similar to: Polynomial fitting

Hello, I am trying to graph a regression line using the followings: Age <- c(39, 47, 45, 47, 65, 46, 67, 42, 67, 56, 64, 56, 59, 34, 42, 48, 45, 17, 20, 19, 36, 50, 39, 21, 44, 53, 63, 29, 25, 69) BloodPressure <- c(144, 220, 138, 145, 162, 142, 170, 124, 158, 154, 162, 150, 140, 110, 128, 130, 135, 114, 116, 124, 136, 142, 120, 120, 160, 158, 144, 130, 125, 175) SimpleLinearReg1=lm(Age ~

Hi folks, Pls help me to understand follow; An Introduction to R 2.4 Logical vectors http://cran.r-project.org/doc/manuals/R-intro.html#R-and-statistics 1) > x [1] 1 2 3 4 5 > temp <- x != 1 > temp [1] FALSE TRUE TRUE TRUE TRUE > 2) > x [1] 1 2 3 4 5 > temp <- x > 1 > temp [1] FALSE TRUE TRUE TRUE TRUE Why NOT > temp [1] TRUE FALSE FALSE FALSE

Hi folks, Pls help me to understand follow; An Introduction to R 2.4 Logical vectors http://cran.r-project.org/doc/manuals/R-intro.html#R-and-statistics 1) > x [1] 1 2 3 4 5 > temp <- x != 1 > temp [1] FALSE TRUE TRUE TRUE TRUE > 2) > x [1] 1 2 3 4 5 > temp <- x > 1 > temp [1] FALSE TRUE TRUE TRUE TRUE Why NOT > temp [1] TRUE FALSE FALSE FALSE

Hi, Anne, assign Age and Bloodpressure in the correct order to the axes in your call to plot as in: plot(y = Age, x = BloodPressure) abline(SimpleLinearReg1) Hth -- Gerrit --------------------------------------------------------------------- Dr. Gerrit Eichner Mathematical Institute, Room 212 gerrit.eichner at math.uni-giessen.de Justus-Liebig-University Giessen Tel:

Dear Gerrit, This issue is discussed in a vignette in the car package (both for functions in the car and effects packages): vignette("embedding", package="car") . The solution suggested there is the essentially the one that you used. I hope this helps, John ----------------------------- John Fox, Professor Emeritus McMaster University Hamilton, Ontario, Canada Web:

Hi, again, I have to modify my query since my first (too simple) example doesn't reflect my actual problem. Second try: When asking Effect() inside a function to compute an effect of an lmer-fit which uses a data frame local to the body of the function, as in the following example (simplifying my actual application), I get the "Error in is.data.frame(data) : object 'X' not

Hello, everyody, when asking, e.g., Effect() to compute the effects of a fitted, e.g., linear model after having deleted the data frame from the workspace for which the model was obtained an error is reported: > myair <- airquality > fm <- lm(Ozone ~ Temp, data = myair) > rm(myair) > Effect("Temp", fm) Error in eval(model$call$data, envir) : object 'myair'

Dear R-help, I am trying to express myself as best as I can here. If you also use Latex to edit math reports or other languages with similar editing method, you'll see what I'm talking about. My sincere appologies if my question is not clear enough to some extend, as also I'm not able to provide my code here because I don`t know which one I can use... When editing the title in R

Dear list, is anyone aware of the following behavious of diag when used to replace diagonals (plural!) of a matrix? Small example: The following is documented and clearly to be expected: A <- matrix(0, nrow = 5, ncol = 5) diag(A) <- 1; A BUT, what about the following? When executing the code of `diag<-` line by line, it throws errors. So why does it work? diag(A[-1, ]) <- 2; A

Dear Gerrit Thanks a lot. "rbind" seems to be the right function. Unfortunately there is a shift in the x-axis (see pdf). There are 52 trapcatch values each, m and w, but m$trapcatch and w$trapcatch are shifted up to x-value 60. The follow-up lines for temp and humidity are fine. Thanks Sibylle setwd("~/Desktop/DatenLogger2017") # am Mac sks trap =

Dear all, I work with R Version 2.0.0 on Machine hardware: sun4u OS version: 5.9 Processor type: sparc Hardware: SUNW,Sun-Blade-1000 and I have a very simple data frame (called OR) with the following variables: > sapply( OR, class) X ci FTyp "factor" "numeric" "factor" (In OR$ci there are some Inf-values. OR's

Hello, R-help, does anybody have already a work-around for the problem that the formula version of sunflowerplot() throws an error when provided with a value for xlab (or ylab) different from NULL: > sunflowerplot( Sepal.Length ~ Sepal.Width, data = iris, xlab = "A") Error in model.frame.default(formula = Sepal.Length ~ Sepal.Width, data = iris, : variable lengths differ

Dear R helpers   I have two series A and B as given below -   A <- c(2, 2, 1, 3, 7, 3, 3, 1, 14, 7, 31) B <- c(0.0728,0.9538,4.0140,0.0020,2.5593,0.1620,2.513,0.3798, .0033,0.2282, 0.1614)   I need to calculate the total in dataset B corresponding to the numbers in dataset A i.e. for no 1 in A, I need the total as 4.0140+0.3798 (as 1 is repeated twice) for no 2, I need the total as

Dear all, I would like to use cut() to make numerics to factors, the sample codes are as follows. However, the result is not what I want, since r[3] =?9 should be in the interval of "8-10%" rather than "2-4%". Maybe cut() is not the right function to use for my situation. Please help. > r <- c(1,1,9,1,1,1) > col_no <-

Dear R users aim Barplot of insect trap catches (y variable trapcatch) at one specific station (variable FiBL_Hecke) from week 1-52 ( x variable week). It works well using the function tapply (sum trapcatch per week, males and females not separated), however, I intend to separate the y variable trapcatch in males and females (variable m_w: m and w) problem I used the function "add" to

Hello, eveRybody, I've been trying to find the origin for the following formatting-"inconsistency": E. g., look at the number of digits in summary.defaults()'s output when NAs occur: in my example below the number of NA's is displayed as an integer, the rest as numeric (floating point numbers): > summary.default( c( 1:2, NA)) Min. 1st Qu. Median Mean 3rd Qu.

I still need to do some repetitive statistical analysis on some outcomes from a dataset. Take the following as an example; id sex hiv age famsize bmi resprate 1 M Pos 23 2 16 15 2 F Neg 24 5 18 14 3 F Pos 56 14 23 24 4 F Pos 67 3 33 31 5 M Neg 34 2 21 23 I want to know if there are statistically detectable differences in all of the continuous variables in

Dear all, I have searched the forums for an answer - and there is plenty of questions along the same line - but none of the paproaches shown worked to my problem: I have a data frame that I get from a csv: summarystats<-as.data.frame(read.csv(file=f_summary)); where I have the columns Dataset, Class, Type, Category,.. Problem1: I want to find a subset of this frame, based on values in

Hi, Sibylle, since you write '"mathematically" add', does barplot(rbind(m$trapcatch, w$trapcatch)) do what you want (modulo layout details)? Hth -- Gerrit --------------------------------------------------------------------- Dr. Gerrit Eichner Mathematical Institute, Room 212 gerrit.eichner at math.uni-giessen.de Justus-Liebig-University Giessen Tel:

Hi Anne, I would suggest to change the linear model to lm(BloodPressure~Age), as this model makes more sense in biological means (you would assume that age influences pressure, not vice versa) and also obeys the statistical assumption of weak exogeneity, that age can be measured without error, at least compared to error-prone bp measures. Cheers Am 18.04.2018 um 16:07 schrieb Gerrit Eichner: