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Asked: 2026-06-11T06:54:07+00:00

My data look like this Study NDF ADF CP Eeff 1 35.8 24.4 18.6

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My data look like this

Study   NDF ADF CP  Eeff
1   35.8    24.4    18.6    34.83181476
1   35.8    24.4    18.6    33.76824264
1   35.8    24.4    18.6    32.67390287
1   35.8    24.4    18.6    33.05520666
2   39.7    23.4    16.1    33.19730252
2   39.4    22.9    16.3    34.04709188
3   28.9    20.6    18.7    33.22501606
3   27.1    18.9    17.9    33.80766289

Of course, I have 80 lines like this.
I used lme function to run a mixed model (Study as random effect), as following:

fm1<-lme(Eeff~NDF+ADF+CP,random=~1|Study, data=na.omit(phuong))

I got this result:

Fixed effects: Ratio ~ ADF + CP + FCM + DMI + DIM 
                 Value  Std.Error  DF   t-value p-value
(Intercept)  3.1199808 0.16237303 158 19.214896  0.0000
ADF         -0.0265626 0.00406990 158 -6.526603  0.0000
CP          -0.0534021 0.00539108 158 -9.905636  0.0000
FCM         -0.0149314 0.00353524 158 -4.223598  0.0000
DMI          0.0072318 0.00498779 158  1.449894  0.1491
DIM         -0.0008994 0.00019408 158 -4.634076  0.0000
 Correlation: 
    (Intr) ADF    CP     FCM    DMI   
ADF -0.628                            
CP  -0.515  0.089                     
FCM -0.299  0.269 -0.203              
DMI -0.229 -0.145  0.083 -0.624       
DIM -0.113  0.127 -0.061  0.010 -0.047

These results show the case where intercept is random but slope is fixed. How can I see my 80 intercept, for example, like below when I used study as fixed effect:

Coefficients:
                            Estimate      Std. Error t value Pr(>|t|)    
(Intercept)        -0.0021083  0.0102536  -0.206 0.837351    
ADF                      0.0005248  0.0002962   1.772 0.078313 .  
CP                        0.0021131  0.0003277   6.448 1.26e-09 ***
factor(Study)2   0.0057274  0.0038709   1.480 0.140933    
factor(Study)3   0.0117722  0.0035262   3.338 0.001046 ** 
factor(Study)4   0.0091049  0.0043227   2.106 0.036730 *  
factor(Study)6   0.0149733  0.0045345   3.302 0.001182 ** 
factor(Study)7   0.0065518  0.0036837   1.779 0.077196 .  
factor(Study)8   0.0066134  0.0035371   1.870 0.063337 .  
factor(Study)9   0.0086758  0.0036641   2.368 0.019083 *  
factor(Study)10  0.0105657  0.0041296   2.559 0.011434 *  
factor(Study)11  0.0083694  0.0040194   2.082 0.038900 *  
factor(Study)16  0.0171258  0.0028962   5.913 1.95e-08 ***
factor(Study)18  0.0019277  0.0042300   0.456 0.649209    
factor(Study)20  0.0172469  0.0040412   4.268 3.36e-05 ***
factor(Study)23  0.0132676  0.0031658   4.191 4.57e-05 ***
factor(Study)24  0.0063313  0.0031519   2.009 0.046236 *  
factor(Study)25  0.0050929  0.0039135   1.301 0.194989

Thank you very much,
Phuong

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1 Answer

  1. Editorial Team

    You didn’t give us a reproducible question, but the answer is to use coef, for example:

    > library(nlme)
> fm1 <- lme(distance~age,random=~1|Subject,data=Orthodont)
> coef(fm1)
    (Intercept)       age
M16    15.84314 0.6601852
M05    15.84314 0.6601852
M02    16.17959 0.6601852
M11    16.40389 0.6601852
M07    16.51604 0.6601852
M08    16.62819 0.6601852
M03    16.96464 0.6601852
[snip]
    • use fixef() to get just the fixed effect coefficients
    • use ranef() to get just the random effects (i.e. deviations of each individual from the fixed coefficients
    • the Orthodont example in lme actually uses a random-slope(+intercept) model; here I have fitted a random-intercept model, so the estimated slope (age parameter) is the same for every individual
    • it looks like individuals are sorted in increasing order of estimated random effect
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