search for: nonmissing

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 > levels(X) [1] "1" "2" "3&quot...

...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)], model.1$residuals) does the job, but it seems irritating that model.1$residuals, does not have length agreeing with the number of rows in the data frame. It would be even more irritating for model.1$fitted.values, where the removed elements...

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 = ~I(year-2007.5)|schoolnum) causes an error in na.fail.default, but adding na.action = "na.omit" makes a model with no errors. However, if I create that model, i...

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

...lda is the R-function I want. I have done this in SPSS using the code (is it 'S' code?)... "DISCRIMINANT /GROUPS=group(1 3) /VARIABLES=agr min man ps con ser fin sps tc /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF COEFF RAW COV TABLE /PLOT=COMBINED MAP /CLASSIFY=NONMISSING POOLED " In R I express this as... library("MASS") # For lda library("foreign") EW <- read.spss("eurowork.sav") Ind <- cbind(EW$AGR, EW$MIN, EW$MAN, EW$PS, EW$CON, EW$SER, EW$FIN, EW$SPS, EW$TC) Dep <- EW$GROUP LDA <- lda(Dep ~ Ind, prior=c(1,1,1)/3)...

I got an array similar to the one below, and want to replace all NAs with the previous value. 99 8.2 b NA 8.3 x NA 7.9 x 98 8.1 b NA 7.7 x 99 9.3 b ... i.e. the first two NAs should be replaced to 99, whereas the last one should be 98. I would like to apply a function to reach row, checking if the value in col 1 is NA, and if it is, set the value to the previous row's col 1 value.

I have the following problem. Consider a `classical' test which works for k .ge. 2 samples. Possible interfaces are e.g. xxx.test(x, g) x ... all data, g ... corresponding groups xxx.test(x1, ..., xk) xxx.test(list(x1, ..., xk)) etc etc. Clearly, the first and the second one are nice, but cannot be combined without making `g' (i.e., `group') a named argument. Hence, in

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,

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

Dear R People: Suppose I have the following two character vectors: xf [1] "W" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA > xg [1] NA "k" "h" NA "g" "r" "j" NA "v" "d" NA "v" NA "z" "r" "r" "i" > I want to end up with "W"

Good morning reader, I have encountered a, probably, simple issue with respect to the *formulae* of a *regression model* I want to use in my research. I’m researching alliances as part of my study Business Economics (focus Strategy) at the Vrije Universiteit in Amsterdam. In the research model I use a moderating variable, I’m looking for confirmation or help on the formulation of the model.