lowess and loess are algorithms and software programs created by William Cleveland. lowess is for adding a smooth curve to a scatterplot, i.e., for univariate smoothing. loess is for fitting a smooth ...

That's an interesting question. My research group has been using the distribution you refer to for some years in our publicly available bioinformatics software. As far as I know, the distribution does ...

No, but you can conclude that the probability of any shared events is zero. Disjoint means that $A_i \cap A_j=\emptyset$ for any $i\ne j$. You cannot conclude that, but you can conclude that $P(A_i \... View answer Accepted answer 24 votes In applied statistics, chisquared test statistics arise as sums of squared residuals, or from sums of squared effects or from log-likelihood differences. In all of these applications, the aim is to ... View answer 17 votes Nothing went wrong. The adjusted p-values are correct. Adjusted$p=1$simply means no evidence at all for rejecting the null hypothesis. However p.adjust(data2$raw.p, method = "holm") is always ...

As Ben points out, you've made an algebraic error and the result is correct. This process is called binomial thinning and, if you search for that expression, you'll find many mentions of it in the ...

In my opinion, the source that you link to is wrong in that it is confusing conditioning with assumptions. Fisher's exact test conditions on the margin totals, meaning that it does not use any ...

The "e" is a symbol for base-10 scientific notation. The "e" stands for $\times 10^{\rm exponent}$. So -1.861246e-04 means $-1.861246 \times 10^{-4}$. In fixed-point notation that would be -0....

Are you familiar with generalized linear models in R? If so, you can fit Tweedie glms just like any other glms. The glm family definition necessary to make this happen is provided by the statmod R ...

In some cases, y is equal to the same value (example 1) for all observations. Theoretically, the model should not converge. Nonsense. This is a very simple dataset for which the maximum likelihood ...

You need to adjust X as well as Y for the confounder The first approach (using multiple regression) is always correct. Your second approach is not correct as you have stated it, but can be made ...

Predicting the proportion of zeros I am the author of the statmod package and joint author of the tweedie package. Everything in your example is working correctly. The code is accounting correctly ...

No, given a multiple regression, there is no way to compute R-squared while avoiding the bulk of the other computations. You can certainly avoid computing the coefficients themselves, but the main ...

Unfortunately, the Wikipedia article on "F-test of equality of variances" is incorrect. When the variances are unequal, the distribution of $F$ is neither $F$ nor non-central $F$, it is simply scaled $... View answer Accepted answer 10 votes What you are seeing is exactly what one would expect. The condition number of your matrix$X$is about$10^6$. Double precision floating point calculations give about 18 figures of accuracy so, in ... View answer Accepted answer 10 votes I think you have misunderstood the motivation for Chebyshev polynomials. Chebyshev polynomials are not used for statistical modelling at all --- their purpose is quite different. Chebyshev polynomials ... View answer Accepted answer 10 votes This is a term that is specifically from empirical Bayes (EB), in fact the concept that it refers to does not exist in true Bayesian inference. The original term was "borrowing strength", which was ... View answer Accepted answer 10 votes Actually it isn't -- the statistical method is not called adonis. The authors of the vegan software package for R (http://vegan.r-forge.r-project.org) implemented the statistical procedure you refer ... View answer 10 votes I agree that coloring the quadrants pink is largely cosmetic, but overall I view this as a clear informative plot. The message is immediately apparent and is not misleading. The BBC has plotted the ... View answer Accepted answer 10 votes Actually, the link you give leads to just two files. The first is a pdf file, as I'm sure you know. The second is a zip file containing an R package. You are supposed to unzip the file, then copy the ... View answer Accepted answer 9 votes The general derivation of the deviance for a GLM family is given in Section 5.4 of Dunn and Smyth (2018) (the book that you mentioned in a previous post). You can insert the form of the gamma density ... View answer 9 votes Nothing is wrong with the formula No, it is not correct that the estimated variance of$\hat\beta$is zero. There are a couple of errors in your reasoning. First, the MSE is NA rather than 0. The ... View answer Accepted answer 8 votes In mathematics, you can use any notation you like as long as you clearly define the symbols you use and the resulting notation is unambiguous to a reader. Having said that, it will generally be easier ... View answer 8 votes The hypothesis test functions in the stats package use classic S3 object-orientated programming. You write a function that creates a "htest" object, which is a list with a standard set of components, ... View answer 8 votes No, a Tweedie GLM assumes that the responses follow a Tweedie distribution so, obviously, neither the data nor the ordinary residuals are expected to follow a normal distribution. No, a Shapiro test ... View answer 8 votes Your code will run and give a correct answer, but it is written in Fortran-like style. As R code, it is spectacularly inefficient because you're not making use of R's vectorizations. Your code is ... View answer Accepted answer 8 votes You have chosen to do a one-sided test and, obviously, order is important in a one-sided test. Your first call to fisher.test is testing the null hypothesis Pct1 = Pct2 vs the alternative that Pct1 &... View answer Accepted answer 7 votes I think you are trying to rediscover the G-test or likelihood ratio test. Your$H$is a scaled version of the G statistic, which is defined as $$G=2\sum_{i=1}^m O_i \log(O_i/E_i)$$ with$E_i=n/m$.$G\$ ...

In statistical likelihood theory, minus the second derivative of the log-likelihood function is called the observed information. We might write this as $$I = -\ddot \ell(y; \theta)$$ where the dots ...