chemosensors 0.7.7

Dataset UNIMANsorption.

Description

The dataset contains the statistics on modeling the Langmuir isotherm on 17 UNIMAN sensors and 3 pure analytes at different concentration levels.

Details

Indeed, the isotherm extends the Langmuir isotherm for a single gas under a simplified assumption that molecules of the analytes in mixture do not interact with each other. Such property allows us to describe the adsorption process in the gas mixture explicitly by computing a single-adsorption Langmuir isotherm per analyte.

We estimate the parameters of the Langmuir isotherm by fitting a linear model based on the short-term UNIMAN dataset UNIMANshort. The resulted coefficients of determination R2 of the models are not below than 0.973 for analyte C, and slightly worse for analytes A and B giving the minimum value 0.779.

The datasets has the only variable UNIMANsorption of class list, that in turn stores the variable qkc of the class array of three dimensions. The first dimension encodes a sensor, and the second encodes a gas. The third dimension represent four parameters extracted from the Langmuir model:

K Sorption affinity in terms of the Langmuir isotherm.
Q Sorption capacity in terms of the Langmuir isotherm (not used in SorptionModel).
KCmin The term KC in the dominator of the isotherm at minimal concentration level (analyte contribution in a mixture).
KCmax The term KCmax in the dominator of the isotherm at maximum concentration level (analyte contribution in a mixture).

Examples


data(UNIMANsorption, package="chemosensors")

# print the list of loaded data variables
str(UNIMANsorption)



List of 1
 $ qkc: num [1:17, 1:3, 1:4] 10.02 9.51 9.52 6.57 9.19 ...
  ..- attr(*, "dimnames")=List of 3
  .. ..$ : chr [1:17] "1" "2" "3" "4" ...
  .. ..$ : chr [1:3] "A" "B" "C"
  .. ..$ : chr [1:4] "Q" "K" "KCmin" "KCmax"




dim(UNIMANsorption$qkc)



[1] 17  3  4




str(UNIMANsorption$qkc)



 num [1:17, 1:3, 1:4] 10.02 9.51 9.52 6.57 9.19 ...
 - attr(*, "dimnames")=List of 3
  ..$ : chr [1:17] "1" "2" "3" "4" ...
  ..$ : chr [1:3] "A" "B" "C"
  ..$ : chr [1:4] "Q" "K" "KCmin" "KCmax"




### Langmuir parameter K
K <- UNIMANsorption$qkc[, , "K"]

mf <- melt(K, varnames = c("sensor", "gas"))

p1 <- qplot(sensor, value, data = mf, geom = "line", color = gas) +
  ylab("Langmuir parameter K")
p1






p2 <- qplot(sensor, value, data = mf, geom = "bar", stat = "identity") + 
  facet_grid(gas ~ ., scale = "free_y") + 
  ylab("Langmuir parameter K")
p2






### Langmuir parameter KCmin
KCmin <- UNIMANsorption$qkc[, , "KCmin"]

mf <- melt(KCmin, varnames = c("sensor", "gas"))

p3 <- qplot(sensor, value, data = mf, geom = "line", color = gas) +
  ylab("Langmuir parameter KCmin")
p3






p4 <- qplot(sensor, value, data = mf, geom = "bar", stat = "identity") + 
  facet_grid(gas ~ .) + 
  ylab("Langmuir parameter KCmin")
p4






### Langmuir parameter KCmax
KCmax <- UNIMANsorption$qkc[, , "KCmax"]

mf <- melt(KCmax, varnames = c("sensor", "gas"))

p5 <- qplot(sensor, value, data = mf, geom = "line", color = gas) +
  ylab("Langmuir parameter KCmax")
p5






p6 <- qplot(sensor, value, data = mf, geom = "bar", stat = "identity") + 
  facet_grid(gas ~ .) + 
  ylab("Langmuir parameter KCmax")
p6






### summary plot for K*
require(gridExtra)
grid.arrange(p1, p3, p5, ncol = 1)






### plot to group sensors based on affinities A vs. C
df <- as.data.frame(K)
df <- mutate(df,
  sensor = 1:nrow(df),
  sensor.group = ifelse(A > C, "More affinity to A", "More affinity to C"))

mf <- melt(K, varnames = c("sensor", "gas"))

p7 <- ggplot(mf, aes(x = factor(sensor), y = value, fill = gas)) + 
  geom_bar(position = "dodge") +
  xlab("sensor") + ylab("Langmuir parameter K")
p7


Mapping a variable to y and also using stat="bin".
  With stat="bin", it will attempt to set the y value to the count of cases in each group.
  This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
  If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
  If you want y to represent values in the data, use stat="identity".
  See ?geom_bar for examples. (Deprecated; last used in version 0.9.2)




p8 <- ggplot(df, aes(reorder(x = factor(sensor), A - C), y = A - C, fill = sensor.group)) + 
  geom_bar(position = "identity") + coord_flip() +
  xlab("sensor") + ylab("Difference in K between A and C")
p8


Mapping a variable to y and also using stat="bin".
  With stat="bin", it will attempt to set the y value to the count of cases in each group.
  This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
  If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
  If you want y to represent values in the data, use stat="identity".
  See ?geom_bar for examples. (Deprecated; last used in version 0.9.2)




### UNIMAN affinities K in polar plot
mf <- melt(UNIMANsorption$qkc[, , "K"], varnames = c("sensor", "gas"))

p9 <- qplot(sensor, value, color = gas, data = mf, geom = "line") + coord_polar()
p9

See also

SorptionModel