similar to: extract the p value of F statistics from the lm class

Hello, I have the following problem when trying to use rm: In a top level script file I have a loop iterating over some index. The loop is not contained within a function, so the scope of variables declared in the loop is global. Within this loop I generate several variables which should be removed at the end of each iteration. To do this, I wrote a function to clean up the workspace. An example

On 07/29/2013 06:28 AM, Matt Fleming wrote: > On Fri, 26 Jul, at 09:49:28AM, H. Peter Anvin wrote: >> Hmm... this might constrain the heap excessively if the SMT_TERMINAL >> cutoff is at the wrong place (because there will be just enough SMT_FREE >> to fit.) I'm wondering if we shouldn't use the highest of these two >> regions. > > Could you give an

hi, either function call in this code makes llc (targeting SPU) assert: declare i8 @return_i8() declare i16 @return_i16() define void @testfunc() { %rv1 = call i8 @return_i8() %rv2 = call i16 @return_i16() ret void } Attached is a patch to fix it, and remove some related redundant code. Does this rather trivial fix warrant a test case? kalle -------------- next part -------------- A

Hello, I'm seeing a miscompilation from the following code: declare <4 x float>* @getPtr() define <4 x float> @func() { %rv1 = call <4 x float>* @getPtr() %rv2 = call <4 x float>* @getPtr() %rv3 = load <4 x float>* %rv1 ret <4 x float> %rv3 } The load ends up loading from the pointer returned by the 2nd function call. I traced down the problem to

Dear list, I want to retrieve the p-value of a two-polynomial regression. For a one-polynomial lm I can easily do this with: summary(lm(b~a, data=c)[[4]][[8]]. But how do I find the final p-value in the two-polynomial regression? Under $coefficients I don't find it Any suggestions? Patrick alt <-(2260,2183,2189,1930,2435, 2000,2100,2050,2020,2470, 1700,2310,2090,1560,2060,

OK, what is the trick to extracting the overall p value from an lm object? It shows up in the summary(lm(model)) output but I can't seem to extract it: > test2 = apply(aa, 1, function(x) summary(lm(x[,1] ~ 0 + x[,3] + x[,6]))) > test2[[1]] Call: lm(formula = x[, 1] ~ 0 + x[, 3] + x[, 6]) [omitted summary output] F-statistic: 40.94 on 2 and 7 DF, p-value: 0.0001371 It does not seem

Hi, I'm trying to get the p-value from the 'lm' regression function as a list object. For example, I can get r^2 from the following code by entering summary(fm)$r.squared. Is there a way to get the p-value? If not, is there a function where I can enter the f-value and degrees of freedom to get the p-value? Thanks. x <- c(1,2,3,4,5,6,7,8,9,10) y <- c(1,2,3,4,4,5,6,8,1,9) fm

could someone explain why the name of FPVAL gets " .value" concatenated onto it when the code below is run and temp is returned. I've been trying to figure this out for too long. It doesn't matter when I put the FPVAL in the return statement. It happens regardless of whether it's first or last. Thanks. f.lmmultenhanced <- function(response, pred1, pred2) {

Dear Sir or Madam, I am a student at MSc Probability and Finance at Paris 6 University/ Ecole Polytechnique. I am using R and I can't find an answer to the following question. I will be very thankful if you can answer it. I have two vectors rendements_CAC40 and rendements_AlcatelLucent. I use the lm function as follows, and then the sumarry function: regression=lm(rendements_CAC40 ~

Dear useRs, i have vectors of about 27 descriptors, each having 703 elements. what i want to do is the following 1. i want to do regression analysis of these 27 vectors individually, against a dependent vector, say B, having same number of elements.2. i would like to know best 10 regression results, if i do regression analysis of dependent vector against the random combination of any 4

Dear all, I am not fluent in R and am struggling to 1) apply a lm to a weight-size dataset, thus the model has to run separately for each species, each year; 2) extract coefs, r-squared, n, etc. The data look like this: year sps cm w 2009 50 16 22 2009 50 17 42 2009 50 18 45 2009 51 15 45 2009 51 16 53 2009 51 17 73 2010 50 15 22 2010 50 16 41 2010 50 16 21 2010

inputfille snpid indid genotype gvariable probeid gene geneexpression rs1040480 CHB_NA18524 C/T 2 GI_19743926-I PTPRT 5.850586 rs1040480 CHB_NA18526 C/C 1 GI_19743926-I PTPRT 6.028641 rs1040480 CHB_NA18529 C/C 3 GI_19743926-I PTPRT 5.944392 rs1040481 CHB_NA18532 C/C 1 GI_19743926-I PTPRT 5.938578 rs1040481 CHB_NA18537 C/C 2 GI_19743926-I PTPRT 5.874439 rs1040481 CHB_NA18540 C/C 3 GI_19743926-I

Hi there, I tried it many times but didn't get it worked. I just want to export the summary of a OLS regression (lm() function) into a csv-file including the "call"-formula", "coefficients", "r-squared", " adjusted r-squared" and "f statistic". I know I can export: write.csv2(Regression_60d_ann$coefficients,

a1 is the first key in input. second column is x-axis and 3rd is y-axis and 4th is its corresponding key. Now for every key in 1st column I would like to calculate LR that gives p value. I tried to manage with a single key. But my problem is that How could I manage multiple keys in input. Thanx in advance input a1 1 2.5 x a1 2 3.5 x a1 3 4.5 x a1 1 2.6 x a1 2 3.8 x b1 1 4.6 y

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

Hi Xiantao, Some more nit-picking, though some of this is a bit more important to fixup. Cheers, Jes > +typedef struct thash_data { Urgh! argh! Please avoid typedefs unless you really need them, see Chapter 5 of Documentation/CodingStyle for details. > diff --git a/include/asm-ia64/kvm_host.h b/include/asm-ia64/kvm_host.h > new file mode 100644 > index 0000000..522bde0 > ---

Hi Xiantao, Some more nit-picking, though some of this is a bit more important to fixup. Cheers, Jes > +typedef struct thash_data { Urgh! argh! Please avoid typedefs unless you really need them, see Chapter 5 of Documentation/CodingStyle for details. > diff --git a/include/asm-ia64/kvm_host.h b/include/asm-ia64/kvm_host.h > new file mode 100644 > index 0000000..522bde0 > ---

Hello I am trying to get out of an lm model the fstatistics, however after I run the model I write > names(Model) and the fstatistic does not appear only these. names(Model) [1] "coefficients" "residuals" "effects" "rank" "fitted.values" [6] "assign" "qr" "df.residual"

I'd like to automatically plot the p-value and r^2 of a linear model. I know that I can extract r^2 with foo <- lm(y~x) a <- summary(foo) a$r.squared I can't find any similar functionality to obtain the p-value based on the ftest a$fstatistic returns the actual f-test and df, but not the p-value. Any thoughts? Thanks, Adam using R-0.65.1 Win32

[Please use the subject line!] In the help page for summary.lm, the "Value" section says that the returned object has a component called "fstatistic", which has the F-statistic and the associated numerator and denominator degrees of freedom. You can get the p-value by something like: fstat <- summary(speciallinearmodel)$fstatistic pval <- pf(fstat[1], fstat[2],