# SE for all fixed factor effect in GLMM

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## SE for all fixed factor effect in GLMM

 Dear members, Let do a example of simple GLMM with x and G as fixed factors and R as random factor: (note that question is the same with GLM or even LM): x <- rnorm(100) y <- rnorm(100) G <- as.factor(sample(c("A", "B", "C", "D"), 100, replace = TRUE)) R <- as.factor(rep(1:25, 4)) library(lme4) m <- lmer(y ~ x + G + (1 | R)) summary(m)\$coefficients I get the fixed effect fit and their SE  > summary(m)\$coefficients                 Estimate Std. Error    t value (Intercept)  0.07264454  0.1952380  0.3720820 x           -0.02519892  0.1238621 -0.2034433 GB           0.10969225  0.3118371  0.3517614 GC          -0.09771555  0.2705523 -0.3611706 GD          -0.12944760  0.2740012 -0.4724344 The estimate for GA is not shown as it is fixed to 0. Normal, it is the reference level. But is there a way to get SE for GA of is-it non-sense question because GA is fixed to 0 ? ______________ I propose here a solution but I don't know if it is correct. It is based on reordering levels and averaging se for all reordering: G <- relevel(G, "A") m <- lmer(y ~ x + G + (1 | R)) sA <- summary(m)\$coefficients G <- relevel(G, "B") m <- lmer(y ~ x + G + (1 | R)) sB <- summary(m)\$coefficients G <- relevel(G, "C") m <- lmer(y ~ x + G + (1 | R)) sC <- summary(m)\$coefficients G <- relevel(G, "D") m <- lmer(y ~ x + G + (1 | R)) sD <- summary(m)\$coefficients seA <- mean(sB["GA", "Std. Error"], sC["GA", "Std. Error"], sD["GA", "Std. Error"]) seB <- mean(sA["GB", "Std. Error"], sC["GB", "Std. Error"], sD["GB", "Std. Error"]) seC <- mean(sA["GC", "Std. Error"], sB["GC", "Std. Error"], sD["GC", "Std. Error"]) seD <- mean(sA["GD", "Std. Error"], sB["GD", "Std. Error"], sC["GD", "Std. Error"]) seA; seB; seC; seD Thanks, Marc ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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## Re: [FORGED] SE for all fixed factor effect in GLMM

 On 12/30/18 5:31 PM, Marc Girondot via R-help wrote: > Dear members, > > Let do a example of simple GLMM with x and G as fixed factors and R as > random factor: > > (note that question is the same with GLM or even LM): > > x <- rnorm(100) > y <- rnorm(100) > G <- as.factor(sample(c("A", "B", "C", "D"), 100, replace = TRUE)) > R <- as.factor(rep(1:25, 4)) > > library(lme4) > > m <- lmer(y ~ x + G + (1 | R)) > summary(m)\$coefficients > > I get the fixed effect fit and their SE > >  > summary(m)\$coefficients >                 Estimate Std. Error    t value > (Intercept)  0.07264454  0.1952380  0.3720820 > x           -0.02519892  0.1238621 -0.2034433 > GB           0.10969225  0.3118371  0.3517614 > GC          -0.09771555  0.2705523 -0.3611706 > GD          -0.12944760  0.2740012 -0.4724344 > > The estimate for GA is not shown as it is fixed to 0. Normal, it is the > reference level. > > But is there a way to get SE for GA of is-it non-sense question because > GA is fixed to 0 ? In a way, yes it's a nonsense question, as you say. If you really want an SE for GA then re-parametrise so that GA is meaningful: m2 <- lmer(y ~ x + 0 + G + (1 | R)) Note that with this formulation GA will be there, "(Intercept)" will disappear, and GB, GC and GD will now mean something different. GA from m2 = (Intercept) from m GB from m2 = (Intercept) + GB from m GC from m2 = (Intercept) + GC from m GD from m2 = (Intercept) + GD from m I haven't followed what you've done below, but I think that you are making things unnecessarily complicated and life difficult for yourself. cheers, Rolf -- Technical Editor ANZJS Department of Statistics University of Auckland Phone: +64-9-373-7599 ext. 88276 > > ______________ > > I propose here a solution but I don't know if it is correct. It is based > on reordering levels and averaging se for all reordering: > > G <- relevel(G, "A") > m <- lmer(y ~ x + G + (1 | R)) > sA <- summary(m)\$coefficients > > G <- relevel(G, "B") > m <- lmer(y ~ x + G + (1 | R)) > sB <- summary(m)\$coefficients > > G <- relevel(G, "C") > m <- lmer(y ~ x + G + (1 | R)) > sC <- summary(m)\$coefficients > > G <- relevel(G, "D") > m <- lmer(y ~ x + G + (1 | R)) > sD <- summary(m)\$coefficients > > seA <- mean(sB["GA", "Std. Error"], sC["GA", "Std. Error"], sD["GA", > "Std. Error"]) > seB <- mean(sA["GB", "Std. Error"], sC["GB", "Std. Error"], sD["GB", > "Std. Error"]) > seC <- mean(sA["GC", "Std. Error"], sB["GC", "Std. Error"], sD["GC", > "Std. Error"]) > seD <- mean(sA["GD", "Std. Error"], sB["GD", "Std. Error"], sC["GD", > "Std. Error"]) > > seA; seB; seC; seD > > > Thanks, > > Marc ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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## Re: SE for all fixed factor effect in GLMM

 In reply to this post by R help mailing list-2 maybe qvcalc https://cran.r-project.org/web/packages/qvcalc/index.html  is useful for you. Marc Girondot via R-help wrote/hat geschrieben on/am 30.12.2018 05:31: > Dear members, > > Let do a example of simple GLMM with x and G as fixed factors and R as > random factor: > > (note that question is the same with GLM or even LM): > > x <- rnorm(100) > y <- rnorm(100) > G <- as.factor(sample(c("A", "B", "C", "D"), 100, replace = TRUE)) > R <- as.factor(rep(1:25, 4)) > > library(lme4) > > m <- lmer(y ~ x + G + (1 | R)) > summary(m)\$coefficients > > I get the fixed effect fit and their SE > >> summary(m)\$coefficients >                Estimate Std. Error    t value > (Intercept)  0.07264454  0.1952380  0.3720820 > x           -0.02519892  0.1238621 -0.2034433 > GB           0.10969225  0.3118371  0.3517614 > GC          -0.09771555  0.2705523 -0.3611706 > GD          -0.12944760  0.2740012 -0.4724344 > > The estimate for GA is not shown as it is fixed to 0. Normal, it is the > reference level. > > But is there a way to get SE for GA of is-it non-sense question because > GA is fixed to 0 ? > > ______________ > > I propose here a solution but I don't know if it is correct. It is based > on reordering levels and averaging se for all reordering: > > G <- relevel(G, "A") > m <- lmer(y ~ x + G + (1 | R)) > sA <- summary(m)\$coefficients > > G <- relevel(G, "B") > m <- lmer(y ~ x + G + (1 | R)) > sB <- summary(m)\$coefficients > > G <- relevel(G, "C") > m <- lmer(y ~ x + G + (1 | R)) > sC <- summary(m)\$coefficients > > G <- relevel(G, "D") > m <- lmer(y ~ x + G + (1 | R)) > sD <- summary(m)\$coefficients > > seA <- mean(sB["GA", "Std. Error"], sC["GA", "Std. Error"], sD["GA", > "Std. Error"]) > seB <- mean(sA["GB", "Std. Error"], sC["GB", "Std. Error"], sD["GB", > "Std. Error"]) > seC <- mean(sA["GC", "Std. Error"], sB["GC", "Std. Error"], sD["GC", > "Std. Error"]) > seD <- mean(sA["GD", "Std. Error"], sB["GD", "Std. Error"], sC["GD", > "Std. Error"]) > > seA; seB; seC; seD > > > Thanks, > > Marc > > ______________________________________________ > [hidden email] mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html> and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.