similar to: QQ plotting of various distributions...

Hello ALL! Can anyone tell me how to specify qqmath() function with distribution qgamma? In help it is bit vague how to pass shape and scale parameters for gamma. Thank you in advance. Best, PM

David, Duncan, > Hi > > Following on David's rate argument > > try (with modifications of pch and grid) > > rate <- 1/4 > shape = 8 > rate = c(rep(1/4,100),rep(1/3,100)) I don't think the problem is related to the rate argument, which can well be vectorized, as is the case for a number of arguments in distrib functions in R (note that you are redefining it

dgamma and friends in S are documented as dgamma(x, shape, rate=1) pgamma(q, shape, rate=1) qgamma(p, shape, rate=1) rgamma(n, shape, rate=1) whereas R has dgamma(x, shape, scale=1, log = FALSE) pgamma(q, shape, scale=1, lower.tail = TRUE, log.p = FALSE) qgamma(p, shape, scale=1, lower.tail = TRUE, log.p = FALSE) rgamma(n, shape, scale=1) Note the use of rate vs scale. Indeed, as both S and

Hi, I have normally distributed random terms u~N(0,1). I want to get gamma distributed random terms g~(scale,shape) with E(g)=1=shape/scale and var(g)=theta=1/scale=1/shape. How can I reach my goal? The following way doesn't work: use the distribution function of u to get U(0,1)- distributed random terms, then take the quantile function of the gamma distribution with shape and scale. The

Hi, I have the following vector which is created from 3 distinct distribution (three components) of gamma: x=c(rgamma(30,shape=.2,scale=14),rgamma(30,shape=12,scale=10),rgamma(30,shape=5,scale=6)) I want to plot the density curve of X, in a way that it shows a distinct 3 curves that represent each component. How can I do that? I tried this but doesn't work: lines(density(x)) Please

Good Morning: I have attached a text file with one hundred thirty six observations. I would like to create a qq plot with the following features: 1. Observed values on the y-axis. 2. Normal approximation line on the plot. 3. X-axis with vertical reference lines at the following percentiles of the data: 1, 10, 20, 50, 80, 90 and 99. 4. Data appearing on the plot as distinct points. I assume that

Hi there, I'm a newbie, plesae bear with me. I have a dataset with about 10000 ~ 30000 data points. Would like fit to both Gamma and Normal distribution to see which one fits better. How do I do this in R? Or I could do a normality test of the data, if it's normal, I then will do a normal fit, otherwise, a gamma fit. But again, I don't know how to do this either. Please help! David

c() is used for constructing vectors. Or in other words using the method c(x,y) provides a vector of length 2 (hopefully). Therefore, you are pushing a single vector to your function and not two arguments as you want. It should be Logic_fn(x,y) not Logic_fn(c(x,y)). Furthermore, i would recommend reading up on how function constructs work. Two return statements within a function are redundant

Hi, That was very useful information. Thanks. But still I am not able to get the desired output with updated code. Kindly help if you have any further thoughts ... Updated code : x <- 0 y <- 0 Logic_fn <- function(x,y){ print("Passed Values") print(x) print(y) x <- x + 1 y <- y + 1 print("After addition :") print(x) print(y) test_data <- rbind(x,y)

Hi, Can someone please help me on below issue I am facing : I am trying to play with returning a dataframe+2 variables using a fn. But facing an issue : Error in Logic_fn(c(x, y)) : argument "y" is missing, with no default This is the code I am using : x <- 0 y <- 0 Logic_fn <- function(x,y){ x <- x + 1 y < y + 1 test_data <- rbind(x,y) test_data <-

The returned values are in the list you assign to test_data, the original x and y are not modified, i.e the returned value for x will be test_data[[1]] and for y will be test_data[[2]]. Using the same variable names in the function and test is perhaps what is leading to confusion. > On 28 Jul 2017, at 09:13, Vijaya Kumar Regati <VijayaKumar.Regati at m3bi.com> wrote: > > Hi, >

Dear R helpers, I generate a qq plot using the following function call. qqmath(~val|ind,data=xx ,distribution=function(p) qt(p,df=19) ,ylab="Sample Quatinles" ,xlab="Theoretical Quantiles" ,aspect=1 ,prepanel = prepanel.qqmathline ,panel=function(x,y) { panel.qqmathline(y, distribution=function(p) qt(p,df=19),col=2)

On a recent FreeBSD 8.0-CURRENT (i386) building R (any version) breaks with the following messages: ---------------------------------------------------------------------- [...snip...] gcc -std=gnu99 -I. -I../../src/include -I../../src/include -I/usr/local/include -DHAVE_CONFIG_H -g -O2 -c wilcox.c -o wilcox.o gcc -std=gnu99 -I. -I../../src/include -I../../src/include -I/usr/local/include

I have a dataset that has many columns which are NA or constant, and so I remove them like so: same <- sapply(dataset, function(.col){ all(is.na(.col)) || all(.col[1L] == .col) }) dataset <- dataset[!same] This works GREAT (thanks to the r-users list archive I found this) however, then when I do my data sampling like so: testSize <- floor(nrow(x) * 10/100) test <-

SuppDists is orphaned on CRAN (and has been since 2013). https://cran.r-project.org/web/checks/check_results_.html Oddly, the simulate method for the inverse.gaussian family [inverse.gaussian()$simulate] depends (in a loose sense) on SuppDists (it fails if the SuppDists namespace is not available: if (!requireNamespace("SuppDists", quietly = TRUE)) stop("need CRAN

Hi All, This might be simple question, I need to retrive data for modelling from the databases. Eveytime date values changes so I countnot fix date value in the code, it is required to pass as parameter. When I pass the date as parameter, it throws error. (ERROR: column "start_dt" does not exist Position: 285) My script is as below, please guide me where am I going wrong? All parameters

I haven't looked inside to see what is causing this, but there's a big discontinuity in qgamma: curve(qgamma(x, shape=19), from=1e-10, to=2e-10) This appears in both R-patched and R-devel. Duncan Murdoch

On 2019-09-25 3:26 a.m., Martin Maechler wrote: >>>>>> Ben Bolker >>>>>> on Tue, 24 Sep 2019 20:09:55 -0400 writes: > > > SuppDists is orphaned on CRAN (and has been since 2013). > > https://cran.r-project.org/web/checks/check_results_.html > > > Oddly, the simulate method for the inverse.gaussian family > >

Hi all, I am wondering in R, suppose I did the principal component analysis on training data set and obtain the rotation matrix, via: > pca=prcomp(training_data, center=TRUE, scale=FALSE, retx=TRUE); Then I want to rotate the test data set using the > d1=scale(test_data, center=TRUE, scale=FALSE) %*% pca$rotation; > d2=predict(pca, test_data, center=TRUE, scale=FALSE); these two

Doese anyone know a R function to find the median of a gamma distribution? [[alternative HTML version deleted]]