similar to: Extracting information from lm results (multiple model runs)

Hello, just to follow up a question from last week. Here what I've done so far (here an example): library(MCMCpack) Y=c(15,14,23,18,19,9,19,13) X1=c(0.2,0.6,0.45,0.27,0.6,0.14,0.1,0.52) X2a=c(17,22,21,18,19,25,8,19) X2b=c(22,22,29,34,19,26,17,22) X2 <- function()runif(length(X2a), X2a, X2b) model1 <- MCMCregress(Y~X1+X2()) summary(model1) but I am not sure if my X2-function is

Hello UseRs, I've got 3 variables, the dependent variable Y as well as a max and a min value of the independent variable (Xa and Xb) where in some cases Xa=Xb (so actually a single value for X). First I'd like to perform a regression, but my problem is that my X is a range (acutally a censored independent variable Xa-Xb) rather then one single value. I know already some possible

Hello, I've a question concerning the display of interval data. A sample dataset where X is an interval between Xa and Xb which should be displayed: Y=c(15,14,23,18,19,9,19,13) Xa=c(17,22,21,18,19,25,8,19) Xb=c(22,22,29,34,19,26,17,22) X = (Xa+Xb)/2 It's easily possible to plot the mean of the interval like: plot(X,Y) afterwards I can create lines for the interval with:

If I remove all the export and change all the ' in ", it does work on SCO 3.2v5.0.4 |--------+-----------------------------> | | Roumen Petrov | | | <Roumen.Petrov at skal| | | asoft.com> | | | | | | 21/12/00 13:10 | | | |

Has anyone programmed condition indexes in R? I know that there is a function for variance inflation factors available in the car package; however, Belsley (1991) Conditioning Diagnostics (Wiley) notes that there are several weaknesses of VIFs: e.g. 1) High VIFs are sufficient but not necessary conditions for collinearity 2) VIFs don't diagnose the number of collinearities and 3) No one has

Hi, I just started with writing functions in R and so some questions popped up. I provide some values as argument to my function such as: function(a,b,c){} Now i want that the function first checks if the arguments are valid for the function. E.g argument "a" has to be a number in the range 0-1. How can that easily done? So far I have: a <- as.numeric(a) if(0 <= a &&

Q: What platform don't run this script: ---------------------------------------- #!/bin/sh export X0='x0' export X1a="$X0/1" export X1b='$X0/1' export X2a="$X1a/2" export X2b='$X1b/2' $SHELL <<EOF_2 $SHELL <<EOF_1 cat <<EOF #define a "$X2a/aa" #define b "$X2b/bb" EOF EOF_1 EOF_2

Hello, I want to use a for loop for repeadely calculating a maxent model (package dismo, function maxent()) which creates an object of the class maxent (S4). I want to collect all the resulting object in a list. I tried to simplify my for loop to explain what I want. There are two problems/questions: 1) How can I create the new variables in the loop (using paste) and assign the objects 2) How

Hi, I have a dataframe and want to remove columns from it that are populated with a similar value (for the total column) (the variation of that column is 0). Is there an easier way than to calculate the statistics and then remove them by hand? A <- runif(100) B <- rep(1,100) C <- rep(2.42,100) D <- runif(100) df <- data.frame(A,B,C,D) # if want to conditionally remove column B and

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: -------------- >

Hi, I have a 1-column dataframe with rownames and I want to sort it based on the single column. The typical procedure that is recommended in diverse posts is to use order in the index. But that "destroys" my dataframe structure. Probabaly it is a very simple solution. Here is a short reproducable example: x <- c(1,3,51,2,34,44,12,33,2,8) df <- data.frame(x) rownames(df) <-

Hi, I'd like to know if it is possible to use wildcards * for indexing... E.g. I have a vector of strings. Now I'd like to select all elements which start with A_*? I'd also need to combine that with logical operators: "Select all elements of a vector that start with A (A*) OR that start with B (B*)" Probably that is quite easy. I looked into grep() which I think might

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.

#I wish to create a "list of three dataframes" ("results2") from a "list of two dataframes" (temp) and a dataframe ("c")? #Please advise. a <- data.frame(x=c(1,2,3),y=c(5,7,9)) b <- data.frame(x=c(2,4,7,9),y=c(2,3,5,4)) c <- data.frame(x=c(22,34,7,9),y=c(52,63,5,4)) results1 <- list(a,b,c) #what I want #but this is how I need to get there

Hello, assume we have following dataframe: group <-c(rep("A",5),rep("B",6),rep("C",4)) x <- c(runif(5,1,5),runif(6,1,10),runif(4,2,15)) df <- data.frame(group,x) Now I want to select all cases (rows) for those groups which have more or equal 5 cases (so I want to select all cases of group A and B). How can I use the indexing for such questions? df[??]...

Hi, I would like to plot some extra text in my plot. This should be a two line text including a special character (sigma). I tried so far a to use expression in combination with paste and "\n"... but I can't get the line break... Here what I've done so far: plot(1,type="n", xaxt='n', yaxt='n', ann=FALSE) text(1,1,labels=expression(paste(sigma,"\n

Hello, I am doing a simple regression using lm(Y~X). As my response and my predictor seemed to be skewed and I can't meet the model assumptions. Therefore I need to transform my variables. I wanted to ask what is the preferred way to find out if predictor and/or response needs to be transformed and if yes how (log-transform?). I found a procedure in "A modern approach to Regressoin in

Hi, I'm trying to write a simple function like case1 <- function (m, cov, Q, R) { theta <- (acos(R/sqrt(Q^3))) beta <- (-2)*sqrt(Q)*cos(theta/3)+m[1]/3 rho1 <- (-2)*sqrt(Q)*cos((theta+2*pi)/3)+m[1]/3 rho2 <- (-2)*sqrt(Q)*cos((theta-2*pi)/3)+m[1]/3 stderrb <- deltamethod( ~(-2)*sqrt(Q)*cos(theta/3)+x1/3,m,cov) stderrr1 <- deltamethod(

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"

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