I'm using GLM models to explain which effects are the most relevant to explain the occurrence of certain wildlife behaviours.
I'm using package effects(), which provides very useful plots. Below is a sample of code of one of the models tested:
> m7 <- glmer(cbind(Feeding,Standing_Foraging) ~ Day_Night+(1|ID) , data=GLM_df , family=binomial)
> summary(m7)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: cbind(Feeding, Standing_Foraging) ~ Day_Night + (1 | ID)
Data: GLM_df
AIC BIC logLik deviance df.resid
665.3 674.2 -329.6 659.3 141
Scaled residuals:
Min 1Q Median 3Q Max
-2.09648 -0.60859 -0.02129 0.56377 3.07963
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.1461 0.3822
Number of obs: 144, groups: ID, 6
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.13918 0.16406 -6.944 0.00000000000382 ***
Day_NightNight -0.15369 0.07525 -2.042 0.0411 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
Dy_NghtNght -0.206
And here are the plotted estimates with confidence intervals:
plot(allEffects(m7))
My question is simple: How do I access the estimate values and respective confidence intervals plotted in the figure? I can't find any information on the forum so please, any help is appreciated!
Cheers!
values <- effect("x", model)
summary(values)
x is the explanatory value you want information about
model is the name of your glmer() model.
Related
Our data set consists of 3 periods of time measuring how often monkeys were in different hights in the tree.
After using a generalized linear mixed model on our data set we want to perform a posthoc test. We want to test if the monkeys are more often in higher areas in the different periods. We want to use the TukeyHSD() to do the tukey post hoc test, but we get an error :
Error in UseMethod("TukeyHSD") :
no applicable method for 'TukeyHSD' applied to an object of class "c('glmerMod', 'merMod')".
Also I can't install lsmeans or emmeans because it is not possible with my version of R (while I just updated R). Does anybody know how to solve this problem?
To do the glmm we used:
output2 <- glmer(StrataNumber ~ ffactor1 + ( 1 | Focal), data = aa, family = "poisson", na.action = "na.fail")
dredge(output2)
dredgeout2 <- dredge(output2)
subset(dredgeout2, delta <6)
summary(output2)
This gave us the following significant results:
> summary(output2)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [
glmerMod]
Family: poisson ( log )
Formula: StrataNumber ~ ffactor1 + (1 | Focal)
Data: aa
AIC BIC logLik deviance df.resid
9404.4 9428.0 -4698.2 9396.4 2688
Scaled residuals:
Min 1Q Median 3Q Max
-1.78263 -0.33628 0.06559 0.32481 1.37514
Random effects:
Groups Name Variance Std.Dev.
Focal (Intercept) 0.006274 0.07921
Number of obs: 2692, groups: Focal, 7
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.31659 0.03523 37.368 < 2e-16 ***
ffactor12 0.09982 0.02431 4.107 4.01e-05 ***
ffactor13 0.17184 0.02425 7.087 1.37e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) ffct12
ffactor12 -0.403
ffactor13 -0.403 0.585
I want to see if four predictors ("OA_statusclosed" "OA_statusgreen" "OA_statushybrid" "OA_statusbronze") have an effect on "logAlt." I have chosen to do a lmer in r to account for random intercepts and slopes by variable "Journal".
I want to interpret the output so that a higher OA status (in order of highest status: green, hybrid, bronze, closed). In order to do this, I have contrast coded the four variables as such (adhering to the order of the variables in my df being hybrid, closed, green, bronze):
contrasts(df$OA_status.c) <- c(0.25, -0.5, 0.5, -0.25)
contrasts(df$OA_status.c)
I have ran this analysis:
M3 <- lmer(logAlt ~ OA_status + (1|Journal),
data = df,
control=lmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e5)))
And got this summary(M3):
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: logAlt ~ OA_status + (1 | Journal)
Data: df
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
REML criterion at convergence: 20873.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.1272 -0.6719 0.0602 0.6618 4.4344
Random effects:
Groups Name Variance Std.Dev.
Journal (Intercept) 0.08848 0.2975
Residual 1.49272 1.2218
Number of obs: 6435, groups: Journal, 7
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.03867 0.15059 18.27727 13.538 5.71e-11 ***
OA_statusclosed -0.97648 0.09915 6428.62227 -9.848 < 2e-16 ***
OA_statusgreen -0.74956 0.10320 6429.65387 -7.263 4.22e-13 ***
OA_statushybrid 0.04621 0.12590 6427.44114 0.367 0.714
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OA_sttsc OA_sttsg
OA_sttsclsd -0.640
OA_statsgrn -0.613 0.934
OA_sttshybr -0.501 0.763 0.744
I interpret this to mean that, for example, OA_statusclosed results in an average of -0.97 less of a logAlt value than the reference value, and that OA_statusclosed is a significant predictor.
I have two questions:
Am I approaching contrast coding correctly— in that, am I making "OA_statusgreen" my reference value (which is what I think I need to do?)
Am I interpreting the output correctly?
Thanks in advance!
I want to use quadratic terms to fit my general linear mixed model with id as a random effect, using the lme4 package. It's about how the distance to settlements influences the probability of occurrence of an animal. I use the following code (I hope it is correct):
glmer_dissettl <- glmer(case ~ poly(dist_settlements,2) + (1|id), data=rsf.data, family=binomial(link="logit"))
summary(glmer_dissettl)
I get the following output:
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: case ~ poly(dist_settlements, 2) + (1 | id)
Data: rsf.data
AIC BIC logLik deviance df.resid
6179.2 6205.0 -3085.6 6171.2 4654
Scaled residuals:
Min 1Q Median 3Q Max
-3.14647 -0.90518 -0.04614 0.94833 1.66806
Random effects:
Groups Name Variance Std.Dev.
id (Intercept) 0.02319 0.1523
Number of obs: 4658, groups: id, 18
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.02684 0.04905 0.547 0.584
poly(dist_settlements, 2)1 37.94959 2.41440 15.718 <2e-16 ***
poly(dist_settlements, 2)2 -1.28536 2.28040 -0.564 0.573
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) p(_,2)1
ply(ds_,2)1 0.083
ply(ds_,2)2 0.067 0.150
I don't know exactly how to interpret this, especially with the two lines for poly(dist_settlements,2). Next to understanding, I also wanna see if the quadratic term is making the model better than the basic model without it.
The output of the basic model without a quadratic term:
Generalized linear mixed model fit by maximum likelihood
(Laplace Approximation) [glmerMod]
Family: binomial ( logit )
Formula: case ~ scale(dist_settlements) + (1 | id)
Data: rsf.data
AIC BIC logLik deviance df.resid
6177.5 6196.9 -3085.8 6171.5 4655
Scaled residuals:
Min 1Q Median 3Q Max
-3.6009 -0.8998 -0.0620 0.9539 1.6417
Random effects:
Groups Name Variance Std.Dev.
id (Intercept) 0.02403 0.155
Number of obs: 4658, groups: id, 18
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.02873 0.04945 0.581 0.561
scale(dist_settlements) 0.55936 0.03538 15.810 <2e-16
(Intercept)
scale(dist_settlements) ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scl(dst_st) 0.077
I appreciate every tip.
A couple of points.
Coefficients of non-linear model terms do not have a straightforward interpretation and you should make effect plots to be able to communicate the results from your analyses. You may use effectPlotData() from the GLMMadaptive package to do this. Refer to this page for more information.
To be able to appraise whether including a quadratic effect of dist_settlements improves the model fit, you should fit a model without the squared term (i.e. only the linear effect of dist_settlements) and a model with the squared term. Then perform a likelihood ratio test to appraise whether inclusion of complex terms improves the model fit. In case of LMMs, make sure to fit both models using maximum likelihood, not REML. For GLMMs, you don't have to borther about (RE)ML.
The variance of the random intercepts is rather close to 0, which may require your attention. Refer to this answer and this section of Ben Bolker's github for more information on this topic.
You may want to take a look at this great lecture series by Dimitris Rizopoulos for more information on (G)LMMs.
I am running a mixed effects logistic regression using lme4 in R.
I have one predictor that is a dichotomous categorical variable. It is coded 1/0 and is defined as a factor.
I find that the random item intercept is perfectly correlated with the random item slope for my predictor. So, I run a new model in which they are uncorrelated using the following code:
m1<-glmer(DV~1+PPTGender+(1|Subject)+(1+PPTGender||Item), data = data, family = "binomial")
However, the output gives me two terms for the random slope:
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: DV ~ 1 + PPTGender + (1 | Subject) + (1 + PPTGender || Item)
Data: data
AIC BIC logLik deviance df.resid
499.7 526.9 -242.9 485.7 353
Scaled residuals:
Min 1Q Median 3Q Max
-1.7334 -1.0057 0.6312 0.8807 1.3858
Random effects:
Groups Name Variance Std.Dev. Corr
Subject (Intercept) 6.323e-10 2.514e-05
Item (Intercept) 2.785e-09 5.278e-05
Item.1 PPTGender0 5.229e-01 7.231e-01
PPTGender1 6.889e-03 8.300e-02 -1.00
Number of obs: 360, groups: Subject, 60; Item, 36
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.28229 0.17833 1.583 0.113
PPTGender -0.07718 0.29534 -0.261 0.794
Correlation of Fixed Effects:
(Intr)
PPTGndr -0.635
Can anyone explain why this happens?
If I redefine the PPTGender variable as a numeric character variable like so:
data$PPTGender<-as.numeric(as.character(data$PPTGender))
It goes away:
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: DV ~ 1 + PPTGender + (1 | Subject) + (1 + PPTGender || Item)
Data: data
AIC BIC logLik deviance df.resid
500.8 520.2 -245.4 490.8 355
Scaled residuals:
Min 1Q Median 3Q Max
-1.4075 -1.0489 0.7410 0.8472 1.1603
Random effects:
Groups Name Variance Std.Dev.
Subject (Intercept) 3.638e-10 1.907e-05
PairNumber (Intercept) 2.081e-01 4.562e-01
PairNumber.1 PPTGender 1.091e-08 1.044e-04
Number of obs: 360, groups: Subject, 60; Item, 36
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.26056 0.14625 1.782 0.0748 .
PPTGender -0.03009 0.26720 -0.113 0.9103
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
PPTGendr -0.397
Is this just a quirk in R? Is there anything wrong with this latter approach?
I have run the following glmm:
mod<-glmer(data=newdata, total_flr_vis ~ treatment + flr_num + (1|individual), family=poisson)
and get this output from summary()
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: poisson ( log )
Formula: total_flr_vis ~ treatment + flr_num + (1 | individual)
Data: newdata
AIC BIC logLik deviance df.resid
706.8 718.9 -349.4 698.8 148
Scaled residuals:
Min 1Q Median 3Q Max
-3.1461 -0.8155 -0.3522 -0.2022 14.1669
Random effects:
Groups Name Variance Std.Dev.
individual (Intercept) 4.066 2.016
Number of obs: 152, groups: individual, 42
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.610176 0.432666 -3.722 0.000198 ***
treatmentR -0.457492 0.121054 -3.779 0.000157 ***
flr_num 0.037063 0.005064 7.319 2.5e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) trtmnR
treatmentR -0.117
flr_num -0.416 0.040
But I get the following plot for treatment using plot(allEffects(mod))
I don't understand why the effects plot shows overlapping error bars while the summary() output tells me that the effect of treatment is significant. Is there a problem with the model, or is it the plot? How can I troubleshoot this?
Here is the residual plot (which I got using plot(mod))
I'm not totally sure how to interpret this plot, but it does not look random to me, thus I suspect that there is something wrong with the model.
I am happy to post data if someone can tell me where to do that.
Any help would be very welcome.