I am using arulesCBA on dataset of words with class attribute which is polarity to be positive or negative. first, I am converting the words to numeric values by using as.numeric function. after that, I am discretizing the columns using this code:
trans.disc <- as.data.frame(lapply(df[2:75], function(x) discretize(x, categories=9)))
in this step, I have warnings that say: parameter categories is deprecated. Use breaks instead! Also, the default method is now frequency!the next step that I am applying is adding the polarity column :
trans.disc$polarity <- df$polarity
the last step, I am trying to build the classifier:
classifier <- CBA(trans.disc, "polarity", supp = 0.05, conf=0.9)
in this phase, there is an error message that says: (Error in discretizeDF.supervised(formula, data, method = disc.method) :data needs to be a data.frame).
It looks like you have the arguments for CBA moxed up. The man page ?CBA says:
CBA(
formula,
data,
pruning = "M1",
parameter = NULL,
control = NULL,
balanceSupport = FALSE,
disc.method = "mdlp",
verbose = FALSE,
...
)
I am using a lme object within a function but it does not use the response variable that I feed it, instead it uses the response variable from the previous time I called the function.
library(nlme)
library(car)
# DATA (Example)
S1=data.frame(blok = c(rep("blokI",16),rep("blokII",16)),
treat=rep(c("plus","plus","min","min"),8),
field = c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,15,16,16),
subfield = rep(c("a","b"),16),
var1=rnorm(32)^2,
var2=rnorm(32)^2)
S2=data.frame(blok = c(rep("blokI",16),rep("blokII",16)),
treat=rep(c("plus","plus","min","min"),8),
field = c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,15,16,16),
subfield = rep(c("a","b"),16),
var3=rnorm(32)^2,
var4=rnorm(32)^2)
# FUNCTION
get.stats = function(S,dofor){
U = data.frame(crop = NA, C = NA, T_S=NA, T_F=NA, T_DF=NA, T_P=NA, R_S=NA, R_F=NA, R_DF=NA, R_P=NA, TR_S=NA,TR_F=NA, TR_DF=NA, TR_P=NA)
for (i in 1:length(dofor)){
f = as.formula(paste(dofor[i]," ~ treat",sep=""))
model = lme(f, data=S, random = ~1|blok/treat/field/subfield)
av = Anova(model, type = "II")
U[i,1] = NaN
U[i,2] = dofor[i]
U[i,3] = av$`Chisq`[1]
U[i,4] = NaN
U[i,5] = av$Df[1]
U[i,6] = av$`Pr(>Chisq)`[1]
U[i,7] = av$`Chisq`[2]
U[i,8] = NaN
U[i,9] = av$Df[2]
U[i,10] = av$`Pr(>Chisq)`[2]
U[i,11] = av$`Chisq`[3]
U[i,12] = NaN
U[i,13] = av$Df[3]
U[i,14] = av$`Pr(>Chisq)`[3]
}
return(U)
}
dofor=c("var1","var2")
U = get.stats(S1, dofor) # First call of function
dofor=c("var3","var4")
U = rbind(U, get.stats(S2, dofor)) # Second call of function
If I call the function the first time I get Error in eval(x$call$fixed) : object 'f' not found, which is already strange to me as f is defined within the function. I execute the command line where f is defined for i = 1 and call the function again. Now it works, but when I call the function a second time with S2 as data input I get the error:
Error in eval(predvars, data, env) : object 'var2' not found
I have to repeat this model for a many variables on yield of different crops for different years. With this function I intend to collect all the statistics for the different crop species in one matrix which I can latter edit in an Excel sheet.
Any suggestions on how to solve or by-pass my problem?
I want to run a decision tree for each variable in my dataframe, so I'm using this:
results_cont = list()
for (i in 2:(ncol(DATA)-1)) {
current_var = colnames(DATA[i])
current_result = ctree(TARGET ~ current_var, DATA, control = ctrl)
results_cont[[i]] = current_result
}
Where DATA is a dataframe where the first column is the ID and the last column (TARGET) is my binary Target.
I keep getting this error:
Error in trafo(data = data, numeric_trafo = numeric_trafo, factor_trafo = factor_trafo, :
data class “character” is not supported
But I don't have any character in mi dataframe.
Is there anything wrong with my loop or something else ?
Thank you guys.
Since you do not provide data, I have not tested this, but I believe your problem is the line
current_result = ctree(TARGET ~ current_var, DATA, control = ctrl)
This is not working because current_var is just a character string. You need to build the formula as a string and then convert it to a formula - like this:
current_var = colnames(DATA[i])
FORM = as.formula(paste("TARGET ~ ", current_var))
current_result = ctree(FORM, DATA, control = ctrl)
I'm running a conditional logistic regression analysis on different individuals using a for statement in R. The code for this is pretty straightforward:
for(ID in unique(Hour168Fin$BAND)){
modelone = clogit(Hour168Fin$OBSERVED ~ Hour168Fin$LNSTEPLENG + Hour168Fin$PowCross + Shrub +
strata(Hour168Fin$STEPID), data=Hour168Fin, subset = which(ID==Hour168Fin$BAND))
I'm interested in very specific parts of the output, so I've structured the output to give me exactly the coefficients I need using this:
x1beta = as.numeric(summary(modelone)$coef[1,1])
x2beta = as.numeric(summary(modelone)$coef[2,1])
x3beta = as.numeric(summary(modelone)$coef[3,1])
x1SE = as.numeric(summary(modelone)$coef[1,3])
x2SE = as.numeric(summary(modelone)$coef[2,3])
x3SE = as.numeric(summary(modelone)$coef[3,3])
x1pvalue = as.numeric(summary(modelone)$coef[1,5])
x2pvalue = as.numeric(summary(modelone)$coef[2,5])
x3pvalue = as.numeric(summary(modelone)$coef[3,5])
modelAIC = AIC(modelone)
results = table(x1beta, x1SE, x1pvalue, x2beta, x2SE, x2pvalue, x2beta, x2SE, x2pvalue, modelAIC, rownames = ID)}
In R, I can see all the results in the format I'm looking for, but when I use this to get these results into a csv:
write.csv = (results, file = "TrialOut.csv")
I'm only getting the results of 1 unique ID. I've tried embedding the write.csv statement in the for statement, and using it outside of it with the same results. Any suggestions? I'm really baffled because I can see the results in R but can't seem to get that to translate to a csv.
Thanks for your time!
Try including the write.csv call inside the loop, and use append = TRUE:
for (...) {
# ...
# ...
write.csv(results, file = "someFile.csv", append = TRUE)
}
I am using a glmulti wrapper for glmer (binomial) and the summary is:
This is glmulti 1.0.7, Apr. 2013.
Length Class Mode
0 NULL NULL
Following what has been done on this this thread, though this is for lmer,
glmulti runs indefinitely when using genetic algorithm with lme4, I get the same result as above. Could it be that the versions have changed since and the wrapping has to be done differently? The following is the dummy code (lifted form the link above):
x = as.factor(round(runif(30),1))# dummy grouping factor
yind = runif(30,0,10) # mock dependent variable
a = runif(30) # dummy covariate
b = runif(30) # another dummy covariate
c = runif(30) # an another one
d = runif(30)
tmpdata <- data.frame(x=x,yind=yind,a=a,b=b,c=c,d=d)
lmer.glmulti <- function (formula, data, random = "", ...) {
lmer(paste(deparse(formula), random), data = data, REML=F, ...)
}
summary(glmulti(formula = yind~a*b*c*d,
data = tmpdata,
random = '+(1|x)',
level = 2,
method = 'h',
crit = 'aicc',
marginality = TRUE,
fitfunc = lmer.glmulti))
lme4 version: 1.1.5
glmulti version: 1.0.7
"R version 3.0.2 (2013-09-25)"
SOLUTION
This works:
lmer.glmulti <- function (formula, data, random, ...) {
lmer(paste(deparse(formula), random), data = data)
}
glmulti(y = yind~a*b*c*d,
data = tmpdata,
random = '+(1|x)',
level = 2,
method = 'h',
crit = 'aicc',
marginality = TRUE,
fitfunc = lmer.glmulti)
packageVersion('lme4')
‘1.1.5’
packageVersion('glmulti')
‘1.0.7’
R.version: 3.1.0
FYI: From the package maintainer:
"fitfunc must be the name of a function so your other call including the function definition in the glmulti call cannot work."
"you named the first argument to glmulti 'formula', where it must be unnamed or 'y'... Sorry. But y is a formula (if passing a string it is the dependent variable only). "