I have constructed a mixed effect model using lmer() with the aim of comparing the growth in reading scores for four different groups of children as they age.
I would like to plot a graph of the 4 different slopes with confidence intervals in R in order to visualize this relationship but I keep getting stuck.
I have tried to use the plot function and some versions of the ggplot as I have done for previous lm() models but it isn't working so far. Here is my attempted model which I hope looks at how the change in reading scores over time(age) interacts with a child's SESDLD grouping (this indicated whether a child has a language problem and whether or not they are high or low income).
AgeSES.model <- lmer(ReadingMeasure ~ Age.c*SESDLD1 + (1|childid), data = reshapedomit, REML = FALSE)
The ReadingMeasure is a continuous score, age.c is centered age measured in months. SESDLD1 is a categorical measure which has 4 levels. I would expect four positive slopes of ReadingMeasure growth with different intercepts and probably differing slopes.
I would really appreciate any pointers on how to do this!
Thank you so much!!
The type of plot I would like to achieve - this was done in Stata
Related
I am relatively new to both R and Stack overflow so please bear with me. I am currently using GLMs to model ecological count data under a negative binomial distribution in brms. Here is my general model structure, which I have chosen based on fit, convergence, low LOOIC when compared to other models, etc:
My goal is to characterize population trends of study organisms over the study period. I have created marginal effects plots by using the model to predict on a new dataset where all covariates are constant except year (shaded areas are 80% and 95% credible intervals for posterior predicted means):
I am now hoping to extract trend magnitudes that I can report and compare across species (i.e. say a certain species declined or increased by x% (+/- y%) per year). Because I use poly() in the model, my understanding is that R uses orthogonal polynomials, and the resulting polynomial coefficients are not easily interpretable. I have tried generating raw polynomials (setting raw=TRUE in poly()), which I thought would produce the same fit and have directly interpretable coefficients. However, the resulting models don't really run (after 5 hours neither chain gets through even a single iteration, whereas the same model with raw=FALSE only takes a few minutes to run). Very simplified versions of the model (e.g. count ~ poly(year, 2, raw=TRUE)) do run, but take several orders of magnitude longer than setting raw=FALSE, and the resulting model also predicts different counts than the model with orthogonal polynomials. My questions are (1) what is going on here? and (2) more broadly, how can I feasibly extract the linear term of the quartic polynomial describing response to year, or otherwise get at a value corresponding to population trend?
I feel like this should be relatively simple and I apologize if I'm overlooking something obvious. Please let me know if there is further code that I should share for more clarity–I didn't want to make the initial post crazy long, but happy to show specific predictions from different models or anything else. Thank you for any help.
Im using the book Applied Survival Analysis Using R by Moore to try and model some time-to-event data. The issue I'm running into is plotting the estimated survival curves from the cox model. Because of this I'm wondering if my understanding of the model is wrong or not. My data is simple: a time column t, an event indicator column (1 for event 0 for censor) i, and a predictor column with 6 factor levels p.
I believe I can plot estimated surival curves for a cox model as follows below. But I don't understand how to use survfit and baseplot, nor functions from survminer to achieve the same end. Here is some generic code for clarifying my question. I'll use the pharmcoSmoking data set to demonstrate my issue.
library(survival)
library(asaur)
t<-pharmacoSmoking$longestNoSmoke
i<-pharmacoSmoking$relapse
p<-pharmacoSmoking$levelSmoking
data<-as.data.frame(cbind(t,i,p))
model <- coxph(Surv(data$t, data$i) ~ p, data=data)
As I understand it, with the following code snippets, modeled after book examples, a baseline (cumulative) hazard at my reference factor level for p may be given from
base<-basehaz(model, centered=F)
An estimate of the survival curve is given by
s<-exp(-base$hazard)
t<-base$time
plot(s~t, typ = "l")
The survival curve associated with a different factor level may then be given by
beta_n<-model$coefficients #only one coef in this case
s_n <- s^(exp(beta_n))
lines(s_n~t)
where beta_n is the coefficient for the nth factor level from the cox model. The code above gives what I think are estimated survival curves for heavy vs light smokers in the pharmcoSmokers dataset.
Since thats a bit of code I was looking to packages for a one-liner solution, I had a hard time with the documentation for Survival ( there weren't many examples in the docs) and also tried survminer. For the latter I've tried:
library(survminer)
ggadjustedcurves(model, variable ="p" , data=data)
This gives me something different than my prior code, although it is similar. Is the method I used earlier incorrect? Or is there a different methodology that accounts for the difference? The survminer code doesn't work from my data (I get a 'can't allocated vector of size yada yada error, and my data is ~1m rows) which seems weird considering I can make plots using what I did before no problem. This is the primary reason I am wondering if I am understanding how to plot survival curves for my model.
I fit my data to a general linear mixed model with Treatment as a fixed effect and Clutch as a random effect. Here is my code:
model<-glmer(cbind(Successes,Failures)~Treatment+(1|Clutch),
data = cont, family = "binomial")
My work deals with sex ratios, and I define a female as a success, and a male as a failure for each observation. I have 4 different treatments. I want to plot (preferably using ggplot) the predicted sex ratio from the model for each treatment, taking clutch into account (with 95% confidence intervals). I realize this is probably a large question, but can anyone help me with the code I would need to do this? I have been searching online for the past few days. Thanks!
I'm studying the effect of different predictors (dummy, categorical and continuos variables) on presence of birds, obtained from bird counts at-sea. To do that I used a glmmadmb function and binomial family.
I've plotted the relationship between response variable and predictors in order to asses the model fit and the marginal effect of each predictor. To draw the graphs I used visreg function, specifying the transformation of the vertical axis:
visreg(modelo.bn7, type="conditional", scale="response", ylab= "Bird Presence")
The output graphs showed a confident bands very wide when I used the original scale of the response variable (covering the whole vertical axis). In case of graphs without transformation, confident bands were shorter but they had the same extension in the different levels of dummy variables. Does anyone know how the confidents bands are calculated in binomial distributions? Could it reflect that I have a problem in the estimated coefficients or in the model fit?
The confidence bands are calculated using p-values for binomial distribution... For detailed explanation you can ask on stats.stackexchange.com. If the bands are very wide (and the interpretation of 'wide' is subjective and mostly based on what is your goal) then it shows that your estimates may not be very accurate. High p-values usually are due to small or insufficient number of observations used for building the model. If the number of observations are large, then it does indicate a poor fit.
So I've looked at a number of similarly themed posts but none of them seem to be exactly what I need, or I simply don't really understand the solutions they offered... So here it goes...
I ran a mixed-effects model with lme4 to look at some chimpanzee data. I have two factors (aggression rate; copulation rate) which affect my dependent (feeding time).
I would like to produce two scatter plots which show the relationship between each of the predictors and the outcome variable but I would like to draw a line, which is derived from the model estimates (and not an abline of the (lm(y ~ x)) type, which only gives a simple regression line, not one based on the full LMM).
I have a sense that this is only possible with ggplot2 but I have not been able to actually figure out how to do this. Having spent most of the day looking through books and forums, I was hoping this is something that may have a fairly straight-forward answer, if one knows what they are doing.
Thanks for any tips in advance!
Alex
To begin with I had the following model:
M3reml
Linear mixed model fit by REML ['lmerMod']
Formula: z.feeding_time ~ z.copul_rate + z.agro_given + z.agro_recd + (1 | Male) + ac_term
Data: N85
where the variables are the z-transformed values of: male chimpanzee feeding time (z.feeding_time); daily copulation rates with females (acts/hr; z.copul_rate); daily rate of aggression given (z.agro_given); and daily rate of aggression received (z.agro_recd). Random effect – male ID for the 12 males of my study; and a temporal autocorellation term (ac_term).
I wanted produce a regression line based on the model estimates for male feeding time.
Getting the estimates:
p1<-predict(M3reml)
Plotting the estimates against male rates of aggression (z-transformed values):
plot(p1~z.agro_given, data=N85)
adding a regression line:
abline(lm(p1~z.agro_given, data=N85))
I would post an image of the plot here but apparently I am not allowed to yet.