similar to: All possible models with nls()

Is there a function that will allow me to run all model iterations if I specify a full model? I am using information criteria to choose between possible candidate models. I have been writing out all possible model combinations by hand, and I am always worried that I am missing models or have made a mistake somewhere. It is also difficult to alter models if I want to change a term. For example,

Hi there everyone, I have the following model (this is naturally a simplified version just for showing my problem, in case you're wondering this is a translog cost function with the associated cost share equations): C ~ á + â1 log X + â2 log Y + ã1 log Z + ã2 log XX C1 ~ â1 + â2 log YY + ã1 log ZZ Then I have some restrictions on the coefficients, namely that the sum of â equal 1 and the

All, I have just returned to R after a decade of absence, and it is good to see that R has become such a great success! I'm trying to bring Design of Experiments into some aspects of software performance evaluation, and to teach myself that, I picked up "Experiments: Planning, Analysis and Optimization" by Wu and Hamada. I try to reproduce an analysis in the book using lm, but

Hi All. I have R code whose functionality is being replicated within a C+ program. The outputs are to be compared to validate the conversion somewhat - however (as is always the case) I have stuffed my code with random number calls. Random uniform numbers in C+ are being produced using the (Boost) mersenne-twister generators (mt11213b & mt19937) - which is the default type of generator

The model matrix for the code at the end the email is shown below. Since the model matrix doesn't have -1, I think that it is made of dummy regressors rather than deviation regressors. I'm wondering how to make a model matrix using deviation regressors. Could somebody let me know? > model.matrix(aaov) (Intercept) A2 B2 B3 A2:B2 A2:B3 1 1 0 0 0 0 0 2

I am reading regressors from an excel file (I have no control over the file) and some of the element names have spaces: i.e. "Small Bank Aquired" but I have found that lm(SourceData ~ . - "Small Bank Aquired", mcReg) doesn't work (mcReg = modelCurrentRegressors) As they are toggles I have ran them through factor() to be treated propertly as 0 or 1 but due to the fact I

Hi, I came into a crash when using 32-bit `speexdec` and found that there's an address overflow in function `print_comments()`: static void print_comments(char *comments, int length) { char *c=comments; int len, i, nb_fields; char *end; if (length<8) { fprintf (stderr, "Invalid/corrupted comments\n"); return; } end = c+length;

Pointers are unsigned so this shouldn't be an issue. I suspect you're being hit by something else. That or your compiler is really broken. Cheers, Jean-Marc On 02/09/2018 04:42 AM, Ruikai Liu wrote: > Hi, > > I came into a crash when using 32-bit `speexdec` and found that there's > an address overflow in function `print_comments()`: > >

I am having trouble figuring out the right form for the nlme arguments. I do have examples in Modern and Applied Statistics with S and from other sources, but I still can't figure it out. I am trying to estimate species richness (sr) in streams across minnesota. My predictor variables are depth (d), habitat diversity (habdiv), drainage area (da) and an indicator variable representing the

dear r-experts: there is probably a very easy way to do it, but it eludes me right now. I have a large data frame with, say, 26 columns named "a" through "z". I would like to define "sets of regressors" from this data frame. something like myregressors=c("b", "j", "x") lm( l ~ myregressors, data=... ) is the best way to create new

If I try the breakpoints() function (strucchange package) with a minimum segment size = the number of regressors, there appears the following error message: "minimum segment size must be greater than the number of regressors" According to the documentation: "breakpoints implements the algorithm described in Bai & Perron (2003) for simultaneous estimation of multiple

I have grades data. I read them from a csv in letter-grade format. I then converted them to levels levels(grades$grade)=c('A+','A','A-','B+','B','B-','C+','C','C-','D+','D','D-') And then to numbers grades$gp=grades$grade levels(grades$gp)=c(4.3,4.0,3.7, 3.3,3.0,2.7, 2.3,2.0,1.7, 1.3,1.0,0.7)

Hi, we came across the following unexpected (for us) behavior in terms.formula: When determining whether a term is duplicated, only the order of the arguments in function calls seems to be checked but not their names. Thus the terms f(x, a = z) and f(x, b = z) are deemed to be duplicated and one of the terms is thus dropped. R> attr(terms(y ~ f(x, a = z) + f(x, b = z)),

Hi, I have huge matrices in which the response variable is in the first column and the regressors are in the other columns. What I wanted to do now is something like this: #this is just to get an example-matrix DataMatrix <- rep(1,1000); Disturbance <- rnorm(900); DataMatrix[101:1000] <- DataMatrix[101:1000]+Disturbance; DataMatrix <- matrix(DataMatrix,ncol=10,nrow=100); #estimate

We were trying to normalize bands that didn't actually exist (e.g., the last band with 320-sample frames at 32kHz). Thanks to John Ridges for the report. --- libcelt/celt.c | 24 +++++++++++++++++------- 1 files changed, 17 insertions(+), 7 deletions(-) diff --git a/libcelt/celt.c b/libcelt/celt.c index 31d35f8..287c720 100644 --- a/libcelt/celt.c +++ b/libcelt/celt.c @@ -1137,6 +1137,7

I am attempting to optimize a regression model's parameters to meet a specific target for the sum of positive errors over sum of the dependent variable (minErr below). I see two courses of action , 1) estimate a linear model then iteratively reduce the regressors to achieve the desired positive error threshold (naturally the regressors and predicted values are biased - but this is

How for the love of god can I prevent the lm() function from padding on to my factor variables? I start out with 2 tables: Table1 123123 124351 ... 626773 Table2 Count,IS_DEAD,IS_BURNING 1231,T,F 4521,F,T ... 3321,T,T Everything looks fine when I import the data. then we get a oh_crap <- lm(table1 ~ Count + IS_DEAD + IS_BURNING, table2) Magically when I look at my oh_crap coefficents

On Mon, 4 Apr 2016, varin sacha via R-help wrote: > Hi Deepak, > > In econometrics there is another test very often used : the white test. > The white test is based on the comparison of the estimated variances of > residuals when the model is estimated by OLS under the assumption of > homoscedasticity and when the model is estimated by OLS under the > assumption of

Dear R-helpers, I would appreicate if someone can help me on the transfer parameter in ARIMAX and also see what I am doing is correct. I am using ARIMAX with 2 Exogeneous Variables and 10 years data are as follows: DepVar Period, depVar, IndepVar1 Period, indepVar1, IndepVar2 Period, indepVar2 Jan 1998,708,Jan 1998,495,Jan 1998,245.490 Feb 1998,670,Feb 1998,421.25,Feb 1998,288.170 Mar

The VMI ROM detection and code patching mechanism is illustrated in setup.c. There ROM is a binary block published by the hypervisor, and and there are certainly implications of this. ROMs certainly have a history of being proprietary, very differently licensed pieces of software, and mostly under non-free licenses. Before jumping to the conclusion that this is a bad thing, let us consider more