similar to: Export summary from regression output

Hi all, I recently tried to calculate the mean and the median just for one column. In this column I have numbers with some empty cells due to missing data. So how can I calculate the mean just for the filled cells? I tried: mean(dataSet2$ac_60d_4d_after_ann[!is.na(master$ac_60d_4d_after_ann)], na.rm=TRUE) But the output was different to the calculation I died in Microsoft Excel. Thanks in

Hi there, I am really new to statistics in R and statistics itself as well. My situation: I ran a lot of OLS regressions with different independent variables. (using the lm() function). After having done that, I know there is endogeneity due to omitted variables. (or perhaps due to any other reasons). And here comes the Hausman test. I know this test is used to identify endogeneity. But what I

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,

Dear R users I have run an regression and want to extract the p value of the F statistics, but I can find a way to do that. x<-summary(lm(log(RV2)~log(IV.m),data=b)) Call: lm(formula = log(RV2) ~ log(IV.m), data = b[[11]]) Residuals: Min 1Q Median 3Q Max -0.26511 -0.09718 -0.01326 0.11095 0.29777 Coefficients: Estimate Std. Error t value Pr(>|t|)

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

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 ~

Hello I am able to extract partial regression coefficients from a fitted model object "model", i.e. model <- lm(var.sel.gkm, weights = count.gkm, data = DATA) summary(model) write.table(model$coef, file = "C:/coef_CO_gkm.txt", row.names = TRUE, col.names = TRUE) I was wondering if anyone could advise me how to extract other object items such as std. error, t-values

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

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

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

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) {

I would like to call both p-values and R-squared values from lm's in a function. I can get the p-values from coef(summary(name.lm))[r,c], however, I cannot figure out how to call the R-squared values without manually calling the summary and inserting them in the script - which negates the value of automating the process through a function. Thanks, Mike [[alternative HTML version deleted]]

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

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: I use lm (linear model) to analyze 47 variables , 8 responses So I use loop to finish it . I want the program to show the results that P-value is less than 0.05. How can I cite the P-valus from lm result ? Ping The code: #using LM to model general fati for (j in 48:52) { for (i in 3:46){ gen.fat<-y_x[,j] gen.fat<-as.numeric(gen.fat) snp_marker<-y_x[,i] x<-colnames(y_x)

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

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"

Hi there, I am stuck trying to solve what should be a fairly easy problem. I have a data frame that essentially consists of (ID, time as seqMonth, variable, value) and i want to find the regression coefficient of value vs time for each combination of ID and Variable. I have tried several approaches and none of them seems to work as i expected. For example, i have tried:

[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],