Does anyone have experience with obtaining latent trait scores from a repeated measure design. Currently, my data is in long format (where baseline data is stacked on post-treatment data, similar to the way you would fix the dataset for lmer). With this dataset in long format, I attempted to use the mixedmirt function from the mirt package and regressed the latent trait on Time; however, I am unsure if mirt recognized that my design is repeated. It seemed to model the correlation and covariance in the latent abilities. Does anyone have experience with mirt or can recommend another R package for my analysis?
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I attempt to conduct generalized linear model in a microbiome dataset (16S HTS).
Given that microbiome dataset is compositional, I am wondering if there are any resources that compatible for doing the analysis.
I am using R package mvabund (doi: 10.1111/j.2041-210X.2012.00190.x), but the examples and documentation did not provide clues on compositional data.
It seems that it is not appropriate to treat the count data as abundance data, would converting the dataset into relative abundance or performing clr-transformation solve the problem?
I have a dataset containing information about weather, air pollution and healthoutcomes. I want to regress temperature (T) and temperature lag (T1) against cardiac deaths (CVD). I have previously used the glm model in R using the following script:
#for mean daily temperature and temperature lags separately.
modelT<-glm(cvd~T, data=datapoisson, family=poisson(link="log"), na=na.omit)
I get the effect estimates and standard error values which i used to convert to risk ratio.
Now i want to use dynamic linear model or distributed linear model for check the predictor-outcome and lagged predictor outcome association. However, i can't find the script for running the model in R.
I installed the DLM package in R, but still can't figure out how to build a model using DLM package in R.
I would appreciate if someone can help with it.
Could you try least squares multiple regression to predict the outcome? I used that method when I tried to 'predict' which factors influenced power in a floating offshore wind turbine. It is good for correlating multiple parameters.
They fit a plane to a set of points, but it seems like a similar idea.
https://math.stackexchange.com/questions/99299/best-fitting-plane-given-a-set-of-points
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 a dataset with a binary dependent variable and a number of predictors, including participant. I am trying to examine the idiosyncratic effects of different predictors for different participants. In order to do that, I'm trying to look at the effect of interactions between participant id and the other predictors on the dependent variable. I'm using randomForest in R. I can fit the forest successfully, and can produce partial dependence plots for individual variables. What I need, however, are partial dependence plots for pairs of variables - participant + others. Is this possible?
For reference, my code:
data_sample<-data_raw[sample(1:nrow(data_raw),500,replace=F),];
test_rf<-randomForest(perceptually.rhotic~vowel+speaker+modified_clip_start+function_word+year_of_birth+gender+fathers_job_type+prepausal,data=data_sample,ntree=500,mtry=3);
partialPlot(test_rf,pred.dat=data_sample,x.var="speaker");
??? partialPlot(test_rf,pred.dat=data_sample,x.var=c("speaker","vowel"));
Thanks very much in advance for any advice anyone can offer!
The plotmo R package will plot partial dependencies for all variables and pairs of variables (bivariate dependencies) for "any" model. For example:
library(randomForest)
data(trees)
mod <- randomForest(Volume~., data=trees)
library(plotmo)
plotmo(mod, pmethod="partdep") # plot partial dependencies
which gives
You can specify exactly which variable and variable pairs get plotted using plotmo's all1, all2, degree1, and degree2 arguments. Additional examples are in the vignette for the plotmo package.
I have two temporal processes. I would like to see if one temporal process (X_{t,2}) can be used to perform better forecast of the other process (X_{t,1}). I have multiple sources providing temporal data on X_{t,2}, (e.g. 3 time series measuring X_{t,2}). All time series require a seasonal component.
I found MARSS' notation to be pretty natural to fit this type of model and the code looks like this:
Z=factor(c("R","S","S","S")) # observation matrix
B=matrix(list(1,0,"beta",1),2,2) #evolution matrix
A="zero" #demeaned
R=matrix(list(0),4,4); diag(R)=c("r","s","s","s")
Q="diagonal and unequal"
U="zero"
period = 12
per.1st = 1 # Now create factors for seasons
c.in = diag(period)
for(i in 2:(ceiling(TT/period))) {c.in = cbind(c.in,diag(period))}
c.in = c.in[,(1:TT)+(per.1st-1)]
rownames(c.in) = month.abb
C = "unconstrained" #2 x 12 matrix
dlmfit = MARSS(data, model=list(Z=Z,B=B,Q=Q,C=C, c=c.in,R=R,A=A,U=U))
I got a beta estimate implying that the second temporal process is useful in forecasting the first process but to my dismay, MARSS gives me an error when I use MARSSsimulate to forecast because one of the matrices (related to seasonality) is time-varying.
Anyone, knows a way around this issue of the MARSS package? And if not, any tips on fitting an analogous model using, say the dlm package?
I was able to represent my state-space model in a form adequate to use with the dlm package. But I encountered some problems using dlm too. First, the ML estimates are VERY unstable. I bypassed this issue by constructing the dlm model based on marss estimates. However, dlmFilter is not working properly. I think the issue is that dlmFilter is not designed to deal with models with multiple sources for one time series, and additional seasonal components. dlmForecast gives me forecasts that I need!!!
In summary for my multivariate time series model (with multiple sources providing data for one of the temporal processes), the MARSS library gave me reasonable estimates of the parameters and allowed me to obtain filtered and smoothed values of the states. Forecast values were not possible. On the other hand, dlm gave fishy estimates for my model and the dlmFilter didn't work, but I was able to use dlmForecast to forecast values using the model I fitted in MARSS and reexpressed in dlm appropriate form.