similar to: Replacing values without looping

Hi, I expect this is simple but haven’t found an answer looking on the archives... I want to convert ‘NA’ (missing) to particular levels (nonmissing) in factor vectors. e.g. I know > X <- c(1, 2, 3) > summary(X) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.0 1.5 2.0 2.0 2.5 3.0 > X <- as.factor(X) > summary(X) 1 2 3 1 1 1 >

Hello Running R2.9.2 on Windows XP I am puzzled by the performance of LME in situations where there are missing data. As I understand it, one of the strengths of this sort of model is how well it deals with missing data, yet lme requires nonmissing data. Thus, m1.mod1 <- lme(fixed = math_1 ~ I(year-2007.5)*TFC_, data = long, random =

Suppose you fit a linear model > model.1 ~ lm(v1 ~ ..., data=myframe) and v2 is some other column of myframe typically not in the model. You will often want to try > plot(v2, model.1$residuals) but this will fail if there are NAs in the response v1 as model.1$residuals has length equal to the number of nonmissing values in v1. I suppose > plot(v2[!is.na(v1)],

Hello, Using the following lines of code, I created the following graph:

Hello, Using the following lines of code, I created the following graph:

Is the following developed in my console output a recognized bug or am I using the cat function incorrectly? Thanks, Stan > ifelse(class(data[[n]])!="factor",{print("yes")},{print("no")}) [1] "yes" [1] "yes" > ifelse(class(data[[n]])!="factor",{cat("yes")},{cat("no")}) yesError in ans[test & !nas] <-

Dear all, I am attempting to explain patterns of arthropod family richness (count data) using a regression model. It seems to be able to do a pretty good job as an explanatory model (i.e. demonstrating relationships between dependent and independent variables), but it has systematic problems as a predictive model: It is biased high at low observed values of family richness and biased low at

Hi R Users, I am using following R code to plot a "grouped boxplot". I'm hoping if I can add MEAN to these boxplots. Data is copied below and attached as text file. install.packages("ggplot2") library(ggplot2) dta<-read.table("Sample.txt",header=T) attach(dta) p <- ggplot(dta, aes(factor(month), nail)) p + geom_boxplot(aes(fill = factor(trt))) Data: month

Hello ! I am attempting to switch from being a long time SAS user to R, and would really appreciate a bit of help ! The first thing I do in getting a large dataset (thousands of obervations and hundreds of variables) is to run a SAS command PROC CONTENTS VARNUM command - this provides me a table with the name of each variable, its type and length; then I run a PROC MEANS - for numerical

Hello list, I have the following function which, as you can see, uses mean: meanratings <- round(apply(stack03[,c(102:121)],2,function(x) (tapply(x ,actcode, mean, na.rm=T))), digits=1) The above function yields the following output: q27a q27b q27c q27d q27e q27f q27g q27h q27i q27j q27k q27l q27m q27o q27p 1 7.8 8.1 7.7 7.9 7.9 NaN NaN 8.4 7.8 7.0 7.6 NaN NaN 7.1 6.0 2

Hello R-help, I noticed the following surprising behavior when using %in% to find elements in a vector generated using seq(). # weird!!! > c(7.7, 7.8, 7.9) %in% seq(4, 8, by=0.1) [1] TRUE FALSE TRUE # OK now > c(7.7, 7.8, 7.9) %in% round(seq(4, 8, by=0.1), 1) [1] TRUE TRUE TRUE # wait, how is this different? > c(7.7, 7.8, 7.9) %in% seq(7, 8, by=0.1) [1] TRUE TRUE TRUE Is there an

Hi, I'm having difficulty importing my textfile that looks something like this: #begin text file Timepoint 1 ObjectNumber Volume SurfaceArea 1 5.3 9.7 2 4.9 8.3 3 5.0 9.1 4 3.5 7.8 Timepoint 2 ObjectNumber Volume SurfaceArea 1 5.1

Hi All, I need to parse data from a file, example shown below. The first two lines can be skipped, the third line contains the column names. The next 13 lines can be skipped. The next line "1991" is a year value, with the following 13 values data for that year. The file then repeats this format with (year, 13 lines of data for that year). I would ideally like to end up with an

Hi. I am running regressions of several dependent variables using the same set of independent variables. The independent variable values are complete, but each dependent variable has some missing values for some observations; by default, lm(y1~x) will carry out the regressions using only the observations without missing values of y1. If I do lm(cbind(y1,y2)~x), the default will be to use

I am trying to use KalmanSmooth to smooth a time series fitted by arima (and with missing values), but the $smooth component of the output baffles me. Look at the following example: testts <- arima.sim(list(ar=0.9),n=100) testts[6:14] <- NA testmod <- arima(testts, c(1,0,0)) testsmooth <- KalmanSmooth(testts, testmod$model) par(mfrow=c(2,1)) plot(testsmooth$smooth,

Hi! I have 2 data.frames: "fish" and "popn": >fish xloc yloc id birth size weight energy gonad 20 15 15 54 -60 107.9 63.0 15952.9 8.0 21 15 15 32 -60 105.1 61.4 15538.8 7.8 91 4 43 96 -60 118.9 69.4 17573.2 8.8 71 32 4 64 -60 121.6 71.0 17976.0 9.0 34 2 64 20 -60 116.2 67.9 17173.0 8.6 95 6 20 58 -60 106.5

I used the following to assign each row the group average, where w is the new group average variable, x is the variabale to be averaged, and g is the nominal group indicator: w <- ave(x,g) Now I want to calculate the group average, but WITHOUT each row's value of x. Is there an easy way to do this? I'm sure I'm missing something obvious here, but for the life of me I can't

Dear R users, I have a dataset with two variables: $esan - a grouping factor with 8 levels and $reus. I'd like to do wilcox.test on this dataset as sugested Weiwei here: https://stat.ethz.ch/pipermail/r-help/2007-July/136627.html. I tried to adapt his recommendation but no succes. Can anyone help me? Regards, Iurie Malai, Senior Lecturer Department of Psychology Faculty of Psychology and

Hola!! tengo un data.frame donde cada fila corresponde a un año y cada columna a un mes (De enero a diciembre) > head(valT) V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 1941 18.0 16.3 15.2 10.1 8.1 8.3 8.8 9.2 7.9 12.2 11.9 14.6 1942 17.2 15.9 13.6 11.6 8.7 6.2 6.4 7.2 9.7 12.0 14.1 16.7 1943 17.6 17.3 13.5 12.5 10.5 7.0 8.2 7.9 -999.9 -999.9

Hello everyone, I am attempting to write a function to count the number of non-missing values of each column in a data frame using the sapply function. I have the following code which is receiving the error message below. > n.valid<-sapply(data1,sum(!is.na)) Error in !is.na : invalid argument type Ultimately, I would like for this to be 1 conponent in a larger function that will produce