similar to: General query regarding scoring new observations

Hello folks, I am on learning phase of R. I have developed Regression Model over six predictor variables. while development, i found my all data are not very linear. So, may because of this the prediction of my model is not exact. Here is the summary of model : Call: lm(formula = y ~ x_1 + x_2 + x_3 + x_4 + x_5 + x_6) Residuals: Min 1Q Median 3Q Max -125.302

Dear R-users, i try to solve to following linear programm in R 0 * x_1 + 2/3 * x_2 + 1/3 * x_3 + 1/3 * x_4 = 0.3 x_1 + x_2 + x_3 + x_4 = 1 x_1, x_2, x_3, x_4 > 0, x_1, x_2, x_3, x_4 < 1 as you can see i have no objective function here besides that i use the following code. library(lpSolve) f.obj<-c(1,1,1,1) f.con<-matrix(c(0,2/3,1/3,1/3, 1,1,1,1,

I have a simple system of linear equations to solve for X, aX=b: > a [,1] [,2] [,3] [,4] [1,] 1 2 1 1 [2,] 3 0 0 4 [3,] 1 -4 -2 -2 [4,] 0 0 0 0 > b [,1] [1,] 0 [2,] 2 [3,] 2 [4,] 0 (This is ex Ch1, 2.2 of Artin, Algebra). So, 3 eqs in 4 unknowns. One can easily use row-reductions to find a homogeneous solution(b=0) of: X_1

Dear all, Is there any method in R to find all possible nonnegative integer solutions to the linear equation with unit coefficients as follow: X1+X2+...+Xk=N Thank you, Amin Zollanvari

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

Hi All Apologies if this is not the correct list for this question. The Rglpk package offers the following example in its documentation library(Rglpk) ## Simple mixed integer linear program. ## maximize: 3 x_1 + 1 x_2 + 3 x_3 ## subject to: -1 x_1 + 2 x_2 + x_3 <= 4 ## 4 x_2 - 3 x_3 <= 2 ## x_1 - 3 x_2 + 2 x_3 <= 3 ## x_1, x_3 are non-negative integers ## x_2 is a non-negative real

Dear List: I cannot seem to find a way to plot my data correctly. I have a small data frame with 6 total variables (x_1 ... x_6). I am trying to plot x_1 against x_2 and x_3. I have tried plot(x_2, x_1) #obviously works fine plot(x_3, x_1, add=TRUE) # Does not work. I keep getting error messages. I would also like to add ablines to this plot. I have experimented with a number of other

I noticed that there is no generalised additive model functions in R (1.1.1) ... is there a package that implements them? Thanks Prasad ***************************************************************** Mr. Anantha Prasad, Ecologist/GIS Specialist USDA Forest Service, 359 Main Rd. Delaware OHIO 43015 USA Ph: 740-368-0103 Email: aprasad at fs.fed.us Web:

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

On Thu, Aug 14, 2014 at 6:37 AM, Daniel Berlin <dberlin at dberlin.org> wrote: > On Wed, Aug 13, 2014 at 8:35 PM, Jeremy Salwen <jeremysalwen at gmail.com> wrote: >> Hey Daniel, >> >> Thanks again for the help. I'm still a bit confused about the interface to >> the alias analysis. It seems like we are talking about different >> interfaces. >

Hi Daniel, Sorry for taking so long to respond. I spoke with a colleague more familiar with llvm who thought he could clear up my confusion, but we both came out of the conversation confused. I will try my best to explain the ambiguity. In an DAG, alias queries would be completely unambiguous. Every instruction would only be executed once, and every SSA value really would have a single static

Hi Hal, Thank you for your email, that makes a lot of sense to me. I am working on some tools to use memory profiling to speculatively replace memory loads and stores with value forwarding in hardware implementations. I'd like to compare the profiled data to static alias analysis, so it would be super useful if there was a way to answer the questions about aliasing across backedges that

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Thanks Daniel! I think you've cleared up some of my misconceptions, but I still am a bit confused about some of the corner cases. Suppose we had something like this std::vector<int> A(100); > int* x,y; > x=&A[0]; > for(int i=0; i<100; i++) { > y=&A[i]; > *y=*x; > x=&A[i+1]; > } > Would the load and store instructions be MustAlias? I

Hi everybody! I'm looking some way to do in R a polynomial fit, say like polyfit function of Octave/MATLAB. For who don't know, c = polyfit(x,y,m) finds the coefficients of a polynomial p(x) of degree m that fits the data, p(x[i]) to y[i], in a least squares sense. The result c is a vector of length m+1 containing the polynomial coefficients in descending powers: p(x) = c[1]*x^n +

On 04/11/2017 10:20 PM, Daniel Nordlund wrote: > Tirthankar, > > "random number generators" do not produce random numbers. Any given > generator produces a fixed sequence of numbers that appear to meet > various tests of randomness. By picking a seed you enter that sequence > in a particular place and subsequent numbers in the sequence appear to > be unrelated.

Dear R-Users, I need to find a smooth function f() and coefficients a_i that give the best fit to y ~ a_0 + a_1*f(x_1) + a_2*f(x_2) Note that it is the same non-linear transformation f() that is applied to both x_1 and x_2. So my first question is how can I do it in R? A more general question is this: suppose I have a utility function U(a_i, f()), where f() is say a spline. Is there a general

Hi all, I'd like to fit a multivariate regression with the variance of the error term porportional to the predictors, like the WLS in the univariate case. y_1~x_1+x_2 y_2~x_1+x_2 var(y_1)=x_1*sigma_1^2 var(y_2)=x_2*sigma_2^2 cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 How can I specify this in R? Is there a corresponding function to the univariate specification lm(y~x,weights=x)??

Dear All, I am wondering on how to use the abline.xyplot with xyplot such that I will have different vertical lines for each panel. More sepcifically, suppose that the xyplot generates 4 panels defined by the combination of two binary variables: X_1 and X_2. i.e. xyplot(Y ~ Z | X_1*X_2, data = df) I want something like: abline(v = 5) if X_1=0 and X_2 = 0 abline(v =