I am using the function adipart in the vegan package to partition diversity across spatial scales. I would like to get the standard error and/or standard deviations for my measures of beta and alpha at different scales. Is there any way to retrieve this data?
Are there other ways to get measures of variations for alpha and beta diversity in additive partitioning framework?
Thank you,
Laura
Related
I have a data set where observations come from highly distinct groups. Each group may have a wildly different distribution, so I am trying to find the best distribution using fitdist from fitdistrplus, then use gamlssML from the gamlss package to find the best parameters.
My issue is with transforming the data after this step. For some of the distributions, like the Box-Cox t, I can find the equation for normalizing the data using the BCT coefficients, but for many of these distributions I cannot.
Does gamlss have a function that normalizes the data after fitting? Their documentation only provides the transformations for a small number of distributions https://www.gamlss.com/wp-content/uploads/2018/01/DistributionsForModellingLocationScaleandShape.pdf
Thanks a lot
The normalised data values (for any distribution) are exactly equal to the residuals from a gamlss fit,
m1 <- gamlss()
which can be accessed by
residuals(m1) or
m1$residuals
I am doing a counterfactual impact evaluation on survival data. More precisely, I try to evaluate the impact of vocational training on time spent in unemployment. I use the Kaplan Meier estimator of the survival curve (package survival).
Before doing Kaplan Meier, I use coarsened exact matching (aim is ATT) to get the control and treatment groups close in terms of pretreatment covariates (package MatchIt).
For the Kaplan Meier estimator, I have to use the weights form the matching, which works well using the weights option and robust standard errors of survfit :
library(survival)
library(survminer)
kp_cem <- survfit(Surv(time=time_cem,event=status_cem)~treatment_cem, data=data_impact_cem,robust =TRUE,weights =weights)
Although, when I try to use a log-rank test to test for the difference in survival curves between treatment and control groups, I cannot take into account the frequency weights from the matching so the test statistics are not correct.
log_rank <- survdiff(Surv(time=time_cem,event=status_cem)~treatment_cem, data=data_impact_cem,rho=0)
I tried the option "pval = TRUE" of ggsurvplot (package survminer) but the problem is the same, the frequency weights are not taken into account.
How can I include frequency weights in survdiff? Are there other packages to compute log-rank test taking into account frequency weights (obtained after matching)?
There are at least two ways to do this:
First, you can use the survey::svylogrank function, as #IRTFM suggests. This will treat the weights as sampling weights, but I think that's ok with the robust standard errors that svylogrank uses.
Second, you can use survival::coxph. The logrank test is the score test in a Cox model, and coxph takes frequency weights. Use robust=TRUE if you want a robust score test: it will be at the bottom of the output of summary(your_cox_model) and you can extract it as summary(your_cox_model)$robscore
Thank you very much #Thomas Lumley and #IRTFM for your answers.
Here is how I apply your 2 suggestions (I added some comments + references).
1. Using survey::svylogrank
I don’t feel very confortable using sampling weights while it is really frequency weights that I have.
How should I specify the survey design ? The weights come from Coarsened Exact Matching (matchit with method = "cem") which is a class of stratum matching.
Should I specify the strata and the weights in the survey design ? In this vignette form Matchit Estimating Effects After Matching, it is suggested to use only weights and robust standard errors in the survival analysis (not the strata) (p. 27).
Here is how I specify the design and how I obtain the log-rank test using the package survey taking into account the weights from matching :
library(survey)
design_weights <- svydesign(id=~ibis, strata=~subclass, weights=~weights, data=data_impact_cem)
log_rank <- svylogrank(Surv(time=time_cem,event=status_cem)~treatment_cem, design=design_weights, rho=0)
2. Using survival::coxph
Thank you for this piece of information, being quite new to survival analysis, I overlooked this nice property of the equivalency of score test from cox model and log-rank test. For people wishing more info on this subject, I found this book very instructive : Moore, D. (2016). Applied survival analysis using R. New York: NY : Springer (p 58).
I find this 2d option more attractive than the 1st involving survey. Here is how I apply it :
library(survival)
cox_cem <-coxph(Surv(time=time_cem,event=status_cem)~treatment_cem, data=data_impact_cem,robust =TRUE,weights =weights)
sum_cox_cem <-summary(cox_cem)
score_test <-sum_cox_month[[13]][[1]]
score_test <- round(score_test,3)
pvalue <- sum_cox_month[[13]][[3]]
pvalue <-if(pvalue<0.001){"<0.001"} else{round(pvalue,3)}
Here is the difference between the 2 test statistics (quite close in the end).
enter image description here
Though, I still wonder why the weights option does not exist in survdiff.
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.
Since I would like to compare the survival time between treated and untreated groups for an observational data set, I used propensity score weighting method to get IPTW. After this, I want to get cumulative incidence curves (CIFs) for competing risk. I know how to get a non-weigted CIF; however, I could not find a R function for weighted CIF. Thanks for everyone.
non-weighted CIF
crr(ftime, fstatus, cov1, cov2, tf, cengroup, failcode=1, cencode=0,
subset, na.action=na.omit, gtol=1e-06, maxiter=10, init, variance=TRUE)
Regards,
Stanley
There is no R-Package which implements this method so far. I am currently workin on an R-Package that does this sort of stuff (including confidence intervals, hypothesis tests etc.), but it's still under development so I can't share my code yet.
What you could do is the following:
1.) Estimate a parametric competing risks model (Fine & Gray / Cause-Specific-Cox ...)
2.) Use the model to predict the CIF at all event times for every individual
3.) Take a weighted mean (with your IPTW) at each time point inside each of your groups of interest
This procedure is decribed in some papers. See for example Neumann (2016)
I'm a student learning forecast, a beginner in R.
I'm forecasting a seasonal data using HoltWinter, however, i want to R optimize alpha, beta and gamma based on a different criteria rather than only MSE, (maybe MAD or MAPE).
1) There is a way HoltWinters() or hw() can do this by themselves?
2) Even better, there is a way R can optimize the parameters based on a diferent function or equation that i can choose?