How to compute standard errors for predicted data - r

I am trying to generate standard errors for predicted values. I use the below code to generate the predicted values but it fails to also give the standard errors.
ord6 <- veg$ord1-2
laimod.group = lmer(log(lai+0.000019) ~ ord6*plant_growth_form +
(1|plot.code) +
(1|species.code),
data=veg,
REML=FALSE)
summary(laimod.group)
new.ord6 <- c(-1,0,1,2,3,4,5,6,7)
new.plant_growth_form <- c("fern", "grass", "herb","herbaceous climber",
"herbaceous shrub", "moss", "tree sapling",
"undet", "woody climber", "woody shrub")
newdat <- expand.grid(
ord6=new.ord6,plant_growth_form=new.plant_growth_form)
newdat$pred <- predict(laimod.group,newdat, se.fit=TRUE, re.form=NA)
newdat
comment 1: laimod.group = final model selected after comparison of five models using lmer (package lme4)
comment 2: predictSE.mer requires package AICcmodavg
I did try the below code as an alternative but continue to receive the the following error message: Error in fam.link.mer(mod) : object 'out.link' not found
newdat$pred <- predictSE.mer(laimod.group, newdat, se.fit = TRUE, type = "response",
level = 0, print.matrix = FALSE)
Please see a reproducible subset of my data:
structure(list(plot.code = structure(c(1L, 2L, 3L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 6L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L,
9L, 9L, 10L, 11L, 11L, 11L, 11L, 11L, 12L, 13L, 14L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 16L, 17L, 18L, 19L, 19L, 19L, 20L, 21L,
22L, 23L, 24L, 25L, 26L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 29L,
29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 32L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 35L, 36L, 36L, 36L, 37L, 38L, 39L, 39L, 39L,
40L, 40L, 33L, 33L), .Label = c("a100f1r", "a100m562r", "a10m562r",
"a1f56r", "a1m5r", "b100f177r", "b100m17r", "b100m5r", "c100f17r",
"c100f1r", "c100f5r", "d100m56r", "d100m5r", "d10f1r", "d10f5r",
"e100m17r(old)", "e100m1r", "e100m5r", "e10f177r", "e10f17r(old)",
"e10f5r(old)", "e1f17r", "e1f5r", "f100m177r", "f10f177r", "f10f17r",
"f1m177r", "f1m56r", "lf1f1r", "lf1f5r", "lf1m1r", "og100f5r",
"og10f1r", "og10m1r", "og10m5r", "op100f562r", "op100m177r",
"op10f1r", "op10f5r", "op10m562r"), class = "factor"), species.code = structure(c(69L,
59L, 67L, 69L, 20L, 44L, 28L, 32L, 31L, 7L, 13L, 63L, 69L, 52L,
69L, 14L, 54L, 57L, 42L, 9L, 62L, 10L, 22L, 69L, 35L, 49L, 38L,
11L, 41L, 39L, 16L, 40L, 69L, 32L, 33L, 41L, 22L, 69L, 43L, 4L,
68L, 48L, 6L, 34L, 53L, 3L, 15L, 30L, 13L, 31L, 66L, 64L, 38L,
46L, 61L, 29L, 61L, 27L, 8L, 41L, 55L, 58L, 23L, 25L, 18L, 45L,
26L, 13L, 65L, 12L, 51L, 50L, 60L, 47L, 17L, 5L, 19L, 61L, 1L,
37L, 13L, 36L, 13L, 2L, 11L, 24L, 44L, 13L, 49L, 56L, 21L), .Label = c("agetri",
"alb214", "annunk", "arimin", "baudip", "beg032", "blurip", "buc009",
"cal079", "calplu", "chrodo", "cishas", "clihir", "cos049", "cycari",
"cypunk", "cyr075", "cyrped1", "dae205", "dalpin", "diapla1",
"dio063", "diosum", "emison", "ery046", "eryborb", "fic119",
"ficmeg", "friacu", "graunk", "indunk", "jactom", "lauunk", "leeind",
"luvsar", "lyccer", "mac068", "melmal", "mergra", "miccra1",
"mikcor", "mitken", "nep127", "nepbis", "paldas", "palunk", "panunk",
"penlax", "poaunk", "pol019", "pop246", "ptecog", "ptesub1",
"rubcle", "ryphul", "scamac", "scl051", "sclsum", "selcup", "selfro",
"spa098", "sphste1", "stitrut", "tet055", "tetdie", "tetdie1",
"tetkor", "xanfla", "zinunk"), class = "factor"), plant_growth_form = structure(c(3L,
6L, 9L, 3L, 7L, 1L, 7L, 4L, 8L, 5L, 5L, 1L, 3L, 7L, 3L, 3L, 9L,
2L, 9L, 7L, 9L, 7L, 7L, 3L, 9L, 2L, 10L, 4L, 4L, 9L, 2L, 7L,
3L, 4L, 7L, 4L, 7L, 3L, 1L, 4L, 7L, 7L, 3L, 10L, 7L, 7L, 1L,
2L, 5L, 8L, 9L, 9L, 10L, 7L, 9L, 9L, 9L, 7L, 7L, 4L, 7L, 2L,
7L, 10L, 3L, 7L, 10L, 5L, 9L, 9L, 7L, 7L, 6L, 7L, 3L, 9L, 9L,
9L, 9L, 7L, 5L, 6L, 5L, 9L, 4L, 3L, 1L, 5L, 2L, 7L, 7L), .Label = c("fern",
"grass", "herb", "herbaceous climber", "herbaceous shrub", "moss",
"tree sapling", "undet", "woody climber", "woody shrub"), class = "factor"),
ord1 = c(9L, 5L, 7L, 9L, 4L, 4L, 5L, 5L, 5L, 2L, 9L, 5L,
4L, 6L, 8L, 6L, 3L, 3L, 5L, 3L, 4L, 5L, 3L, 5L, 3L, 9L, 6L,
4L, 4L, 6L, 2L, 5L, 5L, 9L, 3L, 4L, 3L, 5L, 3L, 4L, 1L, 8L,
1L, 5L, 7L, 6L, 9L, 1L, 9L, 1L, 4L, 4L, 2L, 5L, 2L, 3L, 5L,
1L, 3L, 3L, 3L, 2L, 6L, 5L, 2L, 6L, 5L, 2L, 5L, 3L, 6L, 5L,
6L, 3L, 3L, 4L, 7L, 4L, 6L, 1L, 2L, 2L, 4L, 3L, 3L, 3L, 3L,
4L, 4L, 3L, 3L), lai = c(4.525068022, 0.325399379, 0.229222148,
4.076350538, 0.006889889, 0.003279268, 0.037268428, 0.056032134,
0.013573973, 0.001304667, 0.696949844, 1.256477431, 0.122569437,
0.191398415, 1.606070777, 0.425381508, 0.03013251, 0.00181661,
0.017317993, 0.014455456, 0.102704752, 0.031065374, 0.000923601,
0.453384679, 0.017859983, 7.765697214, 0.127071322, 0.102178413,
0.049099766, 0.427983019, 4.22e-05, 0.229034333, 0.694745347,
0.068069112, 0.218354525, 0.05883256, 0.032252145, 0.304812298,
0.009320025, 0.036424481, 0, 0.326, 0.000201724, 0.286106787,
0.556249444, 0.274764132, 4.21, 0, 0.695663959, 0.000213763,
0.00476907, 0.000205017, 3.77e-05, 0.134661951, 0.005631489,
0.0971, 0.172154618, 5.91e-05, 0.000371101, 0.000145266,
0.013382779, 0.00025348, 0.11016712, 0.0616302, 0.018011524,
0.107619537, 0.189926726, 0.000857257, 0.041252452, 0, 0.00475341,
0.077329281, 0.633865958, 0.038182437, 0.015560589, 0.010375148,
1.515423445, 0.008559863, 0.003636564, 0.000424537, 0.002786085,
0.091458876, 0.014216177, 0.165042816, 0.009187705, 0.00115711,
0.000920496, 0.009072635, 0.001443384, 0.001595447, 0.023263507
)), .Names = c("plot.code", "species.code", "plant_growth_form",
"ord1", "lai"), class = "data.frame", row.names = c(NA, -91L))

Related

R loess regression

I think I missed something in the use of the loess function and I can't understand what i did wrong. I have a data frame in which I store the output (count) of 3 different softwares for 26 different genes on the genomes of different patients. The 3 softwares were each used on the same genome but with different rate of downsampling.
I pooled the results of all the patients by genes. At the end I have a data frame with 4 columns: samplexxx (downsampling rate), software (name of the software I used), gene (the name of the gene) and count (count results given by the software).
My goal is to estimate the downsampling effect (samplexxx) on the count given by the software, and I want to do some regression to be able to compare them with each other.
rate <- c(5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90,
95, 100)
my attempts:
datalist <- list()
for (i in 1:22) {
name <- genes[i]
print(name)
mod <- paste("mod_", name)
xfit <- paste("xfit_", name)
df <- paste("df_", name)
mod <- loess(data2[data2$gene == name,]$count ~
data2[data2$gene == name,]$samplexxx)
xfit <- predict(mod, newdata=data2[data2$gene == name,]$samplexxx)
df <- setNames(data.frame(matrix(ncol=4, nrow=60)),
c("down", "software", "gene", "loess"))
df$down <- data2[data2$gene == name,]$samplexxx
df$software <- data2[data2$gene == name,]$software
df$gene <- data2[data2$gene == name,]$gene
df$loess <- xfit
print(xfit)
datalist[[i]] <- df
}
data_loess <- do.call(rbind, datalist)
ggplot(data_loess, aes(x=gene, y=loess, fill=software)) +
geom_boxplot()
and:
mod <- loess(data2$count ~ data$samplexxx)
xfit <- predict(mod, newdata=data2$samplexxx)
for (i in 1:20) {
down <- rate[i]
print(name)
title <- paste("loess_downsampling", down)
out <- paste("loess_downsampling", down, ".pdf", sep="")
pdf(out, width=10)
print(ggplot(data2, aes(x=down, y=loess, fill=software))) +
geom_boxplot() + ggtitle(title))
dev.off()
}
Sample data:
> dput(data2)
structure(list(samplexxx = c(5L, 10L, 15L, 20L, 25L, 30L, 35L,
40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L), software = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("EH", "GangSTR", "Tred"), class = "factor"),
gene = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L), .Label = c("AFF2", "AR", "ATN1", "ATXN1",
"ATXN10", "ATXN2", "ATXN3", "ATXN7", "C9ORF72", "CACNA1A",
"CBL", "CNBP", "CSTB", "DIP2B", "DMPK", "FMR1", "FXN", "HTT",
"JPH3", "NOP56", "PPP2R2B", "TBP"), class = "factor"), count = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 17L, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15L, 15L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, NA, NA, NA, NA, 20L, 34L, 31L, 33L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, NA, NA, NA, NA, NA,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, NA, NA, NA, NA, NA, 22L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, NA, NA,
NA, NA, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, NA, NA, NA, NA, 6L, 8L, 8L,
8L, 8L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, NA, NA,
NA, NA, 11L, NA, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, NA, NA, NA, 12L, 5L, NA, 12L,
12L, 5L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, NA, NA, NA, NA, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 20L, 20L, 18L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, NA, NA, NA, NA, 27L, 24L,
21L, 14L, 27L, 14L, 21L, 27L, 27L, 14L, 27L, 27L, 27L, 27L,
27L, 27L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 68L, 73L,
78L, 54L, 79L, 76L, 87L, 72L, 62L, 63L, NA, NA, NA, NA, NA,
27L, 27L, 27L, 28L, 27L, 27L, 64L, 27L, 64L, 64L, 27L, 27L,
27L, 27L, 27L, NA, NA, NA, NA, NA, 18L, 20L, 18L, 20L, 20L,
18L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, NA, NA,
NA, NA, NA, 15L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 9L, 7L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, NA, NA, NA, NA, NA, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, NA, NA, NA, NA, NA, 35L, 29L, 35L, 35L, 30L, 35L,
32L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 11L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 20L, 11L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 33L, 33L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 33L,
33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, NA, 21L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 19L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 19L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 8L, 8L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 11L, NA, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 7L, 15L, 15L, 13L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 27L, 19L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, NA, 76L, 23L, 23L, 23L, 32L, 65L, 32L, 28L, 32L,
28L, 32L, 32L, 23L, 28L, 32L, 28L, 28L, 32L, 84L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 14L, 18L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 15L,
NA, NA, 15L, NA, 15L, NA, NA, 15L, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 9L, NA, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, NA, 28L, 36L, 36L, NA, 36L, 36L, 36L,
36L, NA, 36L, NA, 36L, 36L, 36L, 36L, 36L, NA, 36L, 36L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
1L, 8L, 18L, 16L, 15L, 14L, 15L, 16L, 15L, 16L, 14L, 15L,
14L, 14L, 14L, 14L, 16L, 16L, 16L, 16L, 31L, 28L, 31L, 31L,
32L, 32L, 32L, 33L, 31L, 33L, 32L, 31L, 32L, 32L, 32L, 32L,
32L, 32L, 32L, 32L, 7L, 18L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
19L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 5L, 6L, 6L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 12L, 11L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 5L, 7L, 7L, 7L, 7L, 11L, 11L, 7L,
11L, 15L, 15L, 11L, 7L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
1L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 20L, 17L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 1L, 2L, 1L, 1L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 15L, 6L, 22L, 13L, 14L, 13L, 14L, 13L, 14L, 14L,
27L, 27L, 14L, 14L, 27L, 14L, 27L, 14L, 27L, NA, 15L, 20L,
20L, 20L, 20L, 40L, 20L, 40L, 20L, 40L, 40L, 40L, 40L, 20L,
40L, 40L, 40L, 40L, 32L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15L, 14L,
17L, 17L, 17L, 19L, 17L, 13L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 5L, 3L, 1L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 5L, 3L,
1L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 12L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, NA,
2L, 3L, 2L, 29L, 33L, 33L, 35L, 33L, 35L, 35L, 33L, 35L,
35L, 33L, 35L, 35L, 35L, 35L, 35L)), class = "data.frame", row.names = c(NA,
-1320L))

I believe the loess should be done on a split on the "software".
software <- unique(data2$software)
data_loess <- do.call(rbind, lapply(software, \(x) {
X <- subset(data2, software == x)
lo <- loess(count ~ samplexxx, X)
count_pred <- predict(lo, newdata=X)
return(cbind(X, count_pred))
}))
Note: R version 4.1.2 (2021-11-01)
Gives:
head(data_loess[data_loess$samplexxx > 80, ], 10)
# samplexxx software gene count count_pred
# 17 85 EH AFF2 24 22.69004
# 18 90 EH AFF2 24 22.31879
# 19 95 EH AFF2 24 21.83428
# 20 100 EH AFF2 24 21.25618
# 37 85 EH AR 21 22.69004
# 38 90 EH AR 21 22.31879
# 39 95 EH AR 21 21.83428
# 40 100 EH AR 21 21.25618
# 57 85 EH ATN1 NA 22.69004
# 58 90 EH ATN1 NA 22.31879
And here a plot of "count" predictions on "samplexxx".
plot(count_pred ~ samplexxx, data_loess, col=as.numeric(software) + 1,
pch=20, xlab='Downsampling', ylab='Count (LOESS)')
legend('topleft', legend=software, pch=19, col=as.numeric(software) + 1,
horiz=TRUE, cex=.7, title='Software')
Looks interesting, but I'm not sure if it's absolutely right.
In my answer you see something different from for loops, which is probably new to you, however it's the r-ish way and its much shorter to code. The looping job here does lapply().
Anyway, hope this helps.

Undirected network graph calculated by tidygraph shows more degree centrality than should be possible

I have a cleaned data set with 26 nodes. I am placing these 26 nodes in an undirected network graph using tidygraph, where I use the centrality_degree() function to calculate the centrality degree. However, when I graph the resulting network, my highest possible centrality degree is 40, which should not be possible. When I change the graph to directed, this is corrected.
I somewhat confused, as other methods I have used in the past, where I manually calculated the centrality degree, I have never once come across this issue.
Is this regular behaviour, or am I doing something wrong?
Reproducible example:
library(tidygraph)
library(ggraph)
library(tidyverse)
nodes <- structure(list(id = 1:26, label = c("a", "b", "c", "d", "e",
"f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r",
"s", "t", "u", "v", "w", "x", "y", "z")), row.names = c(NA, -26L
), class = "data.frame")
edges <- structure(list(from = c(21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 17L, 17L, 17L, 17L, 17L),
to = c(1L, 12L, 3L, 16L, 24L, 4L, 10L, 6L, 22L, 2L, 8L, 1L,
12L, 13L, 3L, 18L, 16L, 24L, 5L, 7L, 14L, 4L, 10L, 6L, 9L,
22L, 15L, 2L, 20L, 8L, 21L, 12L, 13L, 3L, 16L, 24L, 5L, 7L,
14L, 4L, 10L, 6L, 22L, 15L, 2L, 8L, 17L, 21L, 1L, 13L, 3L,
16L, 5L, 7L, 14L, 10L, 6L, 9L, 22L, 15L, 2L, 20L, 8L, 17L,
21L, 1L, 3L, 18L, 16L, 5L, 7L, 14L, 4L, 10L, 6L, 25L, 9L,
22L, 15L, 20L, 8L, 17L, 21L, 11L, 1L, 12L, 13L, 18L, 16L,
24L, 5L, 7L, 14L, 4L, 10L, 6L, 25L, 9L, 22L, 15L, 20L, 8L,
17L, 1L, 3L, 10L, 6L, 22L, 20L, 8L, 21L, 11L, 1L, 13L, 3L,
18L, 24L, 7L, 4L, 10L, 6L, 25L, 9L, 22L, 15L, 2L, 20L, 8L,
17L, 21L, 11L, 1L, 12L, 13L, 18L, 16L, 5L, 7L, 14L, 10L,
6L, 25L, 9L, 22L, 15L, 20L, 8L, 17L, 1L, 3L, 18L, 16L, 7L,
14L, 4L, 10L, 6L, 9L, 22L, 15L, 2L, 20L, 8L, 17L, 21L, 11L,
1L, 12L, 13L, 3L, 18L, 16L, 24L, 14L, 4L, 10L, 6L, 25L, 9L,
22L, 15L, 2L, 20L, 8L, 11L, 1L, 3L, 18L, 16L, 7L, 10L, 6L,
9L, 22L, 15L, 2L, 20L, 8L, 17L, 21L, 11L, 1L, 12L, 13L, 3L,
18L, 16L, 24L, 5L, 7L, 14L, 10L, 6L, 25L, 9L, 22L, 15L, 2L,
20L, 8L, 17L, 21L, 11L, 1L, 12L, 13L, 3L, 18L, 16L, 24L,
5L, 7L, 14L, 4L, 6L, 25L, 9L, 22L, 15L, 2L, 20L, 8L, 17L,
21L, 11L, 1L, 12L, 13L, 3L, 18L, 24L, 5L, 7L, 14L, 4L, 10L,
25L, 9L, 22L, 15L, 2L, 20L, 8L, 21L, 1L, 13L, 3L, 18L, 5L,
10L, 6L, 22L, 2L, 20L, 8L, 21L, 1L, 13L, 3L, 18L, 16L, 24L,
4L, 10L, 6L, 22L, 15L, 2L, 20L, 8L, 11L, 1L, 12L, 13L, 3L,
16L, 24L, 5L, 7L, 14L, 4L, 10L, 6L, 25L, 9L, 15L, 2L, 20L,
8L, 17L, 21L, 1L, 12L, 3L, 18L, 16L, 24L, 7L, 10L, 6L, 25L,
9L, 22L, 2L, 20L, 8L, 17L, 21L, 11L, 1L, 12L, 13L, 3L, 18L,
16L, 24L, 5L, 7L, 14L, 4L, 6L, 25L, 9L, 22L, 15L, 20L, 8L,
17L, 21L, 11L, 1L, 3L, 16L, 24L, 7L, 10L, 6L, 22L, 2L, 8L,
21L, 11L, 1L, 12L, 13L, 3L, 18L, 16L, 24L, 14L, 4L, 10L,
6L, 25L, 9L, 22L, 2L, 20L, 7L, 6L, 25L, 22L, 8L), weight = c(3L,
1L, 3L, 2L, 1L, 1L, 5L, 1L, 8L, 2L, 1L, 2L, 3L, 2L, 5L, 1L,
4L, 1L, 4L, 4L, 4L, 1L, 5L, 13L, 3L, 7L, 3L, 2L, 3L, 8L,
1L, 1L, 1L, 15L, 10L, 7L, 2L, 4L, 2L, 5L, 19L, 23L, 6L, 2L,
11L, 7L, 1L, 1L, 2L, 3L, 3L, 5L, 4L, 5L, 4L, 4L, 21L, 2L,
9L, 8L, 1L, 1L, 12L, 1L, 2L, 1L, 3L, 1L, 6L, 6L, 5L, 6L,
1L, 6L, 22L, 2L, 2L, 9L, 8L, 3L, 13L, 1L, 5L, 6L, 4L, 10L,
13L, 3L, 41L, 46L, 11L, 39L, 9L, 55L, 2L, 108L, 2L, 8L, 31L,
30L, 13L, 39L, 2L, 2L, 1L, 3L, 4L, 8L, 5L, 1L, 8L, 1L, 6L,
1L, 8L, 2L, 3L, 23L, 2L, 12L, 96L, 1L, 3L, 21L, 1L, 6L, 12L,
38L, 4L, 5L, 4L, 4L, 8L, 8L, 3L, 29L, 3L, 11L, 3L, 3L, 63L,
2L, 5L, 18L, 19L, 4L, 25L, 1L, 2L, 3L, 1L, 7L, 6L, 7L, 1L,
3L, 17L, 1L, 3L, 6L, 1L, 4L, 11L, 1L, 5L, 1L, 5L, 1L, 1L,
15L, 4L, 7L, 3L, 1L, 4L, 12L, 8L, 1L, 9L, 32L, 3L, 7L, 5L,
35L, 1L, 1L, 3L, 1L, 6L, 4L, 4L, 12L, 2L, 5L, 4L, 2L, 2L,
9L, 1L, 2L, 3L, 4L, 9L, 13L, 2L, 1L, 25L, 25L, 10L, 14L,
10L, 4L, 59L, 4L, 5L, 21L, 19L, 1L, 8L, 27L, 3L, 5L, 8L,
8L, 11L, 12L, 111L, 5L, 50L, 45L, 15L, 32L, 10L, 49L, 109L,
1L, 8L, 28L, 39L, 53L, 13L, 48L, 5L, 13L, 2L, 20L, 3L, 3L,
27L, 10L, 8L, 1L, 58L, 1L, 7L, 32L, 13L, 21L, 110L, 1L, 17L,
27L, 124L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 7L, 1L, 1L, 1L,
2L, 2L, 1L, 5L, 2L, 2L, 2L, 1L, 3L, 3L, 14L, 2L, 2L, 4L,
1L, 3L, 14L, 5L, 8L, 44L, 16L, 14L, 4L, 12L, 4L, 19L, 41L,
47L, 2L, 1L, 11L, 24L, 2L, 18L, 1L, 7L, 5L, 1L, 7L, 3L, 27L,
3L, 15L, 7L, 54L, 1L, 4L, 17L, 5L, 6L, 27L, 1L, 1L, 2L, 3L,
4L, 10L, 56L, 3L, 25L, 25L, 7L, 16L, 5L, 29L, 59L, 3L, 3L,
20L, 17L, 5L, 31L, 3L, 6L, 1L, 4L, 7L, 1L, 3L, 1L, 6L, 5L,
13L, 1L, 2L, 9L, 1L, 15L, 2L, 1L, 16L, 4L, 4L, 3L, 1L, 6L,
17L, 10L, 1L, 13L, 63L, 11L, 12L, 1L, 5L, 1L, 2L, 3L)), row.names = c(NA,
-383L), class = c("tbl_df", "tbl", "data.frame"))
routes_tidy <- tbl_graph(nodes=nodes, edges=edges, directed=FALSE) %>% mutate(neighbors = centrality_degree())
# Filtering out 3 nodes out of the graph as they have no connections and zoom the figure way out
ggraph(routes_tidy, layout="graphopt") +
geom_node_point(aes(size=neighbors, filter=(label!="z" & label!="s" & label!="w"))) +
geom_edge_link(aes(width=weight, alpha=weight)) +
scale_edge_width(range=c(0.2, 2)) +
geom_node_text(aes(label=label, fontface="bold", size=neighbors, filter=(label!="z" & label!="s" & label!="w")), repel=TRUE) +
labs(edge_width="N") +
theme_graph()

I'm new to the whole tidygraph thing, stumbled over this question, got confused, and figured it'd be a nice way to get to know stuff. So, I don't know if it's a bug or a feature, but the behaviour is triggered because you have doubled edges:
# Given your edges
edges %>%
filter((from == 1 & to == 2) | from == 2 & to == 1)
# A tibble: 2 x 3
from to weight
<int> <int> <int>
1 1 2 11
2 2 1 3
And those count as 2 connections in the calculation of the degree centrality. One way to remove those double edges is to convert the network to a simple network:
routes_simple <-
routes_tidy %>%
morph(to_simple) %>%
crystallise() %>%
pull(graph) %>%
getElement(1) %>%
activate(nodes) %>%
mutate(neighbors = centrality_degree())
Now the maximum degree is 22 (and the heighest possible, presumably, 25).

Tidying up the text to remove unwanted characters, in output

Assuming that the dataframe is stored as fruit, and is in the following format:
State Fruit Category Fruit Type Gross Value
ACT CitrusFruit Mandarins $4,500,000
ACT CitrusFruit Oranges
NSW PomeFruit Apple $139,130,203.50
NSW Grapes Wine Production $50,000,000
NSW OrchardStoneFruit Avocados $10,031,123
QLD CitrusFruit Oranges
Output from dput(fruit)
structure(list(State = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L,
8L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 2L, 2L, 2L, 3L,
3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L,
8L), .Label = c("ACT", "NSW", "NT", "QLD", "SA", "TAS", "VIC",
"WA"), class = "factor"), Fruit.Category = structure(c(6L, 6L,
6L, 8L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L), .Label = c(" Grapes ", " OrchardStoneFruit ", " OtherFruit ",
" PomeFruit ", " CitrusFruit ", " CitrusFruit ", " Grapes ",
" Grapes ", " OrchardStoneFruit ", " OtherFruit ", " PomeFruit "
), class = "factor"), Fruit.Type = structure(c(5L, 8L, 13L, 18L,
31L, 2L, 4L, 6L, 7L, 9L, 14L, 17L, 3L, 11L, 12L, 15L, 1L, 10L,
16L, 13L, 23L, 26L, 13L, 23L, 26L, 13L, 23L, 26L, 13L, 23L, 26L,
13L, 23L, 26L, 13L, 23L, 26L, 13L, 23L, 26L, 18L, 31L, 18L, 31L,
18L, 31L, 18L, 31L, 18L, 31L, 18L, 31L, 18L, 31L, 14L, 17L, 20L,
22L, 24L, 25L, 27L, 14L, 17L, 20L, 22L, 24L, 25L, 27L, 14L, 17L,
20L, 22L, 24L, 25L, 27L, 14L, 17L, 20L, 22L, 24L, 25L, 27L, 14L,
17L, 20L, 22L, 24L, 25L, 27L, 14L, 17L, 20L, 22L, 24L, 25L, 27L,
14L, 17L, 20L, 22L, 24L, 25L, 27L, 15L, 21L, 29L, 30L, 15L, 21L,
29L, 30L, 15L, 21L, 29L, 30L, 15L, 21L, 29L, 30L, 15L, 21L, 29L,
30L, 15L, 21L, 29L, 30L, 15L, 21L, 29L, 30L, 16L, 19L, 28L, 16L,
19L, 28L, 16L, 19L, 28L, 16L, 19L, 28L, 16L, 19L, 28L, 16L, 19L,
28L, 16L, 19L, 28L), .Label = c(" Apples ", " Avocados ",
" Bananas ", " Cherries ", " Mandarins ", " Mangoes ",
" Nectarines ", " Oranges ", " Peaches ", " Pears ",
" Pineapples ", " Strawberries ", " AllOtherCitrusFruit ",
" AllOtherOrchardFruit ", " AllOtherOtherFruit ", " AllOtherPomeFruit ",
" AllOtherStoneFruit ", " AllOtherUses ", " Apples ", " Avocados ",
" Bananas ", " Cherries ", " Mandarins ", " Mangoes ", " Nectarines ",
" Oranges ", " Peaches ", " Pears ", " Pineapples ", " Strawberries ",
" WineProduction "), class = "factor"), Gross.Value = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 60L, 97L, 23L, 104L, 1L, 1L, 56L, 98L, 36L, 101L, 68L,
11L, 1L, 1L, 1L, 91L, 96L, 57L, 99L, 92L, 21L, 71L, 29L, 48L,
1L, 76L, 51L, 46L, 58L, 1L, 34L, 37L, 14L, 22L, 70L, 18L, 59L,
28L, 32L, 41L, 83L, 61L, 69L, 30L, 1L, 1L, 26L, 1L, 1L, 25L,
35L, 19L, 2L, 80L, 9L, 8L, 7L, 102L, 47L, 31L, 1L, 85L, 75L,
1L, 88L, 93L, 52L, 1L, 66L, 50L, 100L, 43L, 89L, 95L, 2L, 82L,
65L, 5L, 24L, 94L, 33L, 64L, 10L, 90L, 78L, 84L, 62L, 3L, 86L,
20L, 73L, 1L, 38L, 67L, 72L, 15L, 63L, 1L, 1L, 39L, 17L, 1L,
1L, 16L, 40L, 1L, 1L, 103L, 79L, 49L, 1L, 44L, 6L, 105L, 53L,
1L, 1L, 1L, 1L, 81L, 54L, 27L, 87L, 13L, 1L, 55L, 106L, 4L, 42L,
12L, 45L, 77L, 74L), .Label = c("", "$0.00", "$1,025,861.63",
"$1,107,476.82", "$1,135,055.74", "$1,148,385.97", "$1,514,089.93",
"$1,539,762.85", "$1,565,234.83", "$10,469,580.98", "$100,622,922.20",
"$106,039,956.40", "$11,648,561.35", "$113,930,475.80", "$114,195,162.80",
"$12,169,338.44", "$12,492,792.64", "$12,843,528.01", "$120,877,197.60",
"$13,245.08", "$13,331,668.11", "$13,981,075.51", "$130,258,416.50",
"$14,203,578.43", "$14,697,408.09", "$15,085,825.24", "$15,196.71",
"$15,246,349.76", "$154,858,589.30", "$168,325.78", "$17,661,100.37",
"$18,278,371.16", "$188,414.59", "$19,896,312.15", "$2,370,402.03",
"$2,557,589.86", "$209,648,663.50", "$21,426,350.11", "$22,482,034.46",
"$23,929,331.35", "$238,668.61", "$249,675,376.10", "$26,669,599.23",
"$27,540,236.71", "$270,903.84", "$3,485,520.14", "$3,520,605.89",
"$3,659,706.68", "$3,829,198.67", "$301,644.66", "$301,976.25",
"$31,133,715.88", "$313,144.86", "$334,363.30", "$35,212,772.81",
"$37,927,507.70", "$38,989,343.33", "$385,858,491.60", "$4,447,813.26",
"$4,549,208.46", "$4,569,373.00", "$4,702.20", "$4,712,329.56",
"$4,995,833.14", "$40,133,037.39", "$40,481.05", "$435,712,531.70",
"$44,434,103.55", "$443,017.10", "$45,665,029.35", "$45,888,545.67",
"$46,638,011.92", "$47,589.51", "$5,793,841.42", "$5,854,982.37",
"$51,534,636.09", "$53,367,548.56", "$53,377,925.45", "$555,799.71",
"$57,522,144.94", "$57,930,562.37", "$58,316,912.75", "$6,170,170.78",
"$6,791,088.95", "$6,824,520.08", "$623,030.52", "$63,493,163.21",
"$664,237.23", "$7,066,407.60", "$7,168,380.92", "$7,364,245.36",
"$7,426,224.28", "$7,894.54", "$70,218,810.35", "$76,591,000.57",
"$8,596,626.45", "$8,713,417.54", "$85,876,834.41", "$873,748.40",
"$9,262,889.69", "$9,731,658.36", "$9,991,440.81", "$91,781,453.44",
"$92,299.72", "$95,677,012.68", "$983,780.33"), class = "factor")), class = "data.frame", row.names = c(NA,
-152L))
I am trying to sum the Gross Value, based on the Fruit Category, and have used the following code for it:
fruit %>%
mutate(Gross.Value = as.numeric(gsub("[^0-9.]", "", as.character(Gross.Value)))) %>%
group_by(Fruit.Category) %>%
summarize(Gross.Value = sum(Gross.Value, na.rm=TRUE))
However, this is resulting in an output that looks a little like this:
A tibble: 11 x 2
Fruit.Category Gross.Value
<fct> <dbl>
1 " Grapes " 0
2 " OrchardStoneFruit " 0
3 " OtherFruit " 0
4 " PomeFruit " 0
5 " CitrusFruit " 501345814.
6 " CitrusFruit " 0
7 " Grapes " 1048709022.
8 " Grapes " 0
9 " OrchardStoneFruit " 679997807.
10 " OtherFruit " 879348015.
11 " PomeFruit " 683012047.
How would I alter the output so that I can remove the quotation marks and any trailing or leading spaces. Essentially, just tidy up the text.
Also, any suggestions on how I would go about to display the output in a descending order (based on total gross value) would be greatly appreciated. The only method I know is to add:
%>% arrange(desc(n))
at the end of the code. However, this does not seem to work for this.

A continuation of your last question :-)
fruit %>%
mutate_if(~is.factor(.) | is.character(.), ~trimws(as.character(.))) %>%
mutate(Gross.Value = as.numeric(gsub("[^0-9.]", "", Gross.Value))) %>%
group_by(Fruit.Category) %>%
summarize(Gross.Value = sum(Gross.Value, na.rm=TRUE)) %>%
arrange(desc(Gross.Value))
# # A tibble: 5 x 2
# Fruit.Category Gross.Value
# <chr> <dbl>
# 1 Grapes 1048709022.
# 2 OtherFruit 879348015.
# 3 PomeFruit 683012047.
# 4 OrchardStoneFruit 679997807.
# 5 CitrusFruit 501345814.
Because we trim the extra whitespace before summarization, we're able to reduce some of the incorrect uniqueness.
The meat of the answer is in that first line of the pipe:
mutate_if(~is.factor(.) | is.character(.), ~trimws(as.character(.))) %>%
The mutate_if says "mutate all columns that meet a specific condition". In this case, I limited it to those columns that are either character or quasi-char factors (since it would not do well to convert already-numeric columns to character).
From there, plan to arrange(desc(Gross.Value)). (Not sure where arrange(desc(n)) came in ...)

How do I sum a column based on another column?

Assuming that the dataframe is stored as fruit, and is in the following format:
State Fruit Category Fruit Type Gross Value
ACT CitrusFruit Mandarins $4,500,000
ACT CitrusFruit Oranges
NSW PomeFruit Apple $139,130,203.50
NSW Grapes Wine Production $50,000,000
NSW OrchardStoneFruit Avocados $10,031,123
QLD CitrusFruit Oranges
How would I sum the gross value, based on the State - while excluding blank values. But at the same time, the gross value of each state should be summed, rather than displayed separately for CitrusFruit, PomeFruit, etc.
I have tried to use the
library(plyr)
counts
method to no avail.
Any help would be greatly appreciated.
EDIT:
I have tried to use the following method:
library(dplyr)
fruit %>%
group_by(State) %>%
summarise(Gross = sum(Gross))
However, I am getting an error that says:
Evaluation Error: 'sum' not meaningful for factors.
EDIT:
Output from dput(fruit)
structure(list(State = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L,
8L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 2L, 2L, 2L, 3L,
3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L,
8L), .Label = c("ACT", "NSW", "NT", "QLD", "SA", "TAS", "VIC",
"WA"), class = "factor"), Fruit.Category = structure(c(6L, 6L,
6L, 8L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L), .Label = c(" Grapes ", " OrchardStoneFruit ", " OtherFruit ",
" PomeFruit ", " CitrusFruit ", " CitrusFruit ", " Grapes ",
" Grapes ", " OrchardStoneFruit ", " OtherFruit ", " PomeFruit "
), class = "factor"), Fruit.Type = structure(c(5L, 8L, 13L, 18L,
31L, 2L, 4L, 6L, 7L, 9L, 14L, 17L, 3L, 11L, 12L, 15L, 1L, 10L,
16L, 13L, 23L, 26L, 13L, 23L, 26L, 13L, 23L, 26L, 13L, 23L, 26L,
13L, 23L, 26L, 13L, 23L, 26L, 13L, 23L, 26L, 18L, 31L, 18L, 31L,
18L, 31L, 18L, 31L, 18L, 31L, 18L, 31L, 18L, 31L, 14L, 17L, 20L,
22L, 24L, 25L, 27L, 14L, 17L, 20L, 22L, 24L, 25L, 27L, 14L, 17L,
20L, 22L, 24L, 25L, 27L, 14L, 17L, 20L, 22L, 24L, 25L, 27L, 14L,
17L, 20L, 22L, 24L, 25L, 27L, 14L, 17L, 20L, 22L, 24L, 25L, 27L,
14L, 17L, 20L, 22L, 24L, 25L, 27L, 15L, 21L, 29L, 30L, 15L, 21L,
29L, 30L, 15L, 21L, 29L, 30L, 15L, 21L, 29L, 30L, 15L, 21L, 29L,
30L, 15L, 21L, 29L, 30L, 15L, 21L, 29L, 30L, 16L, 19L, 28L, 16L,
19L, 28L, 16L, 19L, 28L, 16L, 19L, 28L, 16L, 19L, 28L, 16L, 19L,
28L, 16L, 19L, 28L), .Label = c(" Apples ", " Avocados ",
" Bananas ", " Cherries ", " Mandarins ", " Mangoes ",
" Nectarines ", " Oranges ", " Peaches ", " Pears ",
" Pineapples ", " Strawberries ", " AllOtherCitrusFruit ",
" AllOtherOrchardFruit ", " AllOtherOtherFruit ", " AllOtherPomeFruit ",
" AllOtherStoneFruit ", " AllOtherUses ", " Apples ", " Avocados ",
" Bananas ", " Cherries ", " Mandarins ", " Mangoes ", " Nectarines ",
" Oranges ", " Peaches ", " Pears ", " Pineapples ", " Strawberries ",
" WineProduction "), class = "factor"), Gross.Value = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 60L, 97L, 23L, 104L, 1L, 1L, 56L, 98L, 36L, 101L, 68L,
11L, 1L, 1L, 1L, 91L, 96L, 57L, 99L, 92L, 21L, 71L, 29L, 48L,
1L, 76L, 51L, 46L, 58L, 1L, 34L, 37L, 14L, 22L, 70L, 18L, 59L,
28L, 32L, 41L, 83L, 61L, 69L, 30L, 1L, 1L, 26L, 1L, 1L, 25L,
35L, 19L, 2L, 80L, 9L, 8L, 7L, 102L, 47L, 31L, 1L, 85L, 75L,
1L, 88L, 93L, 52L, 1L, 66L, 50L, 100L, 43L, 89L, 95L, 2L, 82L,
65L, 5L, 24L, 94L, 33L, 64L, 10L, 90L, 78L, 84L, 62L, 3L, 86L,
20L, 73L, 1L, 38L, 67L, 72L, 15L, 63L, 1L, 1L, 39L, 17L, 1L,
1L, 16L, 40L, 1L, 1L, 103L, 79L, 49L, 1L, 44L, 6L, 105L, 53L,
1L, 1L, 1L, 1L, 81L, 54L, 27L, 87L, 13L, 1L, 55L, 106L, 4L, 42L,
12L, 45L, 77L, 74L), .Label = c("", "$0.00", "$1,025,861.63",
"$1,107,476.82", "$1,135,055.74", "$1,148,385.97", "$1,514,089.93",
"$1,539,762.85", "$1,565,234.83", "$10,469,580.98", "$100,622,922.20",
"$106,039,956.40", "$11,648,561.35", "$113,930,475.80", "$114,195,162.80",
"$12,169,338.44", "$12,492,792.64", "$12,843,528.01", "$120,877,197.60",
"$13,245.08", "$13,331,668.11", "$13,981,075.51", "$130,258,416.50",
"$14,203,578.43", "$14,697,408.09", "$15,085,825.24", "$15,196.71",
"$15,246,349.76", "$154,858,589.30", "$168,325.78", "$17,661,100.37",
"$18,278,371.16", "$188,414.59", "$19,896,312.15", "$2,370,402.03",
"$2,557,589.86", "$209,648,663.50", "$21,426,350.11", "$22,482,034.46",
"$23,929,331.35", "$238,668.61", "$249,675,376.10", "$26,669,599.23",
"$27,540,236.71", "$270,903.84", "$3,485,520.14", "$3,520,605.89",
"$3,659,706.68", "$3,829,198.67", "$301,644.66", "$301,976.25",
"$31,133,715.88", "$313,144.86", "$334,363.30", "$35,212,772.81",
"$37,927,507.70", "$38,989,343.33", "$385,858,491.60", "$4,447,813.26",
"$4,549,208.46", "$4,569,373.00", "$4,702.20", "$4,712,329.56",
"$4,995,833.14", "$40,133,037.39", "$40,481.05", "$435,712,531.70",
"$44,434,103.55", "$443,017.10", "$45,665,029.35", "$45,888,545.67",
"$46,638,011.92", "$47,589.51", "$5,793,841.42", "$5,854,982.37",
"$51,534,636.09", "$53,367,548.56", "$53,377,925.45", "$555,799.71",
"$57,522,144.94", "$57,930,562.37", "$58,316,912.75", "$6,170,170.78",
"$6,791,088.95", "$6,824,520.08", "$623,030.52", "$63,493,163.21",
"$664,237.23", "$7,066,407.60", "$7,168,380.92", "$7,364,245.36",
"$7,426,224.28", "$7,894.54", "$70,218,810.35", "$76,591,000.57",
"$8,596,626.45", "$8,713,417.54", "$85,876,834.41", "$873,748.40",
"$9,262,889.69", "$9,731,658.36", "$9,991,440.81", "$91,781,453.44",
"$92,299.72", "$95,677,012.68", "$983,780.33"), class = "factor")), class = "data.frame", row.names = c(NA,
-152L))

A couple of problems here:
You don't have Gross Value in your data, you have Gross.Value.
That column is factor, which is a more storage-efficient form of strings. Neither factor nor character can be summed. R knows nothing about accounting so the "$" means nothing to it in that context.
Try this:
library(dplyr)
someData %>%
mutate(Gross.Value = as.numeric(gsub("[^0-9.]", "", as.character(Gross.Value)))) %>%
group_by(State) %>%
summarize(Gross.Value = sum(Gross.Value, na.rm=TRUE))
# # A tibble: 8 x 2
# State Gross.Value
# <fct> <dbl>
# 1 ACT 0
# 2 NSW 564400574.
# 3 NT 20133040.
# 4 QLD 1053007677.
# 5 SA 691850721.
# 6 TAS 112902970.
# 7 VIC 1069102796.
# 8 WA 281014929.
The only changes from my comment were (1) using the correct column name, and (2) adding na.rm=TRUE, since you have many blanks. This means you need to be careful how you use this data, as you now have biases and inaccuracies in your summary.

You should convert the factor to numeric and then sum. Here is the solution I came up with:
library(tidyverse)
##This line converts the factor into a numeric variable, by making it a character and then removing the commas and the dollar sign. Finally it converts to number
fruit$`Gross Value` <- as.numeric(str_replace_all(as.character(fruit$`Gross Value`),"\\$|\\,",""))
##Then you can run your sum function
fruit %>%
group_by(State) %>%
summarise(Gross = sum(`Gross Value`, na.rm = TRUE))

R and NLS: singular gradient matrix at initial parameter

I'm trying to use nls to estimate the parameters of a non linear model.
I first use nls2 to find good initial parameters with Random Search and I then use nls to improve the estimation with a Gauss-Newton approach.
The problem is I always get an "singular gradient matrix at initial parameter estimates" error.
I'm not sure I understand, because the input matrix doesn't seem to be a singular gradient matrix.
Moreover even if the fits I'm looking for is not perfect for this data, nls should find a way to improve the
parameters estimations. Isn't it ?
Question: Is there a way to improve the parameters estimation?
I've tried NLS.lm but I had the same problem.
Here is a reproductible example:
Data:
structure(list(x1 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), x2 = c(1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L,
58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 0L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L,
43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L,
56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 0L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L,
55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 0L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L,
55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 0L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L,
43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L,
56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 0L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L,
58L, 59L, 60L, 61L, 62L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L,
35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L,
61L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L,
40L, 41L, 42L, 43L, 44L, 45L), y = c(0.0689464583349188, 0.0358227182166929,
0.0187034836294036, 0.0227081421239796, 0.0146603483536504, 0.00562771204350896,
0.00411351161052011, 0.00356917888321555, 0.0028017552960605,
0.0024750328652541, 0.00243175013170564, 0.00242654283706898,
0.00235224917236107, 0.00176144220485858, 0.00138071934398105,
0.000696375069179013, 0.00106282865382483, 0.00114735219137874,
0.00277256441625284, 0.00214359572321392, 0.00144935953386591,
0.00249732559162499, 0.00225859018399108, 0.00201642941663214,
0.00232438586834105, 0.0016083751355862, 0.00143118376291818,
0.00158323933266031, 0.00157585431454131, 0.00169206800399143,
0.00158514119474578, 0.00134506293557103, 0.00119442163345335,
0.00101284069499962, 0.0012621113004254, 0.00128964367655383,
0.00102819258807122, 0.00125345601171754, 0.00116155619985178,
0.00142466624262548, 0.00141075318725309, 0.00106556656123991,
0.0010976347045814, 0.0012442089226047, 0.0010627617251863, 0.00125322168410487,
0.00112108560656369, 0.0012459199320756, 0.00135773322693401,
0.0013997982284804, 0.00155012485145915, 0.00151108062240688,
0.00149570655260348, 0.00152598641103596, 0.00108261570337346,
0.000992225418429453, 0.000769588971038765, 0.000700496873143604,
0.000688378351958078, 0.000595007407260441, 0.000557615594951187,
0.00040476923690092, 0.000492276455560289, 0.000447248723966691,
0.000388694992851599, 0.000346087542525691, 0.000189803623801549,
0.0709302325562937, 0.0424623423412875, 0.019085896698975, 0.0190650552541205,
0.014276898897581, 0.00593407290200902, 0.00445528598343583,
0.00371231334350143, 0.00253909496678967, 0.00263487912423124,
0.00248012072619926, 0.00263786771266913, 0.00219351150766708,
0.00179271674850348, 0.00139646119589996, 0.000911560061336614,
0.000989537441246412, 0.001046390000492, 0.00223993432619926,
0.00164189356162362, 0.00106041866437064, 0.00194151698794588,
0.0014213192200082, 0.00165239495268553, 0.00196583929282493,
0.00120501090643706, 0.001141403899631, 0.00122398595424354,
0.00124538223829438, 0.00123370121853218, 0.00136883147552275,
0.00110907318146781, 0.000965843164247642, 0.000859986264862649,
0.00104695561918819, 0.00103985460139401, 0.000455832014104141,
0.000704296760639607, 0.000870145383845838, 0.000919870911357114,
0.00101396309667897, 0.000781894087412874, 0.000909712365723658,
0.000889897365477655, 0.000933063039278393, 0.000779395399425994,
0.000789546295038951, 0.000773432990897909, 0.00125614787798278,
0.00123172652693727, 0.00078936677195572, 0.000952107503075031,
0.00105449131480115, 0.00123128091742517, 0.000889501370397704,
0.00085648642099221, 0.000830097733497335, 0.000653482256334563,
0.000521696831160312, 0.000612702433456335, 0.000513576588109881,
0.000475289330709307, 0.00041141913800738, 0.000328157997211972,
0.00031336264403444, 0.000328784093808938, 0.000237448446412464,
0.0520691145678866, 0.0281929482152033, 0.0219024230330532, 0.0141074098760277,
0.00691341703402584, 0.00445785262213699, 0.0034569415664917,
0.00234406584844369, 0.00257369504707459, 0.00234047371531346,
0.00227286083862502, 0.00248544382019894, 0.00180810413760828,
0.00138986347039715, 0.000911936124008956, 0.000932783218782117,
0.00108887529088974, 0.0017855660833578, 0.00159768589505946,
0.00124091041330201, 0.00203036436876009, 0.00154489107876964,
0.00111687975012847, 0.00163256939968433, 0.00143626193198502,
0.000996683818914256, 0.0010781399542101, 0.00122575793431581,
0.00115671467616723, 0.001069532453476, 0.0010106869893371, 0.000978618104445015,
0.000894478048836441, 0.000842874700392747, 0.000819009288742475,
0.000843003919670386, 0.000964158733115548, 0.000877802228013507,
0.00087592051873807, 0.000935810596369843, 0.000879047729316546,
0.000829181439950081, 0.0010295792954412, 0.000765620227389517,
0.00102511256239906, 0.000823109180461753, 0.00111669534392894,
0.000802757620485245, 0.00103231207284173, 0.000884354083467919,
0.00109278942886507, 0.000969283099489796, 0.000827480664091176,
0.000798564447676552, 0.000909248326695786, 0.000682209033640434,
0.000780593294853913, 0.000485172195712818, 0.000467514093470122,
0.000295219649739392, 0.000460636351123183, 0.00045060371687344,
0.000492590160218764, 0.000402536549331963, 0.000271941766535751,
0.000171012123770371, 0.0267385565244063, 0.0275426278720772,
0.0154589149018475, 0.00729065000152096, 0.00513675524527996,
0.00378848397112206, 0.00305965140790087, 0.00240428827949139,
0.00233604733730811, 0.00199601458903693, 0.00198302547453915,
0.00137121122011316, 0.00126241982975401, 0.0012413298189045,
0.00103044327584109, 0.00106759120581615, 0.00190957422380402,
0.00124400301656831, 0.000989035353673623, 0.00160702520431547,
0.0011515826661394, 0.00153203681379408, 0.00134897491229138,
0.000916492937174261, 0.00072393419977287, 0.00115124473393361,
0.00104241370079698, 0.000953324905193568, 0.00121656899373365,
0.000891420608484922, 0.000671666092758208, 0.000659860761797571,
0.000586145968952161, 0.00072735268499929, 0.000658407622538582,
0.000498831767252743, 0.000658345030520574, 0.000542106922897528,
0.000874560054044737, 0.000543320226217274, 0.000751139509440084,
0.000668632963233356, 0.000656903021131188, 0.000574965903652329,
0.0006661524076778, 0.000605171890653201, 0.000527045917239561,
0.000985791370586684, 0.000899420142057553, 0.000933015548254953,
0.00082137283567561, 0.000870124781995904, 0.000498046123582973,
0.000540181050881142, 0.000596948101336416, 0.000405622486362069,
0.000631594016548032, 0.000468749313033603, 0.000389576698910993,
0.000335624642574679, 0.000286763668856847, 0.000439039581432135,
0.000244767908276044, 0.000303911794528604, 0.000160988671898765,
0.0365772382134747, 0.0255898183301035, 0.010327803963121, 0.00714710822108354,
0.00506253612461807, 0.00447056668291465, 0.00322822676102386,
0.00328154620569948, 0.0028470908747756, 0.00253477302081723,
0.00187837758253778, 0.00116416512964702, 0.00119557763663167,
0.000993575112051645, 0.00136274483135782, 0.00204131052512691,
0.00157953945941769, 0.00116523253183218, 0.00190793844827791,
0.00144595416523011, 0.00157423646879793, 0.00126996001866537,
0.00115283860342634, 0.00116894693507543, 0.000930041619012519,
0.00106545753272384, 0.00123507493015348, 0.00130865599847824,
0.000940647984853709, 0.000836521897923032, 0.000778436697656724,
0.00100773629284415, 0.000956581999215341, 0.000808036977042788,
0.000597930101173421, 0.000776453419209873, 0.000630241947142534,
0.000649832426616575, 0.000782188275296327, 0.00102823806308181,
0.000830656989407107, 0.00051915559901561, 0.000537114715917872,
0.000872430107712244, 0.000549284113632851, 0.000738257038745497,
0.00097442578198376, 0.000879724260815807, 0.000884543540237537,
0.00100038027474944, 0.00103543285342337, 0.000875585441608313,
0.000829083410412184, 0.000760316116414823, 0.000712211369823927,
0.000386744815307978, 0.000428331410721292, 0.000397681982571065,
0.000213938551710199, 0.000370800615243779, 0.000281234314553042,
0.000267359921177464, 0.000358376119030352, 0.000337361541022196,
0.0310029062887812, 0.0154963087949333, 0.00959302943445506,
0.00645674376405936, 0.00525321947702945, 0.00386084394749159,
0.00374364242039947, 0.00351047952579374, 0.00298556939927835,
0.00199158625919048, 0.00206559575086432, 0.00169077836254661,
0.00139156751815451, 0.00170363478493893, 0.00250481301085496,
0.00182474837251083, 0.00116804333227652, 0.00155778636185214,
0.00183778204100427, 0.00135012918459471, 0.00166904872503284,
0.00120137403943415, 0.00108307957787943, 0.00146041465872549,
0.0014437889563235, 0.000975926161359965, 0.00102580511345623,
0.00112145083941, 0.000921884915530595, 0.00082253191796126,
0.000634876416504371, 0.00108601324863747, 0.000830573067167897,
0.000965052460105379, 0.000922667052402736, 0.000863193817654785,
0.000982111173513293, 0.000763009170856168, 0.000921755812461313,
0.000771609983091022, 0.000669047474976222, 0.000773869648383834,
0.00072022523061129, 0.000742426347056781, 0.000718728249316847,
0.000761437280522971, 0.000833112611531319, 0.000794451658438637,
0.000907360341651947, 0.00112083735676435, 0.00102996529205731,
0.000651843453054939, 0.000640968179416338, 0.000549646466476441,
0.000778958256714525, 0.000627413038784969, 0.000523658918731223,
0.000418571973368359, 0.000643352520494588, 0.000351378727146459,
0.000504093577607682, 0.000333827596358531, 0.000339505558071773,
0.0181836504450303, 0.0135527124187004, 0.00780738765319868,
0.00643260738080874, 0.00476881905655232, 0.00406986745617877,
0.00400325917456592, 0.00277499160186111, 0.00198311377238581,
0.00241837807740304, 0.00141018451525995, 0.00166798657140732,
0.0013970042073337, 0.00237332662413329, 0.00146721126831566,
0.000990562316636778, 0.00186106889002752, 0.00186322276224556,
0.00140391140302307, 0.00139027556176293, 0.00125730361478641,
0.00127044200804939, 0.00126655503830484, 0.00133956330669488,
0.00128219844136096, 0.00109531452608613, 0.00112195611926977,
0.00101411381866565, 0.00104786051750783, 0.000798711632769435,
0.000852432172756047, 0.000852720107765923, 0.00110385307389073,
0.00081385514739304, 0.00102898862672826, 0.000710330768658628,
0.000803425598538879, 0.000723455383750816, 0.00075034248654992,
0.000864917906994041, 0.000799733114881449, 0.000608518601191706,
0.000855476747683942, 0.000988548021123443, 0.00104800683206201,
0.000997051779707941, 0.000796235203259423, 0.000910577791459715,
0.000869997383535945, 0.000557402535474327, 0.000757813148434336,
0.000480807445269952, 0.000553425518375578, 0.000633029237291637,
0.00050222863978579, 0.000390945889771328, 0.000430333228928208,
0.000425167676834459, 0.000239604519722651, 0.000357021364759551,
0.000292330910803864, 0.000288851701197491, 0.0198837196044917,
0.0142208140311702, 0.00733039271103269, 0.00609158853724431,
0.00487605866828399, 0.00382636157210858, 0.00411545257392807,
0.00235906433257981, 0.00228491326937568, 0.00109255715480326,
0.00158036861847788, 0.00122011020381908, 0.00223761733564904,
0.00173284341769128, 0.00117538923471357, 0.00219622963095698,
0.00214263916211795, 0.0013198229549172, 0.00172951959530242,
0.00128074705482347, 0.00124062569884766, 0.00144218669111025,
0.00148407512819099, 0.00100716026446858, 0.0010842890711437,
0.000800686408079248, 0.000890454658065465, 0.000887152794471706,
0.00105780722647994, 0.000874948318354744, 0.000569126715186268,
0.000924642167943982, 0.000857013884141074, 0.000823122890591976,
0.00073038777177409, 0.000522615873628494, 0.00070936497950782,
0.000823074755104667, 0.000720588701733105, 0.000722724038337836,
0.00063458965098969, 0.000620049346639466, 0.000842327487089008,
0.000617708212493797, 0.000783953750160813, 0.00112567150392384
)), .Names = c("x1", "x2", "y"), class = c("tbl_df", "data.frame"
), row.names = c(NA, -500L))
Initial parameters: initial_par
structure(list(A1 = 0.0529486559121727, alpha1 = 0.00888818269595504,
B1 = 0.250994319084551, beta1 = 0.471984946168959, A2 = 0.281956987357551,
alpha2 = 0.325086771510541, B2 = 0.0562204262765557, beta2 = 0.725645614322275), class = "data.frame", row.names = c(NA,
-1L), .Names = c("A1", "alpha1", "B1", "beta1", "A2", "alpha2",
"B2", "beta2"))
Formula:
formula = y ~
(A1*exp(-alpha1*x1) + B1*exp(-beta1*x1)) *
(A2*exp(-alpha2*x2) + B2*exp(-beta2*x2))
Nls and the error message
final = nls(formula,
data=df,
start = as.list(as.vector(initial_par)))
Error in nlsModel(formula, mf, start, wts) :
singular gradient matrix at initial parameter estimates

The problem is that there is not a one to one relationship between your model and parameters. To see this write A1 = exp(a1+d), A2 = exp(a2-d), B1 = exp(b1+d), B2 = exp(b2-d) in which case we have:
y ~ exp(-alpha1 * x1 + a1 + d) * exp(-alpha2 * x2 + a2 - d) +
exp(-alpha1 * x1 + a1 + d) * exp(-beta2 * x2 + b2 - d) +
exp(-beta1 * x1 + b1 + d) * exp(-alpha2 * x2 + a2 - d) +
exp(-beta1 * x1 + b1 + d) * exp(-beta2 * x2 + b2 - d)
But d cancels in each of the 4 terms and so cancels entirely from the RHS. That is, the RHS is the same for any value of d thus the model is overparameterized and so will give a singular gradient.
Fix one of A1, A2, B1, B2 and then you should be able to get a solution:
A1 <- 1
nls(formula, df, start = initial_par[-1])
giving:
Nonlinear regression model
model: y ~ (A1 * exp(-alpha1 * x1) + B1 * exp(-beta1 * x1)) * (A2 * exp(-alpha2 * x2) + B2 * exp(-beta2 * x2))
data: df
alpha1 B1 beta1 A2 alpha2 B2 beta2
0.11902 1.21030 0.79076 0.04604 0.51697 0.00183 0.02317
residual sum-of-squares: 0.000685
Number of iterations to convergence: 11
Achieved convergence tolerance: 6.686e-06

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