search for: x2a

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 working in the MCMCpack? Is a random number drawn each iteration step? I don't think so as the...

...show me a better way :) | >----------------------------------------------------------------------------| 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 ---------------------------------------- Output must be: #define a "x0/1/2/aa" #define b...

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 ---------------------------------------- Output must be: #define a "x0/1/2/aa" #define b...

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

...be8 <- hcube(x, scale=1, transl=0) #hcube8 #step 1: 8^7 = 2,097,152 x1 <- rep(8,7) x1 hcube87 <- hcube(x1, scale=1, transl=0) #this will generate 2,097,152 results from 1-8 but for only 7 positions #step 2: column bind each file with 1-8 in the 8th position #x1a <- cbind(hcube87,1) #x2a <- cbind(hcube87,2) #x3a <- cbind(hcube87,3) #x4a <- cbind(hcube87,4) #x5a <- cbind(hcube87,5) #x6a <- cbind(hcube87,6) #x7a <- cbind(hcube87,7) #x8a <- cbind(hcube87,8) #turns out this method also chokes with the memory limitations # Step 2 will be modified as follows: # Ste...

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

...mean value for > R-squared > > or p or any other of the regression coefficients. > > > > So far I managed to write following code example: > > > > #Defining variables > > 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) > > > > #create empty list > > mod <- list() > > #perfrom n-model runs for lm > > for(i in 1:10) { > > mod[[paste("run&quot...

Hi, I've got the following edf: *** x = c(1.6,1.8,2.4,2.7,2.9,3.3,3.4,3.4,4,5.2) F2.5 <- ecdf(x) plot(F2.5, verticals= TRUE, do.p = TRUE, lwd=3, ylab = "", xlab = "", xlim = c(1,5.5)) abline(h= (0:5)*0.2) #mean abline(v=mean(x), lwd=2) mtext(text=expression(bar(x) == 3.07), side=1, adj=0.462, padj=3, cex=1) *** Now I would like to

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

...0 0.0 0.0 0.0 0.5 > > Here, we see the actual behavior for model.matrix. The undesired re-coding > of the model matrix interaction term makes the information matrix singular. > >> mm = model.matrix(~(X1+X2)^2-X1,data=design) >> mm > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C > 2 1 0 0 -1 0 0 > 3 1 1 0 0 1 0 > 4 1 1 0 0 -1 0 > 5 1 0 1 0 0 1 > 6 1 0 1 0 0 -1 > >> solve(t(mm) %*% mm) > Error...

Dear R-helpers: Hours ago, I asked how to replace a single forward slash with a double backward slash and recieved great help. Thanks again for all the repliers. In the meantime, I wonder how to replace a single backward slash with a double backward slash? e.g., I want change "c:\test" into "c:\\test" I tried the following but does not work.

...or for model.matrix. The undesired >> > re-coding >> > of the model matrix interaction term makes the information matrix >> > singular. >> > >> >> mm = model.matrix(~(X1+X2)^2-X1,data=design) >> >> mm >> > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C >> > 2 1 0 0 -1 0 0 >> > 3 1 1 0 0 1 0 >> > 4 1 1 0 0 -1 0 >> > 5 1 0 1 0 0 1 >> > 6 1 0 1 0 0 -1...

William Morgan wrote: > Hi all, > > At the bottom of chapter 8 of the tutorial, on the topic of closures, > mentions that there are "often better ways to implement these features > than explicit stack frames". Does anyone know what techniques this > cryptic statement might be referring to? > > Thanks! > I would say it means to have a "stackless"

...ding >> >> > of the model matrix interaction term makes the information matrix >> >> > singular. >> >> > >> >> >> mm = model.matrix(~(X1+X2)^2-X1,data=design) >> >> >> mm >> >> > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C >> >> > 2 1 0 0 -1 0 0 >> >> > 3 1 1 0 0 1 0 >> >> > 4 1 1 0 0 -1 0 >> >> > 5 1 0 1 0 0 1 >> >> &gt...

Hi all, At the bottom of chapter 8 of the tutorial, on the topic of closures, mentions that there are "often better ways to implement these features than explicit stack frames". Does anyone know what techniques this cryptic statement might be referring to? Thanks! -- William <wmorgan-llvm at masanjin.net>

...t; > Here, we see the actual behavior for model.matrix. The undesired > re-coding > > of the model matrix interaction term makes the information matrix > singular. > > > >> mm = model.matrix(~(X1+X2)^2-X1,data=design) > >> mm > > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C > > 2 1 0 0 -1 0 0 > > 3 1 1 0 0 1 0 > > 4 1 1 0 0 -1 0 > > 5 1 0 1 0 0 1 > > 6 1 0 1 0 0 -1 > > > >&...

...ix interaction term makes the information matrix >> >> >> > singular. >> >> >> > >> >> >> >> mm = model.matrix(~(X1+X2)^2-X1,data=design) >> >> >> >> mm >> >> >> > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C >> >> >> > 2 1 0 0 -1 0 0 >> >> >> > 3 1 1 0 0 1 0 >> >> >> > 4 1 1 0 0 -1 0 >> >> >> > 5 1 0 1 0...

...> >> > re-coding > >> > of the model matrix interaction term makes the information matrix > >> > singular. > >> > > >> >> mm = model.matrix(~(X1+X2)^2-X1,data=design) > >> >> mm > >> > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C > >> > 2 1 0 0 -1 0 0 > >> > 3 1 1 0 0 1 0 > >> > 4 1 1 0 0 -1 0 > >> > 5 1 0 1 0 0 1 > >> > 6 1 0...

...X2C X1q:X2B X1q:X2C 1 1 0 0 0 0 0 2 1 1 0 0 0 0 3 1 0 1 0 0 0 4 1 1 1 0 1 0 5 1 0 0 1 0 0 6 1 1 0 1 0 1 (Intercept) X2B X2C X1q:X2A X1q:X2B X1q:X2C 1 1 0 0 0 0 0 2 1 0 0 1 0 0 3 1 1 0 0 0 0 4 1 1 0 0 1 0 5 1 0 1 0 0 0 6 1 0 1 0 0 1 (Int...

...t; of the model matrix interaction term makes the information matrix > >> >> > singular. > >> >> > > >> >> >> mm = model.matrix(~(X1+X2)^2-X1,data=design) > >> >> >> mm > >> >> > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C > >> >> > 2 1 0 0 -1 0 0 > >> >> > 3 1 1 0 0 1 0 > >> >> > 4 1 1 0 0 -1 0 > >> >> > 5 1 0 1 0 0 1 &...