I am trying to write a .csv file that appends the important information from the summary of a glmer analysis (from the package lme4).
I have been able to isolate the coefficients, AIC, and random effects , but I have not been able to isolate the scaled residuals (Min, 1Q, Median, 3Q, Max).
I have tried using $residuals, but I get a very long output, not the information shown in the summary.
> library(lme4)
> setwd("C:/Users/Arthur Scully/Dropbox/! ! ! ! PHD/Chapter 2 Lynx Bobcat BC/ResourceSelection")
> #simple vectors
>
> x <- c("a","b","b","b","b","d","b","c","c","a")
>
> y <- c(1,1,0,1,0,1,1,1,1,0)
>
>
> # Simple data frame
>
> aes.samp <- data.frame(x,y)
> aes.samp
x y
1 a 1
2 b 1
3 b 0
4 b 1
5 b 0
6 d 1
7 b 1
8 c 1
9 c 1
10 a 0
>
> # Simple glmer
>
> aes.glmer <- glmer(y~(1|x),aes.samp,family ="binomial")
boundary (singular) fit: see ?isSingular
>
> summary(aes.glmer)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: y ~ (1 | x)
Data: aes.samp
AIC BIC logLik deviance df.resid
16.2 16.8 -6.1 12.2 8
I can isolate information above by using the call summary(aes.glmer)$AIC
Scaled residuals:
Min 1Q Median 3Q Max
-1.5275 -0.9820 0.6546 0.6546 0.6546
I do not know the call to isolate the above information
Random effects:
Groups Name Variance Std.Dev.
x (Intercept) 0 0
Number of obs: 10, groups: x, 4
I can isolate this information using the ranef function
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.8473 0.6901 1.228 0.22
And I can isolate the information above using summary(aes.glmer)$coefficient
convergence code: 0
boundary (singular) fit: see ?isSingular
>
> #Pull important
> ##write call to select important output
> aes.glmer.coef <- summary(aes.glmer)$coefficient
> aes.glmer.AIC <- summary(aes.glmer)$AIC
> aes.glmer.ran <-ranef(aes.glmer)
>
> ##
> data.frame(c(aes.glmer.coef, aes.glmer.AIC, aes.glmer.ran))
X0.847297859077025 X0.690065555425105 X1.22785125618255 X0.219502810378876 AIC BIC logLik deviance df.resid X.Intercept.
a 0.8472979 0.6900656 1.227851 0.2195028 16.21729 16.82246 -6.108643 12.21729 8 0
b 0.8472979 0.6900656 1.227851 0.2195028 16.21729 16.82246 -6.108643 12.21729 8 0
c 0.8472979 0.6900656 1.227851 0.2195028 16.21729 16.82246 -6.108643 12.21729 8 0
d 0.8472979 0.6900656 1.227851 0.2195028 16.21729 16.82246 -6.108643 12.21729 8 0
If anyone knows what call I can use to isolate the "scaled residuals" I would be very greatful.
I haven't got your data, so we'll use example data from the lme4 vignette.
library(lme4)
library(lattice)
library(broom)
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial)
This is for the residuals. tidy from the broom package puts it in to a tibble, which you can then export to a csv.
x <- tidy(quantile(residuals(gm1, "pearson", scaled = TRUE)))
x
# A tibble: 5 x 2
names x
<chr> <dbl>
1 0% -2.38
2 25% -0.789
3 50% -0.203
4 75% 0.514
5 100% 2.88
Also here are some of the other bits that you might find useful, using glance from broom.
y <- glance(gm1)
y
# A tibble: 1 x 6
sigma logLik AIC BIC deviance df.residual
<dbl> <dbl> <dbl> <dbl> <dbl> <int>
1 1 -92.0 194. 204. 73.5 51
And
z <- tidy(gm1)
z
# A tibble: 5 x 6
term estimate std.error statistic p.value group
<chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 (Intercept) -1.40 0.231 -6.05 1.47e-9 fixed
2 period2 -0.992 0.303 -3.27 1.07e-3 fixed
3 period3 -1.13 0.323 -3.49 4.74e-4 fixed
4 period4 -1.58 0.422 -3.74 1.82e-4 fixed
5 sd_(Intercept).herd 0.642 NA NA NA herd