As you might be able to tell from the sample of my dataset, it contains a lot of dependency, with each study providing multiple outcomes for the construct I am looking at. I was planning on using the metacor library because I only have information about the sample size but not variance. However, all methods I came across to that deal with dependency such as the package rubometa use variance (I know some people average the effect size for the study but I read that tends to produce larger error rates). Do you know if there is an equivalent package that uses only sample size or is it mathematically impossible to determine the weights without it?
Please note that I am a student, no expert.
You could use the escalc function of the metafor package to calculate variances for each effect size. In the case of correlations, it only needs the raw correlation coefficients and the corresponding sample sizes.
See section "Outcome Measures for Variable Association" # https://www.rdocumentation.org/packages/metafor/versions/2.1-0/topics/escalc
Related
I am using the survey package in R to analyse the "Understanding Society" social survey. The main user guide for the survey specifies (on page 45) that the weights have been scaled to have a mean of 1. When using the svydesign() function, I am passing the weight variable to the weight argument.
In the survey package documentation, under the surveysummary() function, it states:
Note that the design effect will be incorrect if the weights have been rescaled so that they are not reciprocals of sampling probabilities.
Will I therefore get incorrect estimates and/or standard errors when using functions such as svyglm() etc?
This came to my attention because, when using the psrsq() function to get the Pseudo R-Squared of a model, I received the following warning:
Weights appear to be scaled: rsquared may be wrong
Any help would be greatly appreciated! Thanks!
No, you don't need to worry
The warning is only about design effect estimation (which most people don't want to do), and only about without-replacement design effects (DEFF rather than DEFT). Most people don't need to do design-effect estimation, they just need estimates and standard errors. These are fine; there is no problem.
If you want to estimate the design effects, R needs to estimate the standard errors (which is fine) and also estimate what the standard errors would be under simple random sampling without replacement, with the same sample size. That second part is the problem: working out the variance under SRSWoR requires knowing the population size. If you have scaled the weights, R can no longer work out the population size.
If you do need design effects (eg, to do a power calculation for another survey), you can still get the DEFT design effects that compare to simple random sampling with replacement. It's only if you want design effects compared to simple random sampling without replacement that you need to worry about the scaling of weights. Very few people are in that situation.
As a final note surveysummary isn't a function, it's a help page.
I am working on quantile forecasting with time-series data. The model I am using is ARIMA(1,1,2)-ARCH(2) and I am trying to get quantile regression estimates of my data.
So far, I have found "quantreg" package to perform quantile regression, but I have no idea how to put ARIMA-ARCH models as the model formula in function rq.
rq function seems to work for regressions with dependent and independent variables but not for time-series.
Is there some other package that I can put time-series models and do quantile regression in R? Any advice is welcome. Thanks.
I just put an answer on the Data Science forum.
It basically says that most of the ready made packages are using so called exact test based on assumption on the distribution (independent identical normal-Gauss distribution, or wider).
You also have a family of resampling methods in which you simulate a sample with a similar distribution of your observed sample, perform your ARIMA(1,1,2)-ARCH(2) and repeat the process a great number of times. Then you analyze this great number of forecast and measure (as opposed to compute) your confidence intervals.
The resampling methods differs in the way to generate the simulated samples. The most used are:
The Jackknife: in which you "forget" one point, that is you simulate a n samples of size n-1 (if n is the size of the observed sample).
The Bootstrap: in which you simulate a sample by taking n values of the original sample with replacements: some will be taken once, some twice or more, some never,...
It is a (not easy) theorem that the expectation of the confidence intervals, as most of the usual statistical estimators, are the same on the simulated sample than on the original sample. With the difference that you can measure them with a great number of simulations.
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I can try to address your question, although this is hard since you don't provide any code/data. Also, I guess by "put ARIMA-ARCH models" you actually mean that you want to make an integrated series stationary using an ARIMA(1,1,2) plus an ARCH(2) filters.
For an overview of the R time-series capabilities you can refer to the CRAN task list.
You can easily apply these filters in R with an appropriate function.
For instance, you could use the Arima() function from the forecast package, then compute the residuals with residuals() from the stats package. Next, you can use this filtered series as input for the garch() function from the tseries package. Other possibilities are of course possible. Finally, you can apply quantile regression on this filtered series. For instance, you can check out the dynrq() function from the quantreg package, which allows time-series objects in the data argument.
I have very little programming experience, but I'm working on a statistics project and would like to generate an unequal probability sample where the inclusion probability of a unit is based on its size (PPS).
Basically, I have two datasets:
ds1 lists US states and the parameter I'm trying to estimate
ds2 has the population size of each state.
My questions:
I want to use R to select a random sample from the first dataset using inclusion probabilities based on the population of each state (second dataset).
Also is there any way to use R to calculate these Generalized Unequal Probability Estimator formulas?
Also just a note on the formulas: pi_i is inclusion probability and pi_ij is joint inclusion probability.
There is a package for the same in R - pps and the documentation is here.
Also, there is another package called survey with a bit of documentation here.
I'm not sure of the difference between the two and haven't used them myself. Hope this is what you're looking for.
Yes, that's called weighted sampling. Simply set the weight to the size of the state, strictly you don't even need to normalize them by 1/sum(sizes) although it's always good practice to. There are tons of duplicate posts on SO showing how to do weighted sampling.
The only tiny complication is that you need to do a join() of the datasets ds1, ds2. Show us what code you've tried if it's causing problems. Recommend you use either dplyr or data.table.
Your second question should be asked as a separate question, and is offtopic on SO, or at least won't get a great response - best to ask statistical questions at sister site CrossValidated
I have a set of data and I'd like to know whether this data set has a logistic distribution.
When I made a histogram of my data set (see the histogram on http://imageshack.us/photo/my-images/593/histogram.png/) it seems to have a logistic distribution, but to be sure I'd like to test for a logistic distribution in R. So my question is: Is there a way to test your data for a logistic distribution and how do you do this?
Additional information: The data set consists of 8544 items. The data are horizontal distances in km between 2 geographical points.
Thanks for your attention
Sander
In R you can use the ks.test or chisq.test functions (and probably others) to test against a hypothesized distribution. Note that these tests (and others) are all rule out tests, a non-significant result does not guarentee that the data come from the given distribution, just that you cannot rule it out. Also note that with a sample size of 8544 these tests are likely to be way overpowered, meaning that it will have power to find slight meaningless differences and you are likely to reject the null hypothesis even though it is "close enough". Also the fact that you decided on a distribution based on looking at the data first could bias results.
Another approach that may give you a better feel for if a logistic distribution is "close enough" rather than exactly is to use the vis.test function in the TeachingDemos package (be sure to read the paper referenced in the help page to understand the test and what assumptions you are making).
Most importantly is understanding the science that leads to the data, does a logistic distribution make sense scientifically? what other distributions could be reasonble? Also understand what question(s) you are trying to answer with the data and what is the effect on those answers of the distribution (e.g. the CLT will let you use the normal to answer some questions, but not others, using a normal distribution even though the data comes from a logistic or something similar).
I'm working on a project now that's rather unlike anything I've done before. I have two tests with binary results that will be administered to the same sample, which is drawn from a clustered population (i.e., some subjects will be from the same family). I'd like to compare proportions of positive test results, but the clustering makes McNemar's test inappropriate so I've been reading up on alternative approaches. The two main routes seem to be 1) the clustering-adjusted McNemar alternatives by Rao and Scott (1992), Eliasziw and Donner (1991), and Obuchowski (1998), and 2) GEE.
Do you know of any implementations of the Rao-Obuchowski lineage in R (or, I suppose, SAS)? GEE is easy to find, but have you had a positive or negative experience with any particular packages? Is there another route to analyzing these data that I'm completely missing?
You could always just use a clustered bootstrap. Resample across families, which you believe are independent. That is, keep families together when you resample. Compute p2 - p1 for each sample. After 1000 iterations or so, compute the upper and bottom 2.5% quantiles. This will give you a bootstrapped 95% confidence interval. Alternatively compute the fraction of samples above zero, or whatever your hypothesis is. The procedure should have good pretty good properties unless the number of families is small.
It's probably easiest to do this by hand in R rather than relying on any package.
Check out the survey package: it is designed to take into account correlations induced by clustered sampling.
Have you already checked the CorrBin package in R?
It is for analysis of correlated binary data, there is a paper named: Using the CorrBin package for nonparametric analysis of
correlated binary data by Szabo, it includes the Rao-Scott, stochastic ordering and three versions of a GEE-based test.
The clust.bin.pair package for clustered binary matched-pair data was recently published to CRAN.
It contains implementations of Eliasziw and Donner (1991) and Obuchowski (1998), as well as two more recent tests in the same family Durkalski (2003) and Yang (2010).