similar to: Error in eval(expr, envir, enclos) : object 'Rayos' not found???

Hi R Listers, I am trying to upload a data file and I received this message. It seems that I am still able to make graphs and Aeventexhumed still works in the analysis. Can I ignore this message or do I need to do something about this? Jean > require(plyr) Loading required package: plyr > turtlehatch <- read.csv(file.choose()) > attach(turtlehatch) The following object(s) are

Hi R-listers, I am trying to compare sections of the beach separated from the HTL to the Veg (east to west), separated into indices (-5 to 30m), HTLIndex. Cross parallel (north to south) are major beach sections (Rayos 1, 2, 3, 4 and MNB). I am thinking to do an ANOVA for each independent rectangle of beach (not exactly but will be treated as). The HTL from (0-5m) in Rayo 1 is one rectangle of

Hi All, I am wondering if anyone can tell me what the warning message below the model means? J add1(DTA.glm,~ Aeventexhumed + Veg + Berm + HTL + Estuary + Rayos) Single term additions Model: cbind(MaxHatch, TotalEggs - MaxHatch) ~ Aeventexhumed + Veg + Berm + HTL Df Deviance AIC <none> 488.86 4232.9 Estuary 1 454.96 4201.0 Rayos 3 258.80 4008.9 Warning

Hi R - listers, I am receiving an error. Does anyone know what this means? J ggplot(subset(foo, Rayos != "Rayos.NA"), aes(x=HTL, y=DevelopIndex, colour=TotalEggs)) +geom_point() +geom_jitter() + facet_grid(Aeventexhumed ~ Rayos) + geom_smooth(method="lm", fill=NA) + ylim(c(0, 7)) Error in +geom_smooth(method = "lm", fill = NA) : invalid argument to unary

Hi R-listers, I have developed 47 GLM models with different combinations of interactions from 1 variable to 5 variables. I have manually made each model separately and put them into individual tables (organized by the number of variables) showing the AIC score. I want to compare all of these models. 1) What is the best way to compare various models with unique combinations and different number

I am wondering if anyone can tell me what the error I'm receiving means below. I thought it said that Aeventexhumed should be converted to a factor, so I tried to do so and received the following error. Please advise. J --------------------------------------------------------- > data.to.analyze.glm <- glm(cbind(MaxHatch, TotalEggs-MaxHatch) ~ > Aeventexhumed, family=binomial,

I was able to make a scatterplot but ... 1) what does the "86" mean? The "86" shows up on the graph as well. > scatterplot (Shells/TotalEggs ~ Sector, data = data.to.analyze) [1] "86" 2) Also how do you change the Y axis title? I don't want it to read Shells/TotalEggs, instead I would like it to read Average Hatching Rate (%). 3) What does this error mean?

Hi R-listers, In plyr I am using coding I have used before but an error seems to be apparent. Has the function "join" been replaced by another word? I tried this before and it didn't work and then worked for me. But now it is not working again. Please advise, Jean > data.to.analyze <- join(responses, envir, by="QuadratEvent") Error: could not find function

After looking at 48 glm binomial models I decided to try the quasibinomial with the top model 25 (lowest AIC). To try to account for overdispersion (residual deviance 2679.7/68 d.f.) After doing so the dispersion factor is the same for the quasibinomial and less sectors of the beach were significant by p-value. While the p-values in the binomial were more significant for each section of the

Hi R-listers, I am receiving an error - see below. Aeventexhumed is the event in which nesting occured, so it is defined by A, B, C. I thought as a factor was ok, tried to change it to as.character but it still gave me the same error. Is there something I should do about this error or just ignore it? Please advise, Jean >summary(aov(EDI ~ HTLIndex + Aeventexhumed + HTLIndex:Aeventexhumed,

Hi R-listers, I am wondering if the function aov() in plyr is appropriate for two different types of tests: 1) > summary(aov(EDI ~ VegIndex, data=data.to.analyze)) AND 2) > summary(aov(HSuccess ~ VegIndex + Aeventexhumed + VegIndex:Aeventexhumed, data=data.to.analyze)) the later inclusive of an interaction of the two explanatory variables. This is for obtaining P value and F

Hi R-listers, Q1) What is the difference between the scatterplot and plot function? Q2) I am able to make a graph with the scatterplot function: scatterplot(DevelopIndex ~ Veg, + data = Turtle, + xlab = "Vegetation border (m)", + ylab = "Embryonic development index") And have been successful. But I do not know if the lines are for:

Hi R-listers, I am using the package plyr. I am just trying to get the hatching success mean of each nesting event and have typed in the following and received the below results: > tapply(HSuccess, Aeventexhumed, mean) A B C 0.2156265 0.1288559 NaN What can I do about NaN? I should be able to get a result for event C because I was able to

I am trying to create a frequency distribution and I am a bit confused. Here are the commands I have entered: > data <- read.csv(file="40609_sortedfinal.csv",head=TRUE,sep=",") > NumberOfActionsByStatus = data$STATUS > NumberOfActionsByUser = data$ETS_LOGIN > NumberOfBidOffer = data$BID_OFFER > NumberOfActionsByUser.freq = table(NumberOfActionsByUser) >

Hi R-listers, I am in plyr and using the script below and when I try to pull up the .csv file with head() it does not show up. I am not sure where I am going wrong. I have also checked the file box for turtlehatch.csv and it still does not show up when I head(turtlehatch.csv). Am I missing a command? Please advise, Jean > devel.index <- function(values, weights=c(1, 2, 3, 4, 5, 6)) { +

Hi R-listers, I am trying to do an ANOVA for the following scatterplot and received the following error: library(car) scatterplot(HSuccess ~ Veg, data = data.to.analyze, xlab = "Vegetation border (m)", ylab = "Hatching success (%)") anova(HSuccess ~ Veg, data=data.to.analyze) Error in UseMethod("anova") : no applicable

Hola, Daniel: Quizá deberías ser más explícito porque de la información que suministras yo solo te puedo decir que no veo la relación entre los 3 tipos de algoritmos que nombras: - un análisis de componentes principales puede ser una fase previa de los otros dos - hacer un cluster es un tipo de aprendizaje no supervisado, mientras que un clasificador normalmente es utilizado en aprendizaje

CREO QUE ESTE TIPO DE CONSULTA, EXCEDE EL PROPÓSITO DE ESTE FORO. El miércoles, 26 de noviembre de 2014, Daniel Carrillo Zapata < daniel.carrillo2 en um.es> escribió: > Hola Isidro, > > mira, te explico mejor: tengo una base de datos con información de > 10 conductores en un recorrido de 30 minutos en coche. Para cada > conductor, se le midió parámetros

Hola de nuevo a todos, me gustaría pediros disculpas por los correos que he enviado. La razón de enviarlos es que pensaba que era también un foro en el que podía plantear cuestiones estadísticas, no solo sobre R en concreto. Siempre es importante aprender algo de todo lo que haces, así que lo que me llevo es el conocimiento de que aquí solo puedo plantear cuestiones de implementación en R, y