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Dear r-devel list members, On a couple of occasions I've encountered the issue illustrated by the following examples: --------- snip ----------- > mod.1 <- lm(Employed ~ GNP.deflator + GNP + Unemployed + + Armed.Forces + Population + Year, data=longley) > mod.2 <- update(mod.1, . ~ . - Year + Year) > all.equal(mod.1, mod.2) [1] TRUE > > f <- function(mod){ + subs <- 1:10 + update(mod, subset=subs) + } > f(mod.1) Cal...

...Even the behaviour of lm() and glm() isn't entirely consistent. In both cases, singularity results in NA coefficients by default, and these are reported in the model summary and coefficient vector, but not in the coefficient covariance matrix: ---------------- > mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), + data=longley) > summary(mod.lm) Call: lm(formula = Employed ~ GNP + Population + I(GNP + Population), data = longley) Residuals: Min 1Q Median 3Q Max -0.80899 -0.33282 -0.02329 0.25895 1.08800 Coefficients: (...

...ovide reproducible code.] I'm doing a multivariate bootstrap, using boot::boot(), where the output of the basic computation is a k x p matrix of coefficients, representing a tuning constant x variable, as shown in the $t0 component from my run, giving a 3 x 6 matrix > lboot$t0 GNP Unemployed Armed.Forces Population Year GNP.deflator 0.00 -3.4472 -1.828 -0.6962 -0.34420 8.432 0.15738 0.01 -0.1798 -1.361 -0.5881 -1.00317 5.656 -0.02612 0.08 1.0907 -1.086 -0.4583 -0.08596 2.642 0.57025 > ?boot doesn't say how these are s...

I want to apply Nyblom-Hansen test with the strucchange package, but I don't know how is the correct way and what is the difference between the following two approaches (leeding to different results): data("longley") # 1. Approach: sctest(Employed ~ Year + GNP.deflator + GNP + Armed.Forces, data = longley, type = "Nyblom-Hansen") #results in: # Score-based CUSUM test with mean L2 norm # #data: Employed ~ Year + GNP.deflator + GNP + Armed.Forces #f(efp) = 0.8916, p-value = 0.4395 #2. Approach: sctest(gefp(Employed ~ Year + GNP.deflato...

...r of lm() and glm() isn't entirely consistent. In both cases, singularity results in NA coefficients by default, and these are reported in the model summary and coefficient vector, but not in the coefficient covariance matrix: > ---------------- >> mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), > + data=longley) >> summary(mod.lm) > Call: > lm(formula = Employed ~ GNP + Population + I(GNP + Population), > data = longley) > Residuals: > Min 1Q Median 3Q Max >...

...) and glm() isn't entirely consistent. In both cases, singularity results in NA coefficients by default, and these are reported in the model summary and coefficient vector, but not in the coefficient covariance matrix: >> ---------------- >>> mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), >> + data=longley) >>> summary(mod.lm) >> Call: >> lm(formula = Employed ~ GNP + Population + I(GNP + Population), >> data = longley) >> Residuals: >> Min 1Q Median...

...so, here is a short dummy code to give an idea of what I am talking about: attach(longley) N <- length(Unemployed) block <- c( rep( "a", N), rep( "b", N), rep( "c", N) ) Unemployed.3 <- c(Unemployed + rnorm(1), Unemployed + rnorm(1), Unemployed + rnorm(1)) GNP.deflator.3 <- c(GNP.deflator + rnorm(1), GNP.deflator + rnorm(1), GNP.deflator + rnorm(1)) cor(Unemployed, GNP.deflator) cor(Unemployed.3, GNP.deflator.3) cor(Unemployed.3[block == "a"], GNP.deflator.3[block == "a"]) cor(Unemployed.3[block == "b"], GNP.deflator.3[b...

...sistent. In both > cases, singularity results in NA coefficients by default, and these are reported > in the model summary and coefficient vector, but not in the coefficient > covariance matrix: > > >> ---------------- > > >>> mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), > >> + data=longley) > >>> summary(mod.lm) > > >> Call: > >> lm(formula = Employed ~ GNP + Population + I(GNP + Population), > >> data = longley) > > >> Residual...

...results in NA coefficients by default, and these are >> reported in the model summary and coefficient vector, but >> not in the coefficient covariance matrix: >> >> >> ---------------- >> >> >>> mod.lm <- lm(Employed ~ GNP + Population + I(GNP + >> Population), >> + data=longley) >>> summary(mod.lm) >> >> >> Call: >> lm(formula = Employed ~ GNP + Population + >> I(GNP + Population), >> data = longley) >> >> >> Residua...

...; cases, singularity results in NA coefficients by default, and these are reported > in the model summary and coefficient vector, but not in the coefficient > covariance matrix: > > > > >> ---------------- > > > > >>> mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), > > >> + data=longley) > > >>> summary(mod.lm) > > > > >> Call: > > >> lm(formula = Employed ~ GNP + Population + I(GNP + Population), > > >> data = longley...

...data(longley) > gr <- lm.ridge(Employed ~ .,longley,lambda = seq(0,0.1,0.001)) > plot(gr) > select(gr) modified HKB estimator is 0.004275 modified L-W estimator is 0.0323 smallest value of GCV at 0.003 > ##let's choose 0.03 for lambda > coef(gr)[gr$lam == 0.03,] GNP.deflator GNP Unemployed Armed.Forces Population Year -1620.429355 0.021060 0.007994 -0.013146 -0.007752 -0.101880 0.869116 But how does one obtain the customary 'lm' summary information for the model above? I tried supplying the chosen lambda to Design...

Dear all, I would like to suggest changing the pairs.formula command such that a command like pairs(GNP ~ . - Year - GNP.deflator, longley) would behave in a similar fashion as lm(GNP ~ . - Year - GNP.deflator, longley) i.e., make a pairwise scatterplot of GNP and all other variables in the (longley) dataframe except for Year and GNP.deflator. The above command, with the current version...

...y entering pol572a1<- read.dta("C:\\alex\\Graduate Coursework\\Pol 572\\pol572a1.dta") So now I can do a few things with the data, but I am only able to do so by entering with(pol572a1, before typing another command. So I enter the following for a scatter plot: with(pol572a1, plot(rgnpc, incmean)) When I run a simple linear regression, I enter the following: with(pol572a1, lm(incmean~rgnpc)) But when try to get a fitted line, I enter the following: with(pol572a1, lm( share.gnp)<-lm(incmean~rgnpc)) But I get the following error message: Error in lm(share.gnp) <- lm(incmea...

...X'y and if a take k=Ridge$kHKV, Coef should be approx equal to Ridge$Coef[near value of kHKV] and it does not seem to happen, why? Values: > Ridge$kHKB [1] 0.004275357 Using the calculation above (third question, third point): Coef= [,1] 1 -0.095492310 GNP.deflator -0.052759002 GNP 0.070993540 Unemployed -0.004244391 Armed.Forces -0.005725582 Population -0.413341544 Year 0.048420107 And if I take from Ridge&coef: Ridge$coef[0.004] GNP.deflator -0.03098507 GNP -1.32553151 Unemployed -1.53237769 Armed.Forces -0.63...

I can plot a time series with: gnp <- ts(cumsum(1+round(rnorm(100), 2)), start=c(1954,7), frequency=12) plot(gnp) But I want to plot more time series in the same plot. How can I do it? =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/...

Hi I am using the package urca and I am interested about the KPSS test. That works fine except the method "summary" did not work in the script, only when it is typed direct in the console the results are shown( not a source file). Is there any problem with these method ?

...the obvious. My code is simple: install.packages('RODBC') install.packages('boot') library(RODBC,boot) setwd("C:/Documents and Settings/Gary Smith/My Documents/Blog/") data = odbcConnectExcel("LafferCurve2.xls") mydata = sqlFetch(data,"Data") n=23 m=73 gnp=mydata[[3]][n:m] incTaxFirst=mydata[[7]][n:m] corr(cbind(gnp,incTax)) Here are the console messages: > install.packages('RODBC') Warning: package 'RODBC' is in use and will not be installed > install.packages('boot') trying URL 'http://cran.stat.ucla.edu/bin/wind...

...uot; result } # Test: > lambda <- c(0, 0.005, 0.01, 0.02, 0.04, 0.08) > lambdaf <- c("", ".005", ".01", ".02", ".04", ".08") > lridge <- ridge(longley.y, longley.X, lambda=lambda) > lridge$coef GNP Unemployed Armed.Forces Population Year GNP.deflator 0.000 -3.4471925 -1.827886 -0.6962102 -0.34419721 8.431972 0.15737965 0.005 -1.0424783 -1.491395 -0.6234680 -0.93558040 6.566532 -0.04175039 0.010 -0.1797967 -1.361047 -0.5881396 -1.00316772 5.656287 -0.02612152 0.020 0.4994945...

...e to know if is there some function to obtain a extended summary like in Gretl. I will write a example in Gretl Modelo 1: MCO, usando las observaciones 1968-1982 (T = 15) Variable dependiente: Invest Coeficient St error t-ratio p-value const 377,631 35,0955 10,7601 <0,00001 *** GNP 0,63612 0,055015 11,5627 <0,00001 *** CPI -9,70228 0,913737 -10,6182 <0,00001 *** Interest -0,941363 2,21077 -0,4258 0,67927 time 8,49182 3,18142 2,6692 0,02353 ** Average of Dep Var. 276,0067 St Error Dep. Var 117,5827 Squared Sum of remainders 818,6362...

...y entering pol572a1<- read.dta("C:\\alex\\Graduate Coursework\\Pol 572\\pol572a1.dta") So now I can do a few things with the data, but I am only able to do so by entering with(pol572a1, before typing another command. So I enter the following for a scatter plot: with(pol572a1, plot(rgnpc, incmean)) When I run a simple linear regression, I enter the following: with(pol572a1, lm(incmean~rgnpc)) But when try to get a fitted line, I enter the following: with(pol572a1, lm( share.gnp)<-lm(incmean~rgnpc)) But I get the following error message: Error in lm(share.gnp) <- lm(incmea...