Apply pmax to dataframe with different args based on dataframe factor

# I have a dataframe in the following form:

track <- c(rep('A', 3), rep('B', 4), rep('C', 4))
value <- c(0.15, 0.25, 0.35, 0.05, 0.99, 0.32, 0.13, 0.80, 0.75, 0.60, 0.44)
df <- data.frame(track=factor(track), value=value)

#> print(df)
? #track value
#1 ? ? ?A ?0.15
#2 ? ? ?A ?0.25
#3 ? ? ?A ?0.35
#4 ? ? ?B ?0.05
#5 ? ? ?B ?0.99
#6 ? ? ?B ?0.32
#7 ? ? ?B ?0.13
#8 ? ? ?C ?0.80
#9 ? ? ?C ?0.75
#10 ? ? C ?0.60
#11 ? ? C ?0.44


# If any of the values are below a threshold value, I want to replace it
with the
# threshold value. The twist is that there is a different threshold value
for
# every track.

# I tried something like this, but it's not working

threshold <- list()
threshold['A'] <- 0.2
threshold['B'] <- 0.4
threshold['C'] <- 0.5


for (track in levels(df$track)){
? ?df[df$track==track,]$outcome <- pmax(df[df$track==track,]$outcome,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? threshold[track])
}
# Warning messages:
# 1: In is.na(mmm) : is.na() applied to non-(list or vector) of type 'NULL'
# 2: In is.na(mmm) : is.na() applied to non-(list or vector) of type 'NULL'
# 3: In is.na(mmm) : is.na() applied to non-(list or vector) of type 'NULL'


#******************
# Desired Results:

#> print(df)
? #track value
#1 ? ? ?A ?0.20 # value changed
#2 ? ? ?A ?0.25
#3 ? ? ?A ?0.35
#4 ? ? ?B ?0.40 # value changed
#5 ? ? ?B ?0.99
#6 ? ? ?B ?0.40 # value changed
#7 ? ? ?B ?0.40 # value changed
#8 ? ? ?C ?0.80
#9 ? ? ?C ?0.75
#10 ? ? C ?0.60
#11 ? ? C ?0.50 # value changed


# Any ideas? Thanks for reading.

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