[R-sig-ME] Model approach for pairwise comparison post-hoc

[Lenth, Russell V]

Just use

    lsm.both <- lsmeans(mTCFd, c("temp", "generation"))
    cld(lsm.both)

-----Original Message-----

Message: 1
Date: Wed, 27 Jul 2022 12:40:32 +0000 (UTC)
From: Alexandre Santos <alexandresantosbr using yahoo.com.br>
To: "r-sig-mixed-models using r-project.org"
	<r-sig-mixed-models using r-project.org>
Subject: [R-sig-ME] Model approach for pairwise comparison post-hoc
Message-ID: <1309771597.2820689.1658925632725 using mail.yahoo.com>
Content-Type: text/plain; charset="utf-8"

In my example: 


    # Packages
    library(glmmTMB)
    library(multcomp)
    library(lsmeans)
    library(car)
    
    # My data set
    ds <- read.csv("https://raw.githubusercontent.com/Leprechault/trash/main/temp_ger_ds.csv")
    str(ds)
    #'data.frame':  140 obs. of  4 variables:
    # $ temp       : chr  "constante" "constante" "constante" "constante" ...
    # $ generation : chr  "G0" "G0" "G0" "G0" ...
    # $ development: int  22 24 22 27 27 24 25 26 27 18 ...


First fit the ziGamma model:


    mTCFd <- glmmTMB(development ~ temp * generation + (1 | generation), data = ds,
                   family = ziGamma(link = "log"))
    Anova(mTCFd,test="Chi")
# Analysis of Deviance Table (Type II Wald chisquare tests)


# Response: development
#                   Chisq Df Pr(>Chisq)    
# temp            198.412  1  < 2.2e-16 ***
# generation       18.346  4   0.001056 ** 
# temp:generation  31.250  4  2.723e-06 ***
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Pairwise Comparison Post Hoc Tests:


    1) For temp:
    lsm.TCFd.temp <- lsmeans(mTCFd, c("temp"))
    cld(lsm.TCFd.temp, Letters=letters)
#  temp      lsmean     SE  df lower.CL upper.CL .group
#  constante   3.18 0.0082 128     3.17     3.20  a    
#  flutuante   3.37 0.0131 128     3.34     3.39   b   
    
    2) For generation:
    lsm.TCFd.gen <- lsmeans(mTCFd, c("generation"))
    cld(lsm.TCFd.gen, Letters=letters)
#  generation lsmean     SE  df lower.CL upper.CL .group
#  G3           3.23 0.0159 128     3.20     3.26  a    
#  G1           3.27 0.0198 128     3.23     3.31  ab   
#  G0           3.27 0.0135 128     3.25     3.30  ab   
#  G4           3.29 0.0217 128     3.25     3.34  ab   
#  G2           3.31 0.0141 128     3.28     3.34   b   
    3) For temp:generation interaction:
    ds$temp_gen <- paste0(ds$temp,"_",ds$generation)
    mTCFd.int <- glmmTMB(development ~ temp_gen + (1 | generation), data = ds,
                   family = ziGamma(link = "log")) 
    lsm.TCFd.temp.gen <- lsmeans(mTCFd.int, c("temp_gen"))
    cld(lsm.TCFd.temp.gen, Letters=letters)
#  temp_gen     lsmean     SE  df lower.CL upper.CL .group 
#  constante_G3   3.13 0.0180 128     3.09     3.16  a     
#  constante_G2   3.14 0.0180 128     3.11     3.18  ab    
#  constante_G0   3.19 0.0191 128     3.15     3.23  abc   
#  constante_G1   3.22 0.0180 128     3.18     3.25   bc   
#  constante_G4   3.23 0.0185 128     3.19     3.27    cd  
#  flutuante_G1   3.32 0.0352 128     3.25     3.39    cde 
#  flutuante_G3   3.34 0.0262 128     3.28     3.39      e 
#  flutuante_G0   3.36 0.0191 128     3.32     3.39      e 
#  flutuante_G4   3.36 0.0393 128     3.28     3.44     def
#  flutuante_G2   3.47 0.0218 128     3.43     3.52       f


Ok, it works, but I'd like to know if is possible for the pairwise comparison
directly with the final model (`mTCFd`) without a new interaction model adjustment (`mTCFd.int`).
This is because different models have different parameters and I'm interesting in the `mTCFd` adjustment.


Please, any help with it?