方式是显示与第一因子水平的截距，并显示截距与其他因子的差异。我认为仅使用一个因素回归会更清楚：
lm1 <- lm(logDIOX ~ 1 + OXYGEN , data=df)
> summary(lm1)
Call:
lm(formula = logDIOX ~ 1 + OXYGEN, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.7803 -0.7833 -0.2027 0.6597 3.1229
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.5359 0.2726 20.305 <2e-16 ***
OXYGENL -0.4188 0.3909 -1.071 0.289
OXYGENN -0.1896 0.3807 -0.498 0.621
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.188 on 54 degrees of freedom
Multiple R-squared: 0.02085, Adjusted R-squared: -0.01542
F-statistic: 0.5749 on 2 and 54 DF, p-value: 0.5662
这个结果说的是，对于 OXYGEN="H"截距为 5.5359，OXYGEN="L"截距为 5.5359-0.4188=5.1171，OXYGEN="N"截距为 5.5359-0.1896= 5.3463。
希望这有帮助
更新：
根据您的评论，我概括为您的模型。
什么时候
OXYGEN = "H"
LOAD = "H"
PLANT= "KARRA"
TIME=1
LAB="KK"
然后：
logDIOX =7.2983
什么时候
OXYGEN = "L"
LOAD = "H"
PLANT= "KARRA"
TIME=1
LAB="KK"
然后：
logDIOX =7.2983-0.4086 =6.8897
什么时候
OXYGEN = "L"
LOAD = "L"
PLANT= "KARRA"
TIME=1
LAB="KK"
然后：
logDIOX =7.2983-0.4086-1.0645 =5.8252
等。