I am applying a GAM model to my data: cell abundance over time.
The model works just fine (although I am aware of a pattern in my resiudals, but this is a different issue not relevant here).
It just fails to display the partial residuals in the final plot, although i set residuals = TRUE. Here is my output:
https://i.stack.imgur.com/C1MlY.png
also I used mgcv package.
Previously this code worked as I wanted, but on different data. Any ideas on why it is not working are welcome!
GAM_EA <- mgcv::gam(EUB_FISH ~ s(Day, by = Heatwave), data = HnH, method = "REML")
gam.check(GAM_EA) #Checking the model
mgcv::anova.gam(GAM_EA) #Retrieving the statistical results. See ?anova.gam
summary.gam(GAM_EA)
plot(GAM_EA, shift = coef(GAM_EA)[1], residuals = TRUE)
See argument by.resid in ?plot.gam. They way these are used in plot.gam would been meaningless for factor by terms unless you were to subset the partial residuals and plot only the residuals for observations in the specific level of the by factor.
Related
I'm working with a linear mixed model with sex and diel (day/night) as my predictors and depth displacement as my response in R. Here is the model:
displacement_lmm_hour <- lmer(Displacement~sex*Light + (1|Hour), data = avg_depth_df_hour)
I want to create a whisker plot displaying each predicted value for each of my predictors from the model. So, I tried using dwplot from the dotwhisker() library in R.
dwplot(displacement_lmm_hour, effects = "fixed")
This is what it came out with:
As you can see, it is only showing the first 'sets' (if you will) of predicted values. Ie. there's no males, or day time values shown. I realize this comes from the model itself and the summary() table of the model only shows those as well. But, how can I show these values for the 'hidden' predicted values that also come from the model?
I also tried using '''plot_model''', which allowed me to separate my predicted values, but I don't think the error bars are correct (why I tried the whisker plots instead)
plot_model(displacement_lmm_hour, type = "pred", terms = c("sex","Light"), axis.title = c("Sex", "Displacement")
Do you have an idea how to accomplish this using the dwplot function? Or another way to accomplish this in general?
Thanks!
I have fitted a lme model in R with a logit transformed response. I have not been able to find a direct command that does the logit transformation so I have done it manually.
logitr<-log(r/1-r)
I then use this as response in my lme model with interaction between two factors and a numerical variable.
model<-lme(logitr<-factor1*factor2*numeric,random=1|random)
Now, R obviously do not know that this model is logit transformed. How can I specify this to R?
I have without luck tried:
update(model, tran="logit")
The reason why I want to specify that the model is logit transformed is because I want to plot the backtransformed results using the function emmip in the emmeans package, showing the trends of the interaction between my variables.
Normally (if I only had factors) I would just use:
update_refgrid_model<-update(ref_grid(model, tran="logit"))
But this approach does not work when I want to use emmip to plot the trends of the interaction between a numerical variable and factors. If I specify:
emmip(update_refgrid_model, factor1~numeric|factor2, cov.reduce = range, type = "response")
then I do not get any trends plotted, only the estimate for the average level on the numerical variable.
So, how can I specify the logit transformation and plot the backtransformed trends of a lme model with factors interacting with numerical variables?
You don't update the model object, you update the reference grid:
rg = update(ref_grid(model, cov.reduce = range), tran = "logit")
emmip(rg, factor1~numeric|factor2, type = "response")
It is possible to update a model with other things, just not the transformation; that is in the update method for emmGrid objects.
Update
Here's an example showing how it works
require(emmeans)
## Loading required package: emmeans
foo = transform(fiber, p = (strength - 25)/25)
foo.lm = lm(log(p/(1-p)) ~ machine*diameter, data = foo)
emm = emmeans(foo.lm, ~diameter|machine,
tran = "logit", at = list(diameter = 15:32))
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
emmip(emm, machine ~ diameter)
emmip(emm, machine ~ diameter, type = "r")
Created on 2020-06-02 by the reprex package (v0.3.0)
In the past, I had used the sjp.glmer from the package sjPlot to visualize the different slopes from a generalized mixed effects model. However, with the new package, I can't figure out how to plot the individual slopes, as in the figure for the probabilities of fixed effects by (random) group level, located here
Here is the code that, I think, should allow for the production of the figure. I just can't seem to get it in the new version of sjPlot.
library(lme4)
library(sjPlot)
data(efc)
# create binary response
efc$hi_qol = 0
efc$hi_qol[efc$quol_5 > mean(efc$quol_5,na.rm=T)] = 1
# prepare group variable
efc$grp = as.factor(efc$e15relat)
# data frame for 2nd fitted model
mydf <- na.omit(data.frame(hi_qol = as.factor(efc$hi_qol),
sex = as.factor(efc$c161sex),
c12hour = as.numeric(efc$c12hour),
neg_c_7 = as.numeric(efc$neg_c_7),
grp = efc$grp))
# fit 2nd model
fit2 <- glmer(hi_qol ~ sex + c12hour + neg_c_7 + (1|grp),
data = mydf,
family = binomial("logit"))
I have tried to graph the model using the following code.
plot_model(fit2,type="re")
plot_model(fit2,type="prob")
plot_model(fit2,type="eff")
I think that I may be missing a flag, but after reading through the documentation, I can't find out what that flag may be.
Looks like this might do what you want:
(pp <- plot_model(fit2,type="pred",
terms=c("c12hour","grp"),pred.type="re"))
type="pred": plot predicted values
terms=c("c12hour", "grp"): include c12hour (as the x-axis variable) and grp in the predictions
pred.type="re": random effects
I haven't been able to get confidence-interval ribbons yet (tried ci.lvl=0.9, but no luck ...)
pp+facet_wrap(~group) comes closer to the plot shown in the linked blog post (each random-effects level gets its own facet ...)
Ben already posted the correct answer. sjPlot uses the ggeffects-package for marginal effects plot, so an alternative would be using ggeffects directly:
ggpredict(fit2, terms = c("c12hour", "grp"), type="re") %>% plot()
There's a new vignette describing how to get marginal effects for mixed models / random effects. However, confidence intervals are currently not available for this plot-type.
The type = "ri.prob" option in the linked blog-post did not adjust for covariates, that's why I first removed that option and later re-implemented it (correctly) in ggeffects / sjPlot. The confidence intervals shown in the linked blog-post are not correct, either. Once I figure out a way how to obtain CI or prediction intervals, I'll add this option as well.
I'm using clmm function from ordinal package in R in order to fit cumulative mixed models to my data. It worked fine until I tried to get predicted probabilities. I can't get either SE or confidence intervals by specifying se.fit=TRUE and interval=TRUE. It looks like this:
mod1<-clmm2(response~X0+X1+X2+X3+X4+X5+X7+X0*X2*X3+X2*X3*X4+X0:X4, random=X6,
data=df,link ="logistic", threshold ="flexible",
Hess=TRUE, nAGQ=7)
As you can see there a bunch of interaction there (all important). I've tried to create a dummy dataset for my problem to be reproducible but clmm can't achieve convergence with a simpler dataset. I took the wine dataset included in the package ordinal and did some changes with the formula to mimic my own (I don't think it makes any sense though):
library(ordinal)
data(wine)
fm1 <- clmm2(rating ~ temp + contact+bottle+temp:contact:bottle+temp:contact+ temp:bottle+bottle:contact,random=judge, data=wine,link ="logistic", threshold ="flexible",
Hess=TRUE, nAGQ=7)
head(do.call("cbind", predict(fm1, se.fit=TRUE, interval=TRUE)))
And then I get this error:
Error in head(do.call("cbind", predict(fm1, se.fit = TRUE, interval = TRUE))) :
error in evaluating the argument 'x' in selecting a method for function 'head' : Erreur dans do.call("cbind", predict(fm1, se.fit = TRUE, interval = TRUE)) : second argument must be a list
My guess is that predict does'nt even compute SE and IC in a case like this. Does anybody knows why? Is there anyway to get those values?
Thanks a lot!
The predict method for clmm2 objects does not offer std-errors. See its help page. This is in keeping with the usual practice of R package authors when dealing with mixed effects models.
I have fit my discrete count data using a variety of functions for comparison. I fit a GEE model using geepack, a linear mixed effect model on the log(count) using lme (nlme), a GLMM using glmer (lme4), and a GAMM using gamm4 (gamm4) in R.
I am interested in comparing these models and would like to plot the expected (predicted) values for a new set of data (predictor variables). My goal is to compare the predicted effects for each model under particular conditions (x variables). Of particular interest is the comparison between marginal (GEE) and conditional estimates.
I think my main problem might be getting the new data in the correct form with the correct labels and attributes and such. I am still very much an R novice and struggle with this stuff (no course on this at my university unfortunately).
I currently have fitted models
gee1 lme1 lmer1 gamm1
and can extract their fixed effect coefficients and standard errors without a problem. I also don't have a problem converting them from the log scale or estimating confidence intervals accounting for the random effects.
I also have my new dataframe newdat which has 365 observations of 23 variables (average environmental data for each day of the year).
I am stuck on how to predict new count estimates from this. I played around with the model.matrix function but couldn't get it to work. For example, I tried:
mm = model.matrix(terms(glmm1), newdat) # Error in model.frame.default(object,
# data, xlev = xlev) : object is not a matrix
newdat$pcount = mm %*% fixef(glmm1)
Any suggestions or good references would be greatly appreciated. Can anyone help with the error above?
Getting predictions for lme() and lmer() is documented on http://glmm.wikidot.com/faq