
Hi,

i am trying to run an ANCOVA and a bootstrapped ANCOVA analysis on a specific data set.  I am using the ancova and ancboot functions as in the following code:

setwd("C:/Users/User/Desktop/Rdatabilingualstudy2012")

bilingualismdata<-read.spss("bilingualdataforconferences2012.sav", use.value.labels = TRUE, to.data.frame = TRUE)

bilingualismdata$TypeBiling<-factor(bilingualismdata$TypeBiling, levels = c(0:1), labels = c("monolingual", "bilingual"))

cypriot<-subset(bilingualismdata, TypeBiling=="monolingual")
cypriotenglish<-subset(bilingualismdata, TypeBiling=="bilingual")

covGrp1<-cypriot$FAS
dvGrp1<-cypriot$Simon
covGrp2<-cypriotenglish$FAS
dvGrp2<-cypriotenglish$Simon

source("http://www-rcf.usc.edu/~rwilcox/Rallfun-v16")

ancova(covGrp1, dvGrp1, covGrp2, dvGrp2)
ancboot(covGrp1, dvGrp1, covGrp2, dvGrp2, nboot = 2000)


However, i always get the following errors (note that i get these errors when i am running the analysis on a specific data set.  The same code works fine with other data sets):

> ancova(covGrp1, dvGrp1, covGrp2, dvGrp2)
[1] "NOTE: Confidence intervals are adjusted to control the probability"
[1] "of at least one Type I error."
[1] "But p-values are not"
Error in if (nuhat < 2) stop("The degrees of freedom must be greater than or equal to 2") : 
  missing value where TRUE/FALSE needed
In addition: Warning messages:
1: In min(sub[vecn >= 12]) :
  no non-missing arguments to min; returning Inf
2: In max(sub[vecn >= 12]) :
  no non-missing arguments to max; returning -Inf
3: In near(x1, x1[isub[i]], fr1) : NAs introduced by coercion
4: In near(x2, x1[isub[i]], fr2) : NAs introduced by coercion
5: NAs introduced by coercion 

> ancboot(covGrp1, dvGrp1, covGrp2, dvGrp2, nboot = 2000)
[1] "Note: confidence intervals are adjusted to control FWE"
[1] "But p-values are not adjusted to control FWE"
[1] "Taking bootstrap samples. Please wait."
Error in if (nuhat < 2) stop("The degrees of freedom must be greater than or equal to 2") : 
  missing value where TRUE/FALSE needed
In addition: Warning messages:
1: In min(sub[vecn >= 12]) :
  no non-missing arguments to min; returning Inf
2: In max(sub[vecn >= 12]) :
  no non-missing arguments to max; returning -Inf
3: In near(x1, x1[isub[i]], fr1) : NAs introduced by coercion
4: In near(x2, x1[isub[i]], fr2) : NAs introduced by coercion
5: In near(x1, x1[isub[i]], fr1) : NAs introduced by coercion
6: In near(x2, x1[isub[i]], fr2) : NAs introduced by coercion

Any help with how i van solve this would be appreciated as i am really stack and don't really know what to do.

Best,
Kyriakos


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