I try to run a Latent Profile Analysis in R with the package mclust. I have four variables, they are continuous (and there are some 0.00). First I say read.csv2("…") and then BIC<-Mclust(data2).
Afterwards this error occurs:
Error in if (loglik > signif(.Machine$double.xmax, 6) || any(!c(scale, : missing value where TRUE/FALSE needed
My data looks like that:
'data.frame': 219 obs. of 4 variables:
$ Var1: num 0.55 0.36 0.09 0.27 1.45 0.45 1.18 3 1.09 0.18 …
$ Var2: num 0 0.22 0.33 0.67 0.22 1 1.67 0 2.33 0 …
$ Var3: num 0 0 0 0 0 0.38 0 0 0.88 0 …
$ Var4: num 0 0 0 0 0 0 0 0 0 0 …
What is wrong here? What am I doing wrong?
I would be very glad if someone could help me.
Thank you
Related
I'm trying to create linear mixed model to explain the presence / absence of a species according to 30 fixed environmental variables and 2 random variables ("Location" and "Season"). My data looks like this:
str(glmm_data)
'data.frame': 209 obs. of 40 variables:
$ CODE : Factor w/ 209 levels "VAL1_1","VAL1_2",..: 1 72 142 170 176 183 190 197 203 8 ...
$ Location : Factor w/ 32 levels "ALMENARA","ARES 1",..: 10 11 12 15 17 2 3 4 21 18 ...
$ Season : Factor w/ 7 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
$ PO4 : num -1.301 -1.301 -1.301 0.437 -1.301 ...
$ NO2 : num -1.129 -1.629 -0.781 -1.699 -1.654 ...
$ NO3 : num 1.044 0.115 1.918 1.457 1.467 ...
$ NH4 : num 0.0123 -0.014 -1.301 -0.2772 -1.301 ...
$ ChlA : num 0.341 0.117 0.87 -0.699 1.53 ...
$ Secchi : num 29 23 10 17 20 9 22 25 25 24 ...
$ Temp_w : num 5.4 3.2 10.3 10.5 4.7 7.2 8 9.2 4.6 6.9 ...
$ Conductivity : num 2.74 2.52 2.76 2.36 2.66 ...
$ Oxi_conc : num 11.6 9.2 7.04 9.99 7 ...
$ Hydroperiod : int 0 0 0 0 1 0 1 0 0 0 ...
$ Rain : int 1 1 1 1 1 1 1 1 1 1 ...
$ RainFre : int 0 0 0 0 0 0 0 0 0 0 ...
$ Veg_flo : num 0 0 0 0 0 0 0 0 0 0 ...
$ Veg_emg : num 0.735 0.524 0.226 0.685 0.226 ...
$ Depth_max : num 1.64 1.57 1.18 1.11 1.85 ...
$ Agricultural : num 0 0 0 0 0 ...
$ LowGrass : num 0 0.41 0.766 0 0.856 ...
$ Forest : num 1.097 1.161 0.44 1.05 0.502 ...
$ Buildings : num 0 0 0 0 0 ...
$ Heterogeneity : num 0.512 0.437 1.028 0.559 0.98 ...
$ Morphology : num 0.04519 -0.00115 0.01556 0.00771 0.12125 ...
$ Fish : int 0 0 0 0 0 0 0 0 0 0 ...
$ TempRange : num 1.4 1.4 1.4 1.4 1.4 ...
$ Tavg : num 1.03 1 1.03 1.03 1 ...
$ Precipitation : num 2.8 2.82 2.8 2.81 2.8 ...
$ MatOrg : num 0.264 0.257 0.236 0.251 0.313 ...
$ CO3 : num 0.14 0.163 0.222 0.335 0.306 ...
$ PC1 : num -0.132 -0.186 -0.074 0.127 -0.175 ...
$ PC2 : num -0.0729 0.0568 -0.0428 -0.0688 -0.0464 ...
$ PC3 : num -0.00638 0.01857 0.02817 -0.00918 0.02056 ...
$ Alytes_obstetricans : int 0 0 0 0 0 0 1 0 0 0 ...
$ Bufo_spinosus : int 0 0 0 0 0 0 0 0 0 0 ...
$ Epidalea_calamita : int 0 0 0 0 0 0 0 0 0 0 ...
$ Pelobates_cultripes : int 0 0 0 0 0 0 0 0 0 0 ...
$ Pelodytes_hespericus: int 1 0 0 0 0 0 0 0 0 0 ...
$ Pelophylax_perezi : int 0 0 0 0 1 0 1 0 0 0 ...
$ Pleurodeles_waltl : int 0 0 0 0 0 0 0 0 0 0 ...
PS: if anyone knows a better way to show my data please explain, I'm a noob at this.
The last 7 columns are the response variables, namely presence (1) or absence (0) of said species so my response variables are binomial. I'm using the glmer function from the lme4 package.
I'm trying to create a model for each species. So the first one looks like this:
Aly_Obs_GLMM <- glmer(Alytes_obstetricans ~ PO4 + NO2 + NO3 + NH4 + ChlA +
Secchi + Temp_w + Conductivity + Oxi_conc + Hydroperiod + Rain + RainFre +
Veg_flo + Veg_emg + Depth_max + Agricultural + LowGrass + Forest + Buildings +
Heterogeneity + Morphology + Fish + TempRange + Tavg + Precipitation +
MatOrg + CO3 + PC1 + PC2 + PC3 + (1|Location) + (1|Season), family = binomial,
data = glmm_data
)
However when running the code, I get the followed error message:
Error in pwrssUpdate(pp, resp, tol = tolPwrss, GQmat = GHrule(0L),
compDev = compDev, : Downdated VtV is not positive definite
and the model fails to create.
Any ideas on what I may be doing wrong? Thanks
While trying to determine the optimal number of clusters for a kmeans, I tried to use the package mclust with the following code :
d_clust <- Mclust(df,
G=1:10,
mclust.options("emModelNames"))
d_clust$BIC
df is a data frame of 132656 obs. of 19 variables, the data is scaled, and there is no missing values (no NA/NaN/Inf values I checked with is.na and is.finite). Also, my variables are all in numeric format thanks to as.numeric
However after using the code, the screen displays "fitting" with a loading bar, goes up to 11%, and then after a moment I get the error message :
NAs in foreign function call (arg 13)
Does anyone know why I have this type of error ?
EDIT
Output of str(df) (I modified the variable name because of confidential issues)
'data.frame': 132656 obs. of 19 variables:
$ X1: num 0.5 1 1 1 0.5 1 1 1 1 1 ...
$ X2: num 0.714 0.286 1 0.857 0.286 ...
$ X3: num 0.667 1 0.667 0.667 0.667 ...
$ X4: num 0.714 0.429 1 0.714 0.429 ...
$ X5: num 0.667 0.333 1 0.667 0.333 ...
$ X6: num 0.5 0.25 1 0.5 0.25 0.25 0 0.5 0.5 0.25 ...
$ X7: num 0.667 0.667 0.667 0.667 0.667 ...
$ X8: num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
$ X9: num 0.667 0 0.667 0.333 0 ...
$ X10: num 1 0.833 1 1 1 ...
$ X11: num 1 0.75 1 1 1 1 1 1 1 1 ...
$ X12: num 1 1 1 0.8 1 1 1 1 1 1 ...
$ X13: num 0.5 0.75 0.75 0.5 0.75 0.25 0.75 0.5 0.5 0.5 ...
$ X14: num 0.75 0.75 0.75 1 0.75 0.75 0.75 1 0.75 0.75 ...
$ X15: num 1 0 0.5 1 1 1 0.75 1 0.5 1 ...
$ X16: num 1 0.333 0.667 0.833 0.833 ...
$ X17: num 1 1 1 1 1 1 1 1 1 1 ...
$ X18: num 0.00157 0.000438 0.001059 0.000879 0.004919 ...
$ X19: num 0.5 0.125 1 0.625 0.125 0.125 0.125 1 0.5 0.25 ...
This is my data:
'data.frame': 72 obs. of 7 variables:
$ X1 : chr "2011M1" "2011M2" "2011M3" "2011M4" ...
$ KPR : int 0 0 0 0 0 0 0 0 0 0 ...
$ LTV : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
$ sukubunga: num 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 ...
$ inflasi : num 0.89 0.13 -0.32 -0.31 0.12 0.55 0.67 0.93 0.27 -0.12 ...
$ npl : num 2.31 2.39 2.22 2.2 2.12 ...
$ sbkredit : num 11.4 11.4 11.3 11.4 11.3 ...
i use the package glarma and this is my steps:
library(readr)
b <- read_csv("E:/b.csv")
dataku<-as.data.frame(b)
dataku$LTV<-as.factor(dataku$LTV)
dataku$LTV<-relevel(dataku$LTV,ref="0")
glmmo<-glm(KPR~LTV+sbkredit+inflasi+npl,data=dataku,family=binomial(link=logit),na.action=na.omit,x=TRUE)
summary(glmmo)
X<-glmmo$x
X<-as.matrix(X)
y1<-dataku$KPR
n1<-rep(1,length(dataku$X))
Y<-cbind(y1,n1-y1)
Y<-as.matrix(Y)
library(glarma)
glarmamo<-glarma(Y,X,phiLags=c(1),phiInit=c(0.6),type="Bin",method="FS",residuals="Pearson",maxit=100,grad=1e-6)
but, i get error :
Error in GL$cov %*% GL$ll.d : requires numeric/complex matrix/vector
arguments
When i multiply GL$cov %*% GL$ll.d for
so, what should i do?
I have a problem when use smbinning package.
I have dataset consists of ratio and Good_Bad:
ratio:
0.40 0.41 0.54 0.61 0.64 0.70 0.74 0.74 0.78 0.79 0.80 0.81 0.82 0.83 0.87 0.89 0.89 1.03 1.03 1.06 1.07 1.08 1.08 1.09 1.09 1.10 1.12 1.12 1.13 1.15 1.18 1.20 1.23 1.24 1.24 1.33 1.33 1.36 1.38 1.38 1.39 1.40 1.42 1.44 1.47 1.48 1.48 1.53 1.55 1.55 1.60 1.62 1.65 1.67 1.72 1.73 1.74 1.75 1.85 1.86 1.89 1.90 2.02 2.04 2.07 2.09 2.18 2.20 2.22 2.24 2.39 2.41 2.43 2.46 2.76 2.85 2.91 3.05 3.75 4.21 5.18 5.33 8.70
Good_Bad:
0 0 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 1 0 0 1 0 0 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 1 0 0 1 1 1 0 1 0 0 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0
Code:
binning <- smbinning(df=dataset, y="Good_Bad", x="ratio", p=0.05)
binning$ivtable
Error in binning$ivtable : $ operator is invalid for atomic vectors
binning
[1] "No Bins"
Why error and the result is "No Bins"?
Have you checked that the column "ratio" is stored as a numeric?
If it's stored as a factor or character you'd need to use
smbinning.factor()
I have a list of data frames (list9).
> str(list9)
List of 2
$ :'data.frame': 64 obs. of 11 variables:
..$ list$Stimulus : Factor w/ 7 levels "108.wav","42.wav",..: 1 1 1 1 1 1 1 1 2 2 ...
..$ list$IndicationStandard: num [1:64] 1 0 1 0 1 0 0 0 0 0 ...
..$ list$P42 : num [1:64] 0 0 0 0 0 0 0 0 0 0 ...
..$ list$P53 : num [1:64] 0 0 0 0 0 0 0 0 0 0 ...
..$ list$P64 : num [1:64] 0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375 ...
..$ list$P75 : num [1:64] 0.812 0.812 0.812 0.812 0.812 ...
..$ list$P86 : num [1:64] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
..$ list$P97 : num [1:64] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 ...
..$ list$P108 : num [1:64] 0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375 ...
..$ list$TGdispInd1 : num [1:64] 0.317 0.317 0.317 0.317 0.317 ...
..$ list$TGdispInd2 : num [1:64] 0.756 0.756 0.756 0.756 0.756 ...
$ :'data.frame': 64 obs. of 11 variables:
..$ list$Stimulus : Factor w/ 7 levels "108.wav","42.wav",..: 1 1 1 1 1 1 1 1 2 2 ...
..$ list$IndicationStandard: num [1:64] 0 0 1 0 1 0 0 0 0 0 ...
..$ list$P42 : num [1:64] 0 0 0 0 0 0 0 0 0 0 ...
..$ list$P53 : num [1:64] 0 0 0 0 0 0 0 0 0 0 ...
..$ list$P64 : num [1:64] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 ...
..$ list$P75 : num [1:64] 0.812 0.812 0.812 0.812 0.812 ...
..$ list$P86 : num [1:64] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
..$ list$P97 : num [1:64] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 ...
..$ list$P108 : num [1:64] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 ...
..$ list$TGdispInd1 : num [1:64] 0.351 0.351 0.351 0.351 0.351 ...
..$ list$TGdispInd2 : num [1:64] 0.784 0.784 0.784 0.784 0.784 ...
I created a target data frame (result)
> str(result)
'data.frame': 2 obs. of 3 variables:
$ TGdispInd1: num 0 0
$ TGdispInd2: num 0 0
$ subject : chr "TG_75ms_Step11_V1-998-1.txt" "TG_75ms_Step11_V1-999-1.txt"
I would like to paste the first value of list$TGdispInd1 and list$TGdispInd2 of each data frame in the list into the data frame "result" (it could also be the mean of list$TGdispInd1 and list$TGdispInd2, since all 64 values are equal).
This is how the resulting data frame should look like
> result
TGdispInd1 TGdispInd2 subject
1 .317 .756 TG_75ms_Step11_v1-998-1.txt
2 .351 .784 TG_75ms_Step11_v1-999-1.txt
Does anybody know how to do this?
Try
result[1:2] <- do.call(rbind,lapply(list9, function(x)
x[1, c('list$TGdispInd1', 'list$TGdispInd2']))
If you are interested in the mean value
result[1:2] <- do.call(rbind, lapply(list9, function(x)
colMeans(x[c('list$TGdispInd1', 'list$TGdispInd2'])))