I'm using the (meta) package in R to analyze a meta-analysis. I have two random-effects models, both rma objects. One model, x1, is all the studies included in the analysis and x2 is the same model but with a few studies excluded. I am trying to create a forest plot with all of the studies using the forest function, but I can't figure out how to get it to display the overall effects including confidence intervals, of both x1 and x2 together on the same forest plot. I've searched everywhere online and can't seem to find an appropriate answer.
Does anyone know of a solution or have any ideas?
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I want to build a survival model then calculate the X-year (e.g. 10-year) risk of survival.
Is there a way to do this using coxph or survreg? Is this possible using random survival forest (e.g. ranger)?
P.S. not sure if important but data is wide (~100 features - mostly continuous) and 17k samples.
For anyone else trying to do the same. If you build a cox-model with survival::coxph or rms::cph you can use the function pec::predictSurvProb.
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 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
I was wondering if I get some advice about fitting hurdle models using continuous data and covariates.
I have some continuous data that are generally well fit using a right-skewed distribution such as a Pareto, Gamma, or Weibull distribution. However, there several zeros in my data which are important to my analysis. In addition, I have some categorical (two-level) covariates and would like to model the parameters of a distribution as a function of these covariates in order to formally evaluate their importance (e.g., using AIC).
I have seen examples of hurdle models fit using continuous data but have not yet found any examples of how to incorporate covariates and a model-selection framework. Does anyone have any suggestions as to how to proceed or know of any R packages that allow this procedure? I have included some code below to reproduce the type of data I am working with. The non-zero data are generated via a generalized Pareto distribution from the package texmex. The parameters were estimated directly from my non-zero data. I have also included the code to plot the data in a histogram to see their distribution.
library("texmex")
set.seed(101)
zeros <- rep(0,8)
non_zeros <- rgpd(17, sigm=exp(-10.4856), xi=0.1030, u = 0)
all.data <- c(zeros,non_zeros)
hist(non_zeros,breaks=50,xlim=c(0,0.00015),ylim=c(0,9),main="",xlab="",
col="gray")
hist(zeros,add=TRUE,col="black",breaks=100,xlim=c(0,0.00015),ylim=c(0,9))
legend("topright",legend=c("zeros"),col="black",lwd=8)
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