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

Hi, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] How do I find the estimates of the coefficients in R? And also I would like to know what technique R employs to find the estimates? Any help is appreciated. Thanks,