Related
Following Exercise 3 of the mlogit package https://cran.r-project.org/web/packages/mlogit/vignettes/e3mxlogit.html, but attempting to use my own data (see below)
structure(list(Choice.Set = c(4L, 5L, 7L, 8L, 10L, 12L), Alternative = c(2L,
1L, 1L, 2L, 2L, 2L), respondent = c(1L, 1L, 1L, 1L, 1L, 1L),
code = c(7L, 9L, 13L, 15L, 19L, 23L), Choice = c(1L, 1L,
1L, 1L, 1L, 1L), price1 = c(0L, 0L, 1L, 1L, 0L, 0L), price2 = c(0L,
1L, 0L, 0L, 1L, 1L), price3 = c(0L, 0L, 0L, 0L, 0L, 0L),
price4 = c(1L, 0L, 0L, 0L, 0L, 0L), price5 = c(0L, 0L, 0L,
0L, 0L, 0L), zone1 = c(0L, 0L, 0L, 1L, 1L, 1L), zone2 = c(0L,
0L, 0L, 0L, 0L, 0L), zone3 = c(1L, 0L, 1L, 0L, 0L, 0L), zone4 = c(0L,
1L, 0L, 0L, 0L, 0L), lic1 = c(0L, 0L, 0L, 0L, 0L, 0L), lic2 = c(1L,
0L, 1L, 0L, 1L, 1L), lic3 = c(0L, 1L, 0L, 1L, 0L, 0L), enf1 = c(0L,
0L, 1L, 0L, 1L, 0L), enf2 = c(0L, 0L, 0L, 1L, 0L, 1L), enf3 = c(1L,
1L, 0L, 0L, 0L, 0L), chid = 1:6), row.names = c(4L, 5L, 7L,
8L, 10L, 12L), class = "data.frame")
I have run into an error when running the code:
dfml <- dfidx(df, idx=list(c("chid", "respondent")),
choice="Alternative", varying=6:20, sep ="")
"Error in reshapeLong(data, idvar = idvar, timevar = timevar, varying = varying, :
'varying' arguments must be the same length"
I have check the data and each col from 6:20 is the same length, however, some respondents chose some of the options more than the others. Can someone possibly point out where I have gone wrong? It's my first attempt at analyzing choice experiment data.
The error means, that your price has five options, whereas the others, zone, lic, enf have less. dfidx obviously can't handle that. You need to provide them, at least as NA columns.
df <- transform(df, zone5=NA, lic4=NA, lic5=NA, enf4=NA, enf5=NA)
library(mlogit)
dfml <- dfidx(df, idx=list(c("chid","respondent")), choice="Alternative",
varying=grep('^price|^zone|^lic|^enf', names(df)), sep="")
dfml
# ~~~~~~~
# first 10 observations out of 30
# ~~~~~~~
# Choice.Set Alternative code Choice price zone lic enf idx
# 1 4 FALSE 7 1 0 0 0 0 1:1
# 2 4 TRUE 7 1 0 0 1 0 1:2
# 3 4 FALSE 7 1 0 1 0 1 1:3
# 4 4 FALSE 7 1 1 0 NA NA 1:4
# 5 4 FALSE 7 1 0 NA NA NA 1:5
# 6 5 TRUE 9 1 0 0 0 0 2:1
# 7 5 FALSE 9 1 1 0 0 0 2:2
# 8 5 FALSE 9 1 0 0 1 1 2:3
# 9 5 FALSE 9 1 0 1 NA NA 2:4
# 10 5 FALSE 9 1 0 NA NA NA 2:5
#
# ~~~ indexes ~~~~
# chid respondent id2
# 1 1 1 1
# 2 1 1 2
# 3 1 1 3
# 4 1 1 4
# 5 1 1 5
# 6 2 1 1
# 7 2 1 2
# 8 2 1 3
# 9 2 1 4
# 10 2 1 5
# indexes: 1, 1, 2
I use grep here to identify the varying= columns. Get rid of the habit of lazily specifying variables as numbers; it's dangerous since order might change easily with small changes in the script.
I have a matrix that represents social interaction data on a CSV, which looks like below:
`0` `1` `2` `3` `4` `5` `6` `7` `8` `9`
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
0 0 29 1 0 1 9 3 0 1 4
1 1 0 0 1 3 1 0 1 1 1
2 1 1 0 13 4 0 1 1 15 0
3 3 0 1 0 1 1 7 1 1 1
4 1 0 1 98 0 1 1 1 1 2
5 2 5 1 1 3 0 2 0 1 5
6 1 1 0 0 12 1 0 2 1 1
7 1 1 0 1 0 1 9 0 1 2
8 1 1 17 13 145 1 39 1 0 1
9 88 23 1 5 1 2 1 7 1 0
I am new to social network analysis, so I am not sure of my terminology, but this seems like a weighted adjacency matrix to me, as we can say from this that student 1 has had 29 interactions with student 0 in the last year. I had this object stored as a data-frame in my RStudio, but when I ran the following code, I received the below error:
> fn <- graph_from_adjacency_matrix(output, weighted = T)
Error in mde(x) : 'list' object cannot be coerced to type 'double'
I've tried converting it to matrix, but that does not seem to work either. Any help concerning this would be really appreciated.
You need to convert your data.frame to matrix first and then apply graph_from_adjacency_matrix, e.g.,
g <- graph_from_adjacency_matrix(as.matrix(df),weighted = TRUE)
and plot(g) gives
Data
> dput(df)
structure(list(``0`` = c(0L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L,
88L), ``1`` = c(29L, 0L, 1L, 0L, 0L, 5L, 1L, 1L, 1L, 23L), ``2`` = c(1L,
0L, 0L, 1L, 1L, 1L, 0L, 0L, 17L, 1L), ``3`` = c(0L, 1L, 13L,
0L, 98L, 1L, 0L, 1L, 13L, 5L), ``4`` = c(1L, 3L, 4L, 1L, 0L,
3L, 12L, 0L, 145L, 1L), ``5`` = c(9L, 1L, 0L, 1L, 1L, 0L, 1L,
1L, 1L, 2L), ``6`` = c(3L, 0L, 1L, 7L, 1L, 2L, 0L, 9L, 39L, 1L
), ``7`` = c(0L, 1L, 1L, 1L, 1L, 0L, 2L, 0L, 1L, 7L), ``8`` = c(1L,
1L, 15L, 1L, 1L, 1L, 1L, 1L, 0L, 1L), ``9`` = c(4L, 1L, 0L, 1L,
2L, 5L, 1L, 2L, 1L, 0L)), class = "data.frame", row.names = c("0",
"1", "2", "3", "4", "5", "6", "7", "8", "9"))
I have a dataset like this:
> dput(head(BurnData))
structure(list(Treatment = c(0L, 0L, 0L, 0L, 0L, 0L), Gender = c(0L,
0L, 0L, 0L, 0L, 0L), Race = c(0L, 1L, 1L, 0L, 1L, 1L), Surface = c(15L,
20L, 15L, 20L, 70L, 20L), head = c(0L, 0L, 0L, 1L, 1L, 1L), buttock = c(0L,
0L, 0L, 0L, 1L, 0L), trunk = c(1L, 1L, 0L, 1L, 1L, 1L), `upper leg` = c(1L,
0L, 1L, 0L, 1L, 0L), `lower leg` = c(0L, 0L, 1L, 0L, 0L, 0L),
`respiratory tract` = c(0L, 0L, 0L, 0L, 0L, 0L), type = c(2L,
4L, 2L, 2L, 2L, 4L), `excision time` = c(12L, 9L, 13L, 11L,
28L, 11L), excision = c(0L, 0L, 0L, 1L, 1L, 0L), `antibiotic time` = c(12L,
9L, 13L, 29L, 31L, 11L), antibiotic = c(0L, 0L, 0L, 0L, 0L,
0L), infection_t = c(12L, 9L, 7L, 29L, 4L, 8L), infection = c(0L,
0L, 1L, 0L, 1L, 1L)), .Names = c("Treatment", "Gender", "Race",
"Surface", "head", "buttock", "trunk", "upper leg", "lower leg",
"respiratory tract", "type", "excision time", "excision", "antibiotic time",
"antibiotic", "infection_t", "infection"), row.names = c(NA,
6L), class = "data.frame")
I am trying to create a new variable which combines the indicators head, buttock, trunk, upper leg, lower leg, respiratory tract into ONE new indicator variable where 0 is when all indicators are zero, 1 - only head, 2 - only buttock, 3 ... , 7 - only respiratory tract and 8 - combination of any of them.
I have been trying to do this with mutate, dplyr but i cannot get it right. I am not very good at this.
Here is an approach with base R using an ifelse statement.
ifelse(rowSums(d1[5:10]) > 1, 8,
ifelse(rowSums(d1[5:10]) == 0, 0, max.col(d1[5:10])))
#1 2 3 4 5 6
#8 3 8 8 8 8
You can also try a case_when using the tidyverse
library(tidyverse)
d %>%
select(head:`respiratory tract`) %>%
mutate(res=case_when(rowSums(.) == 0 ~ 0,
rowSums(.) > 1 ~ 8,
head == 1 ~ 1,
buttock == 1 ~ 2,
trunk == 1 ~ 3,
`upper leg`==1 ~ 4,
`lower leg`==1~5,
`respiratory tract`==1 ~ 6)) %>%
select(res) %>%
bind_cols(d,.)
Treatment Gender Race Surface head buttock trunk upper leg lower leg respiratory tract type
1 0 0 0 15 0 0 1 1 0 0 2
2 0 0 1 20 0 0 1 0 0 0 4
3 0 0 1 15 0 0 0 1 1 0 2
4 0 0 0 20 1 0 1 0 0 0 2
5 0 0 1 70 1 1 1 1 0 0 2
6 0 0 1 20 1 0 1 0 0 0 4
excision time excision antibiotic time antibiotic infection_t infection res
1 12 0 12 0 12 0 8
2 9 0 9 0 9 0 3
3 13 0 13 0 7 1 8
4 11 1 29 0 29 0 8
5 28 1 31 0 4 1 8
6 11 0 11 0 8 1 8
Or completely using the elegant solution of Sotos
mutate(res=case_when(rowSums(.) == 0 ~ 0L,
rowSums(.) > 1 ~ 8L,
TRUE ~ max.col(.)))
This is a question about the fourthcorner algorithm in R. It's designed to measure the relationship between three different tables: an n x m table (table R) of m environmental variables (columns) at n sites (rows), an n x p table (table L) of p abundances (columns) at n sites (rows), and a p x s table (table Q) of s traits (columns) for p species (rows).
The fourthcorner function is in the package ade4.
All three of my dataframes are binary (0s and 1s denoting the presence or absence of a variable, a species at a site, or a trait, respectively). I've tried using "yes" and "no" instead of 0s and 1s without success.
Here are some example matrices in the format I'm using:
tabQ
Trait1 Trait2 Trait3 Trait4
Sp1 0 1 0 0
Sp2 0 1 0 0
Sp3 1 0 1 0
Sp4 1 0 1 0
Sp5 0 1 0 0
Sp6 0 1 0 0
Sp7 0 0 0 1
Sp8 0 0 0 1
tabR
EnV1 EnV2 EnV3 EnV4
Site1 1 1 1 1
Site2 1 1 0 1
Site3 0 1 0 1
Site4 1 1 1 1
Site5 1 1 0 1
Site6 0 1 0 0
Site7 0 1 0 1
Site8 0 1 0 1
Site9 1 1 1 1
Site10 1 1 0 1
Site11 1 1 1 1
Site12 0 1 0 0
Site13 1 1 0 1
Site14 1 1 0 1
Site15 0 1 0 1
Site16 1 1 0 1
Site17 0 1 0 1
Site18 1 1 1 1
Site19 1 1 0 1
Site20 1 1 0 1
tabL
Sp1 Sp2 Sp3 Sp4 Sp5 Sp6 Sp7 Sp8
Site1 1 1 0 0 0 0 0 0
Site2 1 1 0 0 0 0 0 0
Site3 1 1 0 0 0 0 0 0
Site4 1 0 0 0 0 0 0 1
Site5 1 1 0 0 0 0 0 0
Site6 1 0 0 0 1 0 0 0
Site7 1 0 0 0 0 0 0 0
Site8 0 0 0 0 1 0 0 0
Site9 1 0 0 0 0 0 0 0
Site10 1 1 0 0 0 0 0 0
Site11 0 0 1 1 0 0 0 0
Site12 0 0 0 0 0 1 0 0
Site13 1 0 0 0 0 0 0 0
Site14 0 0 0 0 1 0 0 0
Site15 1 1 0 0 0 0 0 0
Site16 1 1 0 0 0 0 0 0
Site17 1 0 0 0 0 0 0 0
Site18 0 0 1 0 0 0 0 0
Site19 1 0 0 0 0 0 0 0
Site20 1 1 0 0 0 0 1 0
I read these dataframes into R from text files, and I specify that the first column is row names.
This is the error I get when I try to use the fourthcorner function on my matrices:
fourth1=fourthcorner(tabR,tabL,tabQ,nrepet=1)
Error in apply(sim, 2, function(x) length(na.omit(x))) :
dim(X) must have a positive length
I don't understand where the problem lies, is it a formatting issue? If so, should I reformat one of the matrices? Which one is causing the trouble? Or can I not use binary traits and environmental variables for this function? In other words, can I solve this problem by changing a piece of code, or is it impossible to use this function for this question?
As an additional tidbit of information, I did email the author of the function, but unfortunately I did not understand his response fully, possibly because my R skills still leave much to be desired. Here is his response if it is helpful:
Q could contain quantitative or qualitative traits. In R, qualitative traits should be coded as factors to obtain adapted statistics (i.e. chi2 or eta2). If you code qualitative variables as dummy variables, they would be considered as quantitative.
Thank you very much to any and all insight.
I noted that your example fails only nrepet is equal to one, so if you can use any other positive number you should be fine.
However, if you do need nrepet=1, you should contact with the author of ade4 and ask to him/her to fix the fourthcorner function code. I traced back the error and found that fourthcorner calls as.krandtest with sim = res$tabD[-1,] where res$tabD is a matrix with nrepet+1 rows. When nrepet=1 and you remove one row from a two-row matrix, R automatically converts the resulting one-row matrix into a vector, but as.krandtest function expects sim to be a matrix and thus raises the error.
Here is your input data just in case somebody else would like to answer your question:
tabR
structure(list(EnV1 = c(1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L), EnV2 = c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), EnV3 = c(1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L), EnV4 = c(1L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("EnV1",
"EnV2", "EnV3", "EnV4"), row.names = c("Site1", "Site2", "Site3",
"Site4", "Site5", "Site6", "Site7", "Site8", "Site9", "Site10",
"Site11", "Site12", "Site13", "Site14", "Site15", "Site16", "Site17",
"Site18", "Site19", "Site20"), class = "data.frame")
tabL
structure(list(Sp1 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L), Sp2 = c(1L, 1L, 1L,
0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
1L), Sp3 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L), Sp4 = c(0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
Sp5 = c(0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L), Sp6 = c(0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), Sp7 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L), Sp8 = c(0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L)), .Names = c("Sp1", "Sp2", "Sp3", "Sp4", "Sp5", "Sp6",
"Sp7", "Sp8"), row.names = c("Site1", "Site2", "Site3", "Site4",
"Site5", "Site6", "Site7", "Site8", "Site9", "Site10", "Site11",
"Site12", "Site13", "Site14", "Site15", "Site16", "Site17", "Site18",
"Site19", "Site20"), class = "data.frame")
tabQ
structure(list(Trait1 = c(0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L), Trait2 = c(1L,
1L, 0L, 0L, 1L, 1L, 0L, 0L), Trait3 = c(0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L), Trait4 = c(0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L)), .Names = c("Trait1",
"Trait2", "Trait3", "Trait4"), row.names = c("Sp1", "Sp2", "Sp3",
"Sp4", "Sp5", "Sp6", "Sp7", "Sp8"), class = "data.frame")
I have a table that looks like this:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
586 0 0 0 1 0 0 0 1 3 1 0 1 0 0 0 0 0 1 0 2 0 3 0 0 0 4 0 1 2 0
637 0 0 0 0 0 0 2 3 2 2 0 4 0 0 0 0 1 0 0 2 0 1 1 1 0 0 0 0 0 1
989 0 0 1 0 0 0 2 1 0 0 0 2 1 0 0 1 2 1 0 3 0 2 0 1 1 0 1 0 1 0
1081 0 0 0 1 0 0 1 0 1 1 0 0 2 0 0 0 0 0 0 3 0 5 0 0 2 1 0 1 1 1
2922 0 1 1 1 0 0 0 2 1 0 0 0 2 0 0 0 1 1 0 1 0 3 1 1 2 0 0 1 0 1
3032 0 1 0 0 0 0 0 3 0 0 1 0 2 1 0 1 0 1 1 0 0 3 1 1 1 1 0 0 1 1
Numbers 1 to 30 in the first row are my labels, and the columns are my items. I would like to find, for each item, the label with the most counts. E.g. 586 has 4 counts of 26, which is the highest number in that row, so for 586, I would like to assign 26.
I am able to get the maximum value for each row with max(table1[1,])), which gets me the maximum value for first row, but doesn't get me the label it corresponds to, but I don't know how to proceed. All help is appreciated!
dput:
structure(c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 2L, 1L, 0L, 0L, 1L, 3L, 1L,
0L, 2L, 3L, 3L, 2L, 0L, 1L, 1L, 0L, 1L, 2L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 4L, 2L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 2L,
2L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 1L, 2L, 2L, 3L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 3L, 1L, 2L, 5L, 3L, 3L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, 0L, 0L, 1L, 2L, 2L, 1L, 4L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 2L, 0L, 1L, 1L, 0L,
1L, 0L, 1L, 0L, 1L, 1L, 1L), .Dim = c(6L, 30L), .Dimnames = structure(list(
c("586", "637", "989", "1081", "2922", "3032"), c("1", "2",
"3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23",
"24", "25", "26", "27", "28", "29", "30")), .Names = c("",
"")))
max.col will give you vector of column numbers which correspond to maximum value for each row.
> max.col(df, tie='first')
[1] 26 12 20 22 22 8
You can use that vector to get column names for each row.
> colnames(df)[max.col(df, tie='first')]
[1] "26" "12" "20" "22" "22" "8"
Perhaps you are looking for which.max. Assuming your matrix is called "temp":
> apply(temp, 1, which.max)
586 637 989 1081 2922 3032
26 12 20 22 22 8
apply with MARGIN = 1 (the second argument) will apply a function by row.