similar to: Comparing distributions

I am a beginner in R, so please don't step on me if this is too simple. I have two data sets datax and datay for which I created a qqplot qqplot(datax,datay) but now I want a line that indicates the perfect match so that I can see how much the plot diverts from the ideal. This ideal however is not normal, so I think qqnorm and qqline cannot be applied. Perhaps you can help? Ralf

Hi, I recently ran into an inconsistency in the way model.matrix.default handles factor encoding for higher level interactions with categorical variables when the full hierarchy of effects is not present. Depending on which lower level interactions are specified, the factor encoding changes for a higher level interaction. Consider the following minimal reproducible example: -------------- >

Hello Tyler, You write that you understand what I am saying. However, I am now at loss about what exactly is the problem with the behavior of R. Here is a script which reproduces your experiments with three variables (excluding the full model): m=expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C")) model.matrix(~(X1+X2+X3)^3-X1:X3,data=m)

Hello Tyler, I rephrase my previous mail, as follows: In your example, T_i = X1:X2:X3. Let F_j = X3. (The numerical variables X1 and X2 are not encoded at all.) Then T_{i(j)} = X1:X2, which in the example is dropped from the model. Hence the X3 in T_i must be encoded by dummy variables, as indeed it is. Arie On Thu, Nov 2, 2017 at 4:11 PM, Tyler <tylermw at gmail.com> wrote: > Hi

Hello Tyler, I want to bring to your attention the following document: "What happens if you omit the main effect in a regression model with an interaction?" (https://stats.idre.ucla.edu/stata/faq/what-happens-if-you-omit-the-main-effect-in-a-regression-model-with-an-interaction). This gives a useful review of the problem. Your example is Case 2: a continuous and a categorical regressor.

Hello Tyler, Thank you for searching for, and finding, the basic description of the behavior of R in this matter. I think your example is in agreement with the book. But let me first note the following. You write: "F_j refers to a factor (variable) in a model and not a categorical factor". However: "a factor is a vector object used to specify a discrete classification"

Dear all, this is not strictly R question. I have two vectors of different length (this is in the order of 10%). I am trying to see if still one can compare these two for similarity. IF the vectors were of the same length I would just take the difference of the two and plot a pdf of it. One way I am thinking is prorbably to find the longer length and short it in some way to get to the length of

Hi R Users, I have two vectors, x and y, of equal length representing two types of data from two studies. I would like to test if they are similar enough to use them interchangeably. No assumptions about distributions can be made (initial tests clearly show that they are not normal). Here some result: Two-sample Kolmogorov-Smirnov test data: x and y D = 0.1091, p-value < 2.2e-16 alternative

hi, i am new in this field. do favorite<http://stackoverflow.com/questions/36404707/simulation-study-of-2-sample-test-on-different-combination-of-factors#> If I wish to conduct a simulation on the robustness of two sample test by using R language, is that any ways in writing the code? There are several factors (sample sizes-(10,10),(10,25),(25,25),(25,50),(25,100),50,25),(50,100),

Hello, I once used the metawin software to perform a meta-analysis (see metawinsoft, Rosenberg et al.) and produced normal qqplot to test for a potential bias in the dataset. I now want to re-use the same dataset with the package metafor by W. Viechtbauer (great package btw). I run the qqnorm.rma.uni function. I use standardized effect sizes as in metawin. QQplot generated with metafor differs

By any chance is any one aware of a website, book, paper, etc. or combinations of those sources that show plots of different distributions? After reading a pretty good whitepaper I became aware of the benefit of I the benefit of doing Q-Q plots and histograms to help assess a distribution.   The whitepaper is called: "Univariate Analysis and Normality Test Using SAS, Stata, and SPSS*" ,

I'm looking for goodness of fit tests for gamma distributions with large data sizes. I have a matrix with around 10,000 data values in it and i have fitted a gamma distribution over a histogram of the data. The problem is testing how well that distribution fits. Chi-squared seems to be used more for discrete distributions and kolmogorov-smirnov seems that large sample sizes make it had to

Hi Everyone, I want to make 3 lines on the same graph (not as subplots, all in the same window, one on top of each other) and I want them to be quantile-quantile plots (qqplot). Essentially, I am looking for the equivalent of Matlab's "hold on" command to use with qqplot. I know I can use 'points' or 'lines', but these do not give me a qqplot (only appear to work

Hi all, I'm using R to analyze some research and I'm not sure which test would be appropriate for my data. I was hoping someone here might be able to help. Short version: Evaluate null hypothesis that change A1->A2 is similar to change C1->C2, for continuous, non-normal datasets. Long version: I have two populations A and C. I take a measurement on samples of these

Dear People, In a qqplot I am doing, I get lines/points that are very thick. I've tried setting the lwd variable to 0.1, but it doesn't seem to have any effect. Also, I have set the value of lty to dashed, but I still get dots. The command looks like qqplot(cdf.inv(seq(0,1,length=size),theta,pos,len),empmargdistvec(len,theta,pos,size), xlim=c(-theta,theta), ylim=c(-theta,theta),

Hi, We could use qqplot to see how two distributions are different from each other. To show better how they are different (departs from the straight line), how is it possible to plot the straight line that goes through them? I am looking for some thing like qqline for qqnorm. I thought of abline but how to determine the slope and intercept? Best wishes, Carol

Hi, I would like to tabulate the likelihood for an affection. For this, I retrieve indices of affected people and controls for my data set and proceed as follows: flags <- c(rep(1, length(patient_indices)), rep(0, length(control_indices))) # dataset is a data.frame and param the parameter to be analysed: data1 <- dataset[,param][c(patient_indices, control_indices)] fit1 <- glm(flags ~

Hello All, Would anyone be able to help me understand how R computes a quantile-quantile plot for comparing two data samples with unequal sample sizes? Normally, the procedure should be to rearrange the larger data sample into n equally-spaced parts using interpolation, where n is the sample size of the smaller sample, and then plot the matching data pairs. I tried using different plotting

Hello, I would like to use qqplot() to compare two experimental distributions. But I do not understand how qqplot() compute quantiles. In fact, quantile() do not return the same results. Thank you for your help. Vincent.

Dear Sir, I am comparing two samples using wilcox.test in R. Literature appears to describe mann whitney u test as the most appropriate test to use on my data. is the wilcox.test function equivalent to mann-whitney u? Is there a way to gain the U-value as apposed to the W-value in R? Thank you