similar to: bugs in summary.lm (PR#7061)

Something for the wish list (not really a bug): I was reminded of what I see as a problem with summary.glm last week when some of my students fell into a trap in a homework exercise, defining a logit model in which coefficients were aliased. When this happens in lm, summary.lm prints a message ["Coefficients: (1 not defined because of singularities)"], but summary.glm is silent. In

Hi, folks, I would like to copy the output of summary(lm) and anova (lm) in R to my word file. But the output will be a mess if I just copy after I call summary and anova. ##################### x=rnorm(10) y=rnorm(10,mean=3) lm=lm(y~x) summary(lm) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -1.278567 -0.312017 0.001938 0.297578 1.310113

I have a simple linear model (fitted with lm()) with 2 independant variables : one categorical and one integer. When I run summary.lm() on this model, I get a standard linear regression summary (in which one categorical variable has to be converted into many indicator variables) which looks like : Estimate Std. Error t value Pr(>|t|) (Intercept) -3595.3 2767.1 -1.299

Hi all, I use the R2HTML package to print results, but I have a little problem when I want to print a table provides from a summary.lm. If I want to align this table differently of the others, I use the "align" argument ("left", "center or "right") of the HTML function , but it prints "Coefficients left" (or "Coefficients center",

dear R experts: I wonder whether it is possible to extend the summary method for the lm function, so that it uses an option "hccm" (well, model "hc0"). In my line of work, it is pretty much required in reporting of almost all linear regressions these days, which means that it would be very nice not to have to manually library car, then sqrt the diagonal, and recompute

On 07/04/2016 5:21 PM, Murray Efford wrote: > Following some old advice on this list, I have been reading the code for summary.lm to understand the computation of R-squared from a weighted regression. Usually weights in lm are applied to squared residuals, but I see that the weighted mean of the observations is calculated as if the weights are on the original scale: > > [...] > f

Hello! I have already reported on this behaviour at [1] and now I have checked the same example on r-devel and the problem is still there. Thomas Lumley inidicated problem [2]. Example: n <- 50 x <- runif(n=n) y1 <- 2 * x + rnorm(n=n) y2 <- 5 * x + rnorm(n=n) y2[sample(1:n, size=5)] <- NA y <- cbind(y1, y2) ## Goes ok here fit <- lm(y1 ~ 1,

Full_Name: Jeffrey Racine Version: 2.6.1 and previous... OS: FreeBSD Submission from: (NULL) (130.113.139.86) Hi. I almost feel bad reporting this, but here goes. The summary() for lm() (and possibly others?) uses a capitalized `S' in Multiple R-Squared but a lowercase `s' in Adjusted R-squared. For instance, Residual standard error: 0.5608 on 202 degrees of freedom Multiple

Dear list members, I have a 2-factor ANOVA where the summary.lm output looks like this (using treatment contrasts): Value Std. Error t value Pr(>|t|) (Intercept) 0.0389 0.0220 1.7695 0.0817 as.factor(Block)1 0.0156 0.0066 2.3597 0.0215 as.factor(Block)2 -0.0018 0.0037 -0.4857 0.6289 as.factor(Block)3 -0.0007 0.0026 -0.2812 0.7795

Hello list, I have a linear regression ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2,10,20, labels=c("Ctl","Trt")) weight <- c(ctl, trt) reg <- lm(weight ~ group) sreg <- summary(reg) and I would like to dump exactly what I see on the console with

What is the difference between the two R-squareds returned for a linear regression by summary(lm...)? When might one report multiple vs. adjusted R-squared? Thank you, Ben Osborne -- Botany Department University of Vermont 109 Carrigan Drive Burlington, VT 05405 benjamin.osborne at uvm.edu phone: 802-656-0297 fax: 802-656-0440

I have a program which is doing a few thousand runs of lm(). Suppose it is a simple model y = a + bx1 + cx2 + e I have the R object "d" where d <- summary(lm(y ~ x1 + x2)) I would like to obtain Var(x2) out of "d". How might I do it? I can, of course, always do sd(x2). But it would be much more convenient if I could snoop around the contents of summary.lm and

Hello all, I'm trying to do a nested linear model with a dataset that incorporates an observation for each of several classes within each of several plots. I have 219 plots, and 17 classes within each plot. data.frame has columns "plot","class","age","dep.var" With lm(dep.var~class*age), The summary(lm) function returns t-test and F-test values

Hi, I'm running a regression using lm(), in which one of the right-hand side variables is factor with many levels (say, 80). I am not intersted in the estimates of the resulting dummies, but I have to include them in my regression equation. So, I don't want the estimates associated with theses dummies to be printed by summary.lm( ). Is there an easy way to do this? Thank you, Dimitri

Dear all, Is there anybody who can help me to avoid scientific number in the summary of an lm model? Here there is an example of a usual output of the lm model. Thank you! Guillermo Example summary(lm(promiscuity.Index~allK)) Call: lm(formula = promiscuity.Index ~ allK) Residuals: Min 1Q Median 3Q Max -1.67094 -0.13126 0.06703 0.19913 0.40673 Coefficients:

Hi, I am having a hard time getting what the summary.lm-output for an ANOVA / ANCOVA means. Examples I find always seem to deal with simpler cases than what I meet in my data. My main problem is understanding the output when getting significant INTERACTION TERMS (what never occurs in examples :(). The following is the output after summary.lm(ancova) where "week" is continuous,

Let's assume that according to Anova(lm(y~a*b, data=d)) the a:b interaction is significant, and I would like to know if there are specific combinations of a and b levels that differ from the control group. Are there any caveats against simply looking at the p-values in the output generated by summary(lm(y~a*b,data=d))? The documentation for summary.lm says nothing about multiple comparison

Let's say I've run Anova(lm(y~a*b)) and found the a:b interaction to be significant. Now I'm interested in which specific level combinations of a and b significantly differ from the control group. Can I use the t-tests from summary(lm(y~a*b)) to answer that question? I saw no mention of multiple comparison in the documentation for summary.lm, so am I right in assuming I need to

lm works with difftime objects but then if you try to get the summary it fails with an error: fit <- lm(as.difftime(Time, units = "mins") ~ demand, BOD) summary(fit) ## Error in Ops.difftime((f - mean(f)), 2) : ## '^' not defined for "difftime" objects A number of other lm methods also fail, e.g. plot(fit), but others work, e.g. coef(fit), resid(fit) --

This seemed to me to be basic, but I can't seem to find a solution online, so I wondered what I might be missing. I wish to include the output of an lm summary object inside an Sweave (.Rnw) document. I can either output the summary.lm as is, or use the xtable/Hmisc packages (through xtable or latex commands). Is there something like xtable that also gives the summary information which is