Example 2.5.1.1: generalised least-squares estimates of the fixed effects
Source:R/examples.R
Examp2.5.1.1.RdThe solution for the fixed effects of ex125 with the last level of each effect set to zero:
17.13, 7.15, 4.35 and -3.18, with the variances 1.27, 0.83, 0.70 and 1.40 of their estimates (the COVB matrix).
References
Duchateau, L., Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with Applications in Veterinary Research. International Livestock Research Institute, Nairobi.
See also
Other VetResearchLMM examples:
Examp1.3.2,
Examp2.2.1.7,
Examp2.3.2,
Examp2.4.2.2,
Examp2.4.3.1,
Examp2.5.2.1,
Examp2.5.3.1,
Examp2.5.4.1,
Examp2.6.1,
Examp3.1,
Examp3.2,
Examp3.3
Author
Muhammad Yaseen (myaseen208@gmail.com)
Examples
# SAS program of the book (Appendix B):
# proc mixed data=ex125;
# class drug dose region;
# model pcv = drug dose drug*dose / solution covb;
# random region drug*region;
# run;
fm <-
vlm_fit(
fixed = Pcv ~ Drug + dose + Drug:dose
, data = ex125
, random = ~ Region + Region:Drug
, repeated = NULL
, method = "REML"
, ddfm = "default"
, type = 3
, class = NULL
, bound = TRUE
, control = list()
)
fm$fixed
#> effect level estimate se df t p
#> <char> <char> <num> <num> <num> <num> <num>
#> 1: (Intercept) 17.133333 1.1279960 5 15.189179 2.242189e-05
#> 2: Drug BERENIL 7.150000 0.9098382 5 7.858540 5.358618e-04
#> 3: Drug samorin 0.000000 NA NA NA NA
#> 4: dose h 4.350000 0.8359459 10 5.203686 3.991648e-04
#> 5: dose l 0.000000 NA NA NA NA
#> 6: Drug:dose BERENIL h -3.183333 1.1822060 10 -2.692706 2.259450e-02
#> 7: Drug:dose BERENIL l 0.000000 NA NA NA NA
#> 8: Drug:dose samorin h 0.000000 NA NA NA NA
#> 9: Drug:dose samorin l 0.000000 NA NA NA NA
fm$fixed$se^2
#> [1] 1.2723750 0.8278056 NA 0.6988056 NA 1.3976111 NA
#> [8] NA NA