similar to: configure.in: Someone please show me a better way :)

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 | | | |

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

My apologies, I forgot to CC: to the list on my previous communication with Daniel. Jesse ----- Forwarded by Jesse Albert Canchola/EMVL/DIAG/US/BAYER on 08/21/2006 10:50 AM ----- Jesse Albert Canchola/EMVL/DIAG/US/BAYER 08/21/2006 09:36 AM To "Daniel Nordlund" <res90sx5 at verizon.net> cc Subject RE: [R] Permutations with replacement Thanks, Daniel. I need to enumerate

Just to inform: I posted that before in R-sig-ecology but as it might be interesting also for other useRs, I post it also to the general r-user list: Hello Alexandre, thank you very much. I also found another way to extract summarizing information from lm results over e.g. 1000 repeated model runs: results2 <- t(as.data.frame(results)) summary(results2) Although some questions popped up in

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

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

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.

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

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"

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, 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, 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)

Dear r-helpers, I'm looking for a way of sorting variable names in a "natural" order, when the names are composed of digits and other characters. I know that this is a vague idea, and that sorting character strings is a complex topic, but perhaps a couple of examples will clarify what I mean: > s <- c("x1b", "x1a", "x02b", "x02a",

Hello, We have a nodejs app which is injecting first boot scripts using virt-customize however the exact same commands are working when triggered manually. Here is the debug output of the commands Regards, Peter [ 0.0] Examining the guest ... libguestfs: trace: set_network true libguestfs: trace: set_network = 0 libguestfs: trace: add_drive

Am 13.03.19 um 18:50 schrieb Rowland Penny via samba: > Try running 'samba-tool ldapcmp ldap://dc1 ldap://dc2' thanks! I get differences, too many to post here, but I assume mostly related to the drift between the 2 dcs now? (lastLogonTimestamp seems obvious to me) What might solve the initial problem *maybe* * Comparing [DNSFOREST] context... * Objects to be compared: 19

I have attached a text file representing the centralized amplitude of a signal, sampled at 30Hz, whose length N = 6922 My goal is to remove the trend. I am using package "simsalabim". I ran command decompSSA with L = length(Amps)/5 The reason is that I have SSA/MTM toolkit running in Mac/OS. SSA/MTM documentation, relative to SSA, recommends that N/10 <= L <= N/5.

Hi Arie, Thank you for your further research into the issue. Regarding Stata: On the other hand, JMP gives model matrices that use the main effects contrasts in computing the higher order interactions, without the dummy variable encoding. I verified this both by analyzing the linear model given in my first example and noting that JMP has one more degree of freedom than R for the same model, as

Hi Arie, The book out of which this behavior is based does not use factor (in this section) to refer to categorical factor. I will again point to this sentence, from page 40, in the same section and referring to the behavior under question, that shows F_j is not limited to categorical factors: "Numeric variables appear in the computations as themselves, uncoded. Therefore, the rule does not

Hi Arie, I understand what you're saying. The following excerpt out of the book shows that F_j does not refer exclusively to categorical factors: "...the rule does not do anything special for them, and it remains valid, in a trivial sense, whenever any of the F_j is numeric rather than categorical." Since F_j refers to both categorical and numeric variables, the behavior of

I think it is not a bug. It is a general property of interactions. This property is best observed if all variables are factors (qualitative). For example, you have three variables (factors). You ask for as many interactions as possible, except an interaction term between two particular variables. When this interaction is not a constant, it is different for different values of the remaining