search for: crosspid

...n't work. plot(var1, var2, xlab = "var1", ylab = "var2", col =var3 , bty='L') legend(locator(1),c("level 1 var3", "level 2 var3","level 3 var3", "level 4 var3","level 5 var3"), col=var3,pch=16) Thanks in advance, crosspide at hotmail.com -- View this message in context: http://r.789695.n4.nabble.com/assign-same-legend-colors-than-in-the-grouped-data-plot-tp4390641p4390641.html Sent from the R help mailing list archive at Nabble.com.

...8 variables involved. I'd like to predict and only introduce in newdata the values of these 8 variables involved. However, as the tree was built with the 23, it asked me for 15 values, even if it doesn't need them. Is there a way to introduce only this 8 values? Thanks in advance, show crosspide at hotmail.com as user at host.com -- View this message in context: http://r.789695.n4.nabble.com/predict-rpart-newdata-introduce-only-values-variables-used-in-the-tree-tp4642145.html Sent from the R help mailing list archive at Nabble.com.

Dear Community, I want to identify outliers in my data. I don't know how to use identify command in the plots obtained. I've gone through help files and use mahalanobis example for my purpose: NormalMultivarianteComparefunc <- function(x) { Sx <- cov(x) D2 <- mahalanobis(x, colMeans(x), Sx) plot(density(D2, bw=.5), main="Squared Mahalanobis distances, n=nrow(x),

Before loading sql packages, i was wondering, once all packages required are installed, if it's possible to do from R console sth like this: SQL> select v1 from sql_table where v2 in ("R_variable") # being "R_variable" a vector Thanks in advance, user at host.com -- View this message in context:

Dear Community, I'd like to plot an rq object the same way I do with a lm one, is it possible? Something like this plot(rqmodel , 1:4, id.labels=rownames(pga1)); where rqmodel <- rq(log(vd) ~ v1 + log(v2) +log(v3) + v4 + v5 ,data =dat) Thanks in advance and apologies, I'm pretty newbie with this, user at host.com -- View this message in context:

Dear community, Apologies, I'm still pretty newbie. Anyway, I am performing linar regression analysis. As a common cause of non-normally distributed residuals is non-normally predictor variables, i'm interested in achieving the best transformation of the predictors. I've seen some commands at R, but I would like to know if it exists a command equivalent to gladder (graphic display)

Dear Community, I've a Work -Shift Scheduling Problem I'd like to solve via constraint linear programming. Maybe something similar to http://support.sas.com/documentation/cdl/en/orcpug/63349/HTML/default/viewer.htm#orcpug_clp_sect037.htm Can anybody suggest me any package/R examples to solve this? If it's needed more details of my little problemm I can provide. Thanks in

...tu >3, ] (Do I only have 8 entries?) Has this happened to anybody before? If so, why this extra NA rows? what's the problem? Is there any other way to know which data have studentized residuals larger than 3? if it's needed to upload my data, just tell me. Thanks in advance,show crosspide at hotmail.com as user at host.com -- View this message in context: http://r.789695.n4.nabble.com/NA-s-when-subset-in-a-dataframe-tp4606172.html Sent from the R help mailing list archive at Nabble.com.

Dear community, I'd like to fix a mixed model. I have unbalance data, what should i use: lme in nlme package , or lmer in lme4. Thanks, user at host.com as user at host.com -- View this message in context: http://r.789695.n4.nabble.com/lme-or-lmer-for-unbalance-data-tp4608425.html Sent from the R help mailing list archive at Nabble.com.

Dear community, I'm working with the data.frame attached ( http://r.789695.n4.nabble.com/file/n4122926/df1.xls df1.xls ), let's call it df1. I typed: df1<- read.xls("C:/... dir .../df1.xls",colNames= TRUE, rowNames= TRUE) Then I splited randomly df1 using splitdf function (http://gettinggeneticsdone.blogspot.com/2011/03/splitting- dataset-revisited-keeping.html)

Dear all, I've tried a multiple regression, and now I want to try a cross-validation. I obtain this error (it must be sth related to df) that I don't understand, any help would be appreciated. cv.lm(df= dat, lm2.52f, m=3) Error en `[.data.frame`(df, , ynam) : undefined columns selected lm2.52f is my lm object, dat is a dataframe where the variables involved in .lm are I tried CVlm

Dear all, I want to improve my adj - R sq. I 've chequed some established models and they introduce two times the same variable, one transformed, and the other not. It also improves my adj - R sq. But, isn't this bad for the collinearity? Do I interpret coefficients as usual? Estimate Std. Error t value Pr(>|t|) (Intercept) 1.73140 7.22477 0.240

Dear community, I was wondering if it's possible to know if you have enough data for a regression study. I remember you must have more data than parameters to obtain, but I'd like to know if there was something more sophisticated. Thanks, user at host.com -- View this message in context: http://r.789695.n4.nabble.com/how-many-records-for-suitable-regression-tp3331522p3331522.html

Dear community, I'm doing a lm. In the independent variables I've got a categorical one. Here is its histogram: http://r.789695.n4.nabble.com/file/n3333638/altitude.png I did this regression: lmeo2.52f <- lm(dat82$IncAltuDom ~ dat82$hdom2+log(dat82$CV)+ dat82$CA+ dat82$FCC+ factor(dat82$IdAltitud)) I obtain: Coefficients: Estimate Std. Error t value

Hello, I am new with R, and I'm trying to fit a linear model, I did the following and obtein this result, can anybody help? Thanks, > logdftodos7925vi <- log(dftodos7925vi) > logALTURA7925<- log(dftodos7925$ALTURA7917) > logtodos7925.lm <- lm (logALTURA7925~., data= logdftodos7925vi) Error en lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : NA/NaN/Inf en

Dear community, I have fitted a model using comands above, (rlm, lmrob or lmRob). I don't have new data to validate de models obtained. I was wondering if exists something similar to CVlm in robust regression. In case there isn't, any suggestion for validation would be appreciated. Thanks, user at host.com -- View this message in context:

Dear all, I've tried an lm model. I want to check indepence of the residuals, so I was trying the following: library(ape) plot.correlogram(residuals(model1)) I obtain this error: Error en x$p.values : $ operator is invalid for atomic vectors Any idea? Thanks in advance, user at host.com -- View this message in context:

Dear all, I want to measure the goodness of prediction of my linear model. That's why I was thinking about the area under roc curve. I'm trying the following, but I don't know how to avoid the error. Any help would be appreciated. library(ROCR) model.lm <- lm(log(outcome)~log(v1)+log(v2)+factor1) pred<-predict(model.lm) pred<-prediction(as.numeric(pred),

I also have the same problem. Can you send me the link to download a proper Sun Java application (I have Microsot Windows XP Profesional V.2002)? Anyway, this is what I tried: > library(JGR) Loading required package: JavaGD Loading required package: iplots Error : .onLoad failed in loadNamespace() for 'iplots', details: call: .jnew("org/rosuda/iplots/Framework") error:

Dear community, I'm trying to model growth with this function: Yi = A* exp(-k*(1/ti^m)) ; A asymptote, k rate of decrease of the relative growth rate, m shape parameter. I don't have variable time so, finally, following some papers, I try to fit Yi+a = A*exp(-k* (1/(-k/(log(Yi/A)))^(1/m)+a)^m); a= 10 I've tried: a) nls.1 <- nls(Yi+a ~A*exp(-k*