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
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
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 > > ______________________________________________ > R-help at r-project.org 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. >
On 1/2/19 9:35 AM, Marc Girondot wrote:> Hello members of the list, > > I asked 3 days ago a question about "how to get the SE of all effects > after a glm or glmm". I post here a synthesis of the answer and a new > solution: > > For example: > > x <- rnorm(100) > > y <- rnorm(100) > > G <- as.factor(sample(c("A", "B", "C", "D"), 100, replace = TRUE)); G <- > relevel(G, "A") > > > m <- glm(y ~ x + G) > > summary(m)$coefficients > > > No SE for A level in G category is calculated. > > > * Here is a synthesis of the answers: > > > 1/ The first solution was proposed by Rolf Turner > <r.turner at auckland.ac.nz>. It was to add a + 0 in the formula and then > it is possible to have the SE for the 4 levels (it works also with > objects obtained with lme4:lmer() ): > > m1 <- glm(y ~ x + G +0) > > summary(m1)$coefficients > > > However, this solution using + 0 does not works if more than one > category is included. Only the levels of the first one have all the SE > estimated.Well, you only asked about the setting in which there was only one categorical predictor. If there are, e.g. two (say "G" and "H") try m2 <- glm(y ~ x + G:H + 0) I would suggest that you learn a bit about how the formula structure works in linear models. cheers, Rolf Turner P.S. Your use of relevel() is redundant/irrelevant in this context. R. T. -- Honorary Research Fellow Department of Statistics University of Auckland Phone: +64-9-373-7599 ext. 88276
Please keep communications on-list. On 1/2/19 10:57 AM, Marc Girondot wrote:> Le 01/01/2019 ? 22:35, Rolf Turner a ?crit?: >> On 1/2/19 9:35 AM, Marc Girondot wrote: >>> Hello members of the list, >>> >>> I asked 3 days ago a question about "how to get the SE of all effects >>> after a glm or glmm". I post here a synthesis of the answer and a new >>> solution: >>> >>> For example: >>> >>> x <- rnorm(100) >>> >>> y <- rnorm(100) >>> >>> G <- as.factor(sample(c("A", "B", "C", "D"), 100, replace = TRUE)); G >>> <- relevel(G, "A") >>> >>> >>> m <- glm(y ~ x + G) >>> >>> summary(m)$coefficients >>> >>> >>> No SE for A level in G category is calculated. >>> >>> >>> * Here is a synthesis of the answers: >>> >>> >>> 1/ The first solution was proposed by Rolf Turner >>> <r.turner at auckland.ac.nz>. It was to add a + 0 in the formula and >>> then it is possible to have the SE for the 4 levels (it works also >>> with objects obtained with lme4:lmer() ): >>> >>> m1 <- glm(y ~ x + G +0) >>> >>> summary(m1)$coefficients >>> >>> >>> However, this solution using + 0 does not works if more than one >>> category is included. Only the levels of the first one have all the >>> SE estimated. >> >> Well, you only asked about the setting in which there was only one >> categorical predictor.? If there are, e.g. two (say "G" and "H") try >> >> m2 <- glm(y ~ x + G:H + 0) >> >> I would suggest that you learn a bit about how the formula structure >> works in linear models. >> >> cheers, >> >> Rolf Turner >> >> P.S.? Your use of relevel() is redundant/irrelevant in this context. >> >> R. T. >> > Thanks for the advises. But based on my little knowledge of formula > structure in linear models, A+B is not the same than A:B.That is very true! But I never suggested using "A+B". In the context of an additive model there is *NO WAY* to make sense of parameters corresponding to each level of each factor. Consequently there can be no way to form estimates of such parameters or of the standard errors of such estimates. They cannot be made meaningful. (This is, in effect, the reason for the existence of the --- rather confusing --- over-parametrised model.)> The first structure used 6 parameters and the second one 14.Well, it depends on how many levels each of A and B has! But yes, the numbers of parameters will be different. They are different models.> Then adding > +0 does not solve the problem... or perhaps I am wrong ? > > Thanks for your time.For an *additive* model "+0" does indeed not solve the problem. In this context the "problem" has no solution. You might get some insight by reading about "the cell means model" in "Linear Models" by Shayle R. Searle: @book{searle1997, title={Linear Models}, author={Searle, S.R.}, isbn={9780471184997}, year={1997}, publisher={Wiley} } If you use the model I suggested (i.e. G:H + 0) you get an explicit estimate for each cell mean, and the standard errors of these estimates. cheers, Rolf Turner -- Honorary Research Fellow Department of Statistics University of Auckland Phone: +64-9-373-7599 ext. 88276