I have to make a prediction tool from a Cox model.
Normally I could have used predictCox from the riskRegression package to get the predicted survival and confidence interval.
But for that I would have to upload my data with the tool. But I don't have the right to do that.
The alternative is to estimate the basis risk at a given instant, obtain the estimated coefficients to build a simple regression model which can be put online.
However, I'm not sure about the procedure and the packages I can use to get it.
I have read that some packages center linear predictors etc.
Also, I don't know how to get the confidence interval.
Can someone confirm me in a few words what would be the right procedure and the packages to use in this case?
library(survival)
library(riskRegression)
mfit <- coxph(Surv(futime, death) ~ sex +ns(age,2), data=mgus, x=T, y=T)
prediction_surv2 <- predictCox(mfit, newdata=newdata, times=365.25*5 , type = "survival",
se = TRUE, iid = TRUE, band = TRUE)
prediction_surv2
Thanks a lot !
Related
I am utilizing the predictInterval() function from the merTools package. My model is fit utilizing a Poisson family specification like the below:
glmer(y ~ (1|key) + x, data = dat, family = poisson())
When I use predictInterval() to calculate the prediction interval associated with my model, I get the following warning message:
Warning message:
Prediction for NLMMs or GLMMs that are not mixed binomial regressions is not tested. Sigma set at 1.
I am taking this to mean that predictInterval() doesn't have an implementation for models fit with a Poisson distribution. I therefore do not trust the resulting interval.
Is my interpretation correct? I have searched around for similar issues but haven't found anything.
Any help would be greatly appreciated.
I have longitudinal data from two surveys and I want to do a pre-post analysis. Normally, I would use survey::svyglm() or svyVGAM::svy_vglm (for multinomial family) to include sampling weights, but these functions don't account for the random effects. On the other hand, lme4::lmer accounts for the repeated measures, but not the sampling weights.
For continuous outcomes, I understand that I can do
w_data_wide <- svydesign(ids = ~1, data = data_wide, weights = data_wide$weight)
svyglm((post-pre) ~ group, w_data_wide)
and get the same estimates that I would get if I could use lmer(outcome ~ group*time + (1|id), data_long) with weights [please correct me if I'm wrong].
However, for categorical variables, I don't know how to do the analyses. WeMix::mix() has a parameter weights, but I'm not sure if it treats them as sampling weights. Still, this function can't support multinomial family.
So, to resume: can you enlighten me on how to do a pre-post test analysis of categorical outcomes with 2 or more levels? Any tips about packages/functions in R and how to use/write them would be appreciated.
I give below some data sets with binomial and multinomial outcomes:
library(data.table)
set.seed(1)
data_long <- data.table(
id=rep(1:5,2),
time=c(rep("Pre",5),rep("Post",5)),
outcome1=sample(c("Yes","No"),10,replace=T),
outcome2=sample(c("Low","Medium","High"),10,replace=T),
outcome3=rnorm(10),
group=rep(sample(c("Man","Woman"),5,replace=T),2),
weight=rep(c(1,0.5,1.5,0.75,1.25),2)
)
data_wide <- dcast(data_long, id~time, value.var = c('outcome1','outcome2','outcome3','group','weight'))[, `:=` (weight_Post = NULL, group_Post = NULL)]
EDIT
As I said below in the comments, I've been using lmer and glmer with variables used to calculate the weights as predictors. It happens that glmer returns a lot of problems (convergence, high eigenvalues...), so I give another look at #ThomasLumley answer in this post and others (https://stat.ethz.ch/pipermail/r-help/2012-June/315529.html | https://stats.stackexchange.com/questions/89204/fitting-multilevel-models-to-complex-survey-data-in-r).
So, my question is now if a can use participants id as clusters in svydesign
library(survey)
w_data_long_cluster <- svydesign(ids = ~id, data = data_long, weights = data_long$weight)
summary(svyglm(factor(outcome1) ~ group*time, w_data_long_cluster, family="quasibinomial"))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.875e+01 1.000e+00 18.746 0.0339 *
groupWoman -1.903e+01 1.536e+00 -12.394 0.0513 .
timePre 5.443e-09 5.443e-09 1.000 0.5000
groupWoman:timePre 2.877e-01 1.143e+00 0.252 0.8431
and still interpret groupWoman:timePre as differences in the average rate of change/improvement in the outcome over time between sex groups, as if I was using mixed models with participants as random effects.
Thank you once again!
A linear model with svyglm does not give the same parameter estimates as lme4::lmer. It does estimate the same parameters as lme4::lmer if the model is correctly specified, though.
Generalised linear models with svyglm or svy_vglm don't estimate the same parameters as lme4::glmer, as you note. However, they do estimate perfectly good regression parameters and if you aren't specifically interested in the variance components or in estimating the realised random effects (BLUPs) I would recommend just using svy_glm.
Another option if you have non-survey software for random effects versions of the models is to use that. If you scale the weights to sum to the sample size and if all the clustering in the design is modelled by random effects in the model, you will get at least a reasonable approximation to valid inference. That's what I've seen recommended for Bayesian survey modelling, for example.
I have build a binary logistic regression for churn prediction in Rstudio. Due to the unbalanced data used for this model, I also included weights. Then I tried to find the optimum cutoff by try and error, however To complete my research I have to incorporate ROC curves to find the optimum cutoff. Below I provided the script I used to build the model (fit2). The weight is stored in 'W'. This states that the costs of wrongly identifying a churner is 14 times as large as the costs of wrongly identifying a non-churner.
#CH1 logistic regression
library(caret)
W = 14
lvl = levels(trainingset$CH1)
print(lvl)
#if positive we give it the defined weight, otherwise set it to 1
fit_wts = ifelse(trainingset$CH1==lvl[2],W,1)
fit2 = glm(CH1 ~ RET + ORD + LVB + REVA + OPEN + REV2KF + CAL + PSIZEF + COM_P_C + PEN + SHOP, data = trainingset, weight=fit_wts, family=binomial(link='logit'))
# we test it on the test set
predlog1 = ifelse(predict(fit2,testset,type="response")>0.5,lvl[2],lvl[1])
predlog1 = factor(predlog1,levels=lvl)
predlog1
confusionMatrix(pred,testset$CH1,positive=lvl[2])
For this research I have also build ROC curves for decision trees using the pROC package. However, of course the same script does not work the same for a logistic regression. I have created a ROC curve for the logistic regression using the script below.
prob=predict(fit2, testset, type=c("response"))
testset$prob=prob
library(pROC)
g <- roc(CH1 ~ prob, data = testset, )
g
plot(g)
Which resulted in the ROC curve below.
How do I get the optimum cut off from this ROC curve?
Getting the "optimal" cutoff is totally independent of the type of model, so you can get it like you would for any other type of model with pROC. With the coords function:
coords(g, "best", transpose = FALSE)
Or directly on a plot:
plot(g, print.thres=TRUE)
Now the above simply maximizes the sum of sensitivity and specificity. This is often too simplistic and you probably need a clear definition of "optimal" that is adapted to your use case. That's mostly beyond the scope of this question, but as a starting point you should a look at Best Thresholds section of the documentation of the coords function for some basic options.
I have a linear mixed effects model that looks like this:
model.1 <- lmer(x ~ 0 + treatment + (1|block), data)
I pulled out the fixed effect estimates from the model:
data$FittedValues <- fixef(model.1)
I made a distribution histogram of the fitted values and I need to know the 95% CI of the fitted values. I tried confint() which gives a CI for each treatment, but what I need a CI for the entire set of fitted values. I can run a t.test on the fitted values but I don't think this gives me the correct answer.
t.test(FittedValues, data = data,
alternative = 'two.sided',
conf.level = 0.95,
na.rm = TRUE)
I am new to stats and R, but I searched for quite some time and couldn't find an answer. Please excuse me if this is too simple of a questions for this board.
I am trying to determine confidence intervals for predicted probabilities from a binomial logistic regression in R. The model is estimated using lrm (from the package rms) to allow for clustering standard errors on survey respondents (each respondent appears up to 3 times in the data):
library(rms)
model1<-lrm(outcome~var1+var2+var3,data=mydata,x=T,y=T,se.fit=T)
model.rob<-robcov(model1,cluster=respondent.id)
I am able to estimate a predicted probability for the outcome using predict.lrm:
predicted.prob<-predict(model.rob,newdata=data.frame(var1=1,var2=.33,var3=.5),
type="fitted")
What I want to determine is a 95% confidence interval for this predicted probability. I have tried specifying se.fit=T, but this not permissible in predict.lrm when type=fitted.
I have spent the last few hours scouring the Internet for how to do this with lrm to no avail (obviously). Can anyone point me toward a method for determining this confidence interval? Alternatively, if it is impossible or difficult with lrm models, is there another way to estimate a logit with clustered standard errors for which confidence intervals would be more easily obtainable?
The help file for predict.lrm has a clear example. Here is a slight modification of it:
L <- predict(fit, newdata=data.frame(...), se.fit=TRUE)
plogis(with(L, linear.predictors + 1.96*cbind(- se.fit, se.fit)))
For some problems you may want to use the gendata or Predict functions, e.g.
L <- predict(fit, gendata(fit, var1=1), se.fit=TRUE) # leave other vars at median/mode
Predict(fit, var1=1:2, var2=3) # leave other vars at median/mode; gives CLs