R/runcurtis_f2.r
In AndySouth/resistance: an insecticide resistance population genetics model with 2 loci and 2 insecticides
Defines functions
runcurtis_f2
#' run analyses to create fig2 of Curtis(1985)
#'
#' Recreates figure 2 of Curtis(1985), allows inputs to be tweaked
#'
#' @param max_gen number of generations
#' @param P_1 locus 1 frequency of resistance allele
#' @param P_2 locus 2 frequency of resistance allele
#' @param recomb_rate recombination rate
#' @param exposure exposure to the insecticide(s) if set same for both
#' @param exp1 option to set exposure differently for the diff insecticides
#' @param exp2 option to set exposure differently for the diff insecticides
#' @param phi.SS1_a0 Baseline fitness of SS1 in a0
#' @param phi.SS1_A0 Baseline fitness of SS1 in A0
#' @param phi.SS2_0b Baseline fitness of SS2 in 0b
#' @param phi.SS2_0B Baseline fitness of SS2 in 0B
#' @param W.SS1_00 Fitness of SS1 in 00 (no insecticide)
#' @param W.SS2_00 Fitness of SS2 in 00 (no insecticide)
#' @param h.RS1_00 Dominance coefficient locus1 in 00
#' @param h.RS1_a0 Dominance coefficient locus1 in a0
#' @param h.RS1_A0 Dominance coefficient locus1 in A0
#' @param h.RS2_00 Dominance coefficient locus2 in 00
#' @param h.RS2_0b Dominance coefficient locus2 in 0b
#' @param h.RS2_0B Dominance coefficient locus2 in 0B
#' @param s.RR1_a0 Selection coefficient locus1 in a
#' @param s.RR1_A0 Selection coefficient locus1 in A
#' @param s.RR2_0b Selection coefficient locus2 in b
#' @param s.RR2_0B Selection coefficient locus2 in B
#' @param rr_restoration_ins1 effect of resistance in overcoming insecticide 1 effectiveness in RR
#' @param rr_restoration_ins2 effect of resistance in overcoming insecticide 2 effectiveness in RR
#' @param z.RR1_00 fitness cost of resistance allele 1 in insecticide free environment
#' @param z.RR2_00 fitness cost of resistance allele 2 in insecticide free environment
#' @param sexLinked whether resistance is sex linked, default=0(FALSE)
#' @param male_exposure_prop male exposure as a propoertion of female, default 1 for same, likely <1
#' @param correct_mix_deploy proportion of times that mixture is deployed correctly,
#' assumes that when not deployed correctly the single insecticides are used instead
#' @param addCombinedStrategy whether to add a line for the combined mixture/individual strategy, makes plot confusing so set to FALSE by default
#' @param addStrategyLabels add labels to plot for where strategies cross 0.5 line TRUE gives seq,mix1,mix2,mix3
#' @param strategyLabels = c("seq","mix1","adapt","mix2")
#' @param xlab xlab
#' @param ylab ylab
#' @param ylabs whether to add tick labels
#' @param xlabs whether to add tick labels
#' @param yticks tick positions
#' @param cex.axis scaling for x&y tick labs
#' @param addLegend whether to add a legend inside plot
#' @param main title for the plot
#' @param vlines colour of vertical lines to add to plot, NULL for none
#' @param maxX optional max x value for the plot to facilitate comparison with other plots
#' @param labelMixSeqRatio how many dp in label of the ratio of mix/seq, NULL for no label, only option compatible with addLegend
#'
#' @return list of plot components
#' @export
runcurtis_f2 <- function( max_gen = 500,
P_1 = 0.01,
P_2 = 0.01,
recomb_rate = 0.5,
exposure = 0.9,
exp1 = NULL,
exp2 = NULL,
phi.SS1_a0 = 0 ,
phi.SS1_A0 = 0.73 ,
phi.SS2_0b = 0 ,
phi.SS2_0B = 1 ,
W.SS1_00 = 1 ,
W.SS2_00 = 1 ,
h.RS1_00 = 0 ,
h.RS1_a0 = 0 ,
h.RS1_A0 = 0.17 ,
h.RS2_00 = 0 ,
h.RS2_0b = 0 ,
h.RS2_0B = 0.0016 ,
s.RR1_a0 = 0 ,
s.RR1_A0 = NULL ,
s.RR2_0b = 0 ,
s.RR2_0B = NULL ,
# 14/6/16 rr_restoration = s / effectiveness
rr_restoration_ins1 = 0.23/0.73,
rr_restoration_ins2 = 0.43/1,
z.RR1_00 = 0 ,
z.RR2_00 = 0 ,
sexLinked = 0 ,
male_exposure_prop = 1,
correct_mix_deploy = 1,
addCombinedStrategy = FALSE,
addStrategyLabels = TRUE,
strategyLabels = c("seq","mix1","adapt","mix2"),
ylab = "Resistance frequency %",
ylabs = TRUE,
yticks = c(0.1,1,5,10,50,100),
xlab = "Generation",
xlabs = TRUE,
cex.axis = 0.8,
addLegend = TRUE,
main = "",
vlines = 'grey95',
maxX = NULL,
labelMixSeqRatio = NULL
)
{
#fill exposure array I1
a <- setExposure(exposure=exposure, exp1=exp1, exp2=exp2,
insecticideUsed = "insecticide1",
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
inputI1 <- setInputOneScenario(a=a,
max_gen = max_gen,
P_1 = P_1,
P_2 = P_2,
recomb_rate = recomb_rate,
phi.SS1_a0 = phi.SS1_a0 ,
phi.SS1_A0 = phi.SS1_A0 ,
phi.SS2_0b = phi.SS2_0b ,
phi.SS2_0B = phi.SS2_0B ,
W.SS1_00 = W.SS1_00 ,
W.SS2_00 = W.SS2_00 ,
h.RS1_00 = h.RS1_00 ,
h.RS1_a0 = h.RS1_a0 ,
h.RS1_A0 = h.RS1_A0 ,
h.RS2_00 = h.RS2_00 ,
h.RS2_0b = h.RS2_0b ,
h.RS2_0B = h.RS2_0B ,
s.RR1_a0 = s.RR1_a0 ,
s.RR1_A0 = s.RR1_A0 ,
s.RR2_0b = s.RR2_0b ,
s.RR2_0B = s.RR2_0B ,
# 14/6/16
rr_restoration_ins1 = rr_restoration_ins1,
rr_restoration_ins2 = rr_restoration_ins2,
z.RR1_00 = z.RR1_00 ,
z.RR2_00 = z.RR2_00 ,
sexLinked = sexLinked ,
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
#fill exposure array I2
a <- setExposure(exposure=exposure, exp1=exp1, exp2=exp2,
insecticideUsed = "insecticide2",
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
inputI2 <- setInputOneScenario(a=a,
max_gen = max_gen,
P_1 = P_1,
P_2 = P_2,
recomb_rate = recomb_rate,
phi.SS1_a0 = phi.SS1_a0 ,
phi.SS1_A0 = phi.SS1_A0 ,
phi.SS2_0b = phi.SS2_0b ,
phi.SS2_0B = phi.SS2_0B ,
W.SS1_00 = W.SS1_00 ,
W.SS2_00 = W.SS2_00 ,
h.RS1_00 = h.RS1_00 ,
h.RS1_a0 = h.RS1_a0 ,
h.RS1_A0 = h.RS1_A0 ,
h.RS2_00 = h.RS2_00 ,
h.RS2_0b = h.RS2_0b ,
h.RS2_0B = h.RS2_0B ,
s.RR1_a0 = s.RR1_a0 ,
s.RR1_A0 = s.RR1_A0 ,
s.RR2_0b = s.RR2_0b ,
s.RR2_0B = s.RR2_0B ,
# 14/6/16
rr_restoration_ins1 = rr_restoration_ins1,
rr_restoration_ins2 = rr_restoration_ins2,
z.RR1_00 = z.RR1_00 ,
z.RR2_00 = z.RR2_00 ,
sexLinked = sexLinked ,
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
#fill exposure array mix
a <- setExposure(exposure=exposure, exp1=exp1, exp2=exp2,
insecticideUsed = "mixture",
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
inputmix <- setInputOneScenario(a=a,
max_gen = max_gen,
P_1 = P_1,
P_2 = P_2,
recomb_rate = recomb_rate,
phi.SS1_a0 = phi.SS1_a0 ,
phi.SS1_A0 = phi.SS1_A0 ,
phi.SS2_0b = phi.SS2_0b ,
phi.SS2_0B = phi.SS2_0B ,
W.SS1_00 = W.SS1_00 ,
W.SS2_00 = W.SS2_00 ,
h.RS1_00 = h.RS1_00 ,
h.RS1_a0 = h.RS1_a0 ,
h.RS1_A0 = h.RS1_A0 ,
h.RS2_00 = h.RS2_00 ,
h.RS2_0b = h.RS2_0b ,
h.RS2_0B = h.RS2_0B ,
s.RR1_a0 = s.RR1_a0 ,
s.RR1_A0 = s.RR1_A0 ,
s.RR2_0b = s.RR2_0b ,
s.RR2_0B = s.RR2_0B ,
# 14/6/16
rr_restoration_ins1 = rr_restoration_ins1,
rr_restoration_ins2 = rr_restoration_ins2,
z.RR1_00 = z.RR1_00 ,
z.RR2_00 = z.RR2_00 ,
sexLinked = sexLinked ,
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
#bind inputs together
input <- cbind(inputI1, inputI2, inputmix)
#run model
listOut <- runModel2(input=input, produce.plots = FALSE)
#this is a modified version of Beths function
#allowing curtis fig2 to be applied to any scenario
#plotcurtis_f2_generic( listOutMix$results[[1]], listOutI2$results[[1]], listOutI1$results[[1]] )
#be careful of order
#mix,2,1
plotcurtis_f2_generic( listOut$results[[3]], listOut$results[[2]], listOut$results[[1]],
addCombinedStrategy = addCombinedStrategy,
addStrategyLabels = addStrategyLabels,
strategyLabels = strategyLabels,
xlab = xlab,
xlabs = xlabs,
ylab = ylab,
ylabs = ylabs,
yticks = yticks,
cex.axis = cex.axis,
addLegend = addLegend,
main = main,
vlines = vlines,
maxX = maxX,
labelMixSeqRatio = labelMixSeqRatio
)
#add returning the results
invisible(listOut)
}
AndySouth/resistance documentation built on Nov. 12, 2020, 3:39 a.m.
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Defines functions runcurtis_f2
#' run analyses to create fig2 of Curtis(1985)
#'
#' Recreates figure 2 of Curtis(1985), allows inputs to be tweaked
#'
#' @param max_gen number of generations
#' @param P_1 locus 1 frequency of resistance allele
#' @param P_2 locus 2 frequency of resistance allele
#' @param recomb_rate recombination rate
#' @param exposure exposure to the insecticide(s) if set same for both
#' @param exp1 option to set exposure differently for the diff insecticides
#' @param exp2 option to set exposure differently for the diff insecticides
#' @param phi.SS1_a0 Baseline fitness of SS1 in a0
#' @param phi.SS1_A0 Baseline fitness of SS1 in A0
#' @param phi.SS2_0b Baseline fitness of SS2 in 0b
#' @param phi.SS2_0B Baseline fitness of SS2 in 0B
#' @param W.SS1_00 Fitness of SS1 in 00 (no insecticide)
#' @param W.SS2_00 Fitness of SS2 in 00 (no insecticide)
#' @param h.RS1_00 Dominance coefficient locus1 in 00
#' @param h.RS1_a0 Dominance coefficient locus1 in a0
#' @param h.RS1_A0 Dominance coefficient locus1 in A0
#' @param h.RS2_00 Dominance coefficient locus2 in 00
#' @param h.RS2_0b Dominance coefficient locus2 in 0b
#' @param h.RS2_0B Dominance coefficient locus2 in 0B
#' @param s.RR1_a0 Selection coefficient locus1 in a
#' @param s.RR1_A0 Selection coefficient locus1 in A
#' @param s.RR2_0b Selection coefficient locus2 in b
#' @param s.RR2_0B Selection coefficient locus2 in B
#' @param rr_restoration_ins1 effect of resistance in overcoming insecticide 1 effectiveness in RR
#' @param rr_restoration_ins2 effect of resistance in overcoming insecticide 2 effectiveness in RR
#' @param z.RR1_00 fitness cost of resistance allele 1 in insecticide free environment
#' @param z.RR2_00 fitness cost of resistance allele 2 in insecticide free environment
#' @param sexLinked whether resistance is sex linked, default=0(FALSE)
#' @param male_exposure_prop male exposure as a propoertion of female, default 1 for same, likely <1
#' @param correct_mix_deploy proportion of times that mixture is deployed correctly,
#' assumes that when not deployed correctly the single insecticides are used instead
#' @param addCombinedStrategy whether to add a line for the combined mixture/individual strategy, makes plot confusing so set to FALSE by default
#' @param addStrategyLabels add labels to plot for where strategies cross 0.5 line TRUE gives seq,mix1,mix2,mix3
#' @param strategyLabels = c("seq","mix1","adapt","mix2")
#' @param xlab xlab
#' @param ylab ylab
#' @param ylabs whether to add tick labels
#' @param xlabs whether to add tick labels
#' @param yticks tick positions
#' @param cex.axis scaling for x&y tick labs
#' @param addLegend whether to add a legend inside plot
#' @param main title for the plot
#' @param vlines colour of vertical lines to add to plot, NULL for none
#' @param maxX optional max x value for the plot to facilitate comparison with other plots
#' @param labelMixSeqRatio how many dp in label of the ratio of mix/seq, NULL for no label, only option compatible with addLegend
#'
#' @return list of plot components
#' @export
runcurtis_f2 <- function( max_gen = 500,
P_1 = 0.01,
P_2 = 0.01,
recomb_rate = 0.5,
exposure = 0.9,
exp1 = NULL,
exp2 = NULL,
phi.SS1_a0 = 0 ,
phi.SS1_A0 = 0.73 ,
phi.SS2_0b = 0 ,
phi.SS2_0B = 1 ,
W.SS1_00 = 1 ,
W.SS2_00 = 1 ,
h.RS1_00 = 0 ,
h.RS1_a0 = 0 ,
h.RS1_A0 = 0.17 ,
h.RS2_00 = 0 ,
h.RS2_0b = 0 ,
h.RS2_0B = 0.0016 ,
s.RR1_a0 = 0 ,
s.RR1_A0 = NULL ,
s.RR2_0b = 0 ,
s.RR2_0B = NULL ,
# 14/6/16 rr_restoration = s / effectiveness
rr_restoration_ins1 = 0.23/0.73,
rr_restoration_ins2 = 0.43/1,
z.RR1_00 = 0 ,
z.RR2_00 = 0 ,
sexLinked = 0 ,
male_exposure_prop = 1,
correct_mix_deploy = 1,
addCombinedStrategy = FALSE,
addStrategyLabels = TRUE,
strategyLabels = c("seq","mix1","adapt","mix2"),
ylab = "Resistance frequency %",
ylabs = TRUE,
yticks = c(0.1,1,5,10,50,100),
xlab = "Generation",
xlabs = TRUE,
cex.axis = 0.8,
addLegend = TRUE,
main = "",
vlines = 'grey95',
maxX = NULL,
labelMixSeqRatio = NULL
)
{
#fill exposure array I1
a <- setExposure(exposure=exposure, exp1=exp1, exp2=exp2,
insecticideUsed = "insecticide1",
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
inputI1 <- setInputOneScenario(a=a,
max_gen = max_gen,
P_1 = P_1,
P_2 = P_2,
recomb_rate = recomb_rate,
phi.SS1_a0 = phi.SS1_a0 ,
phi.SS1_A0 = phi.SS1_A0 ,
phi.SS2_0b = phi.SS2_0b ,
phi.SS2_0B = phi.SS2_0B ,
W.SS1_00 = W.SS1_00 ,
W.SS2_00 = W.SS2_00 ,
h.RS1_00 = h.RS1_00 ,
h.RS1_a0 = h.RS1_a0 ,
h.RS1_A0 = h.RS1_A0 ,
h.RS2_00 = h.RS2_00 ,
h.RS2_0b = h.RS2_0b ,
h.RS2_0B = h.RS2_0B ,
s.RR1_a0 = s.RR1_a0 ,
s.RR1_A0 = s.RR1_A0 ,
s.RR2_0b = s.RR2_0b ,
s.RR2_0B = s.RR2_0B ,
# 14/6/16
rr_restoration_ins1 = rr_restoration_ins1,
rr_restoration_ins2 = rr_restoration_ins2,
z.RR1_00 = z.RR1_00 ,
z.RR2_00 = z.RR2_00 ,
sexLinked = sexLinked ,
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
#fill exposure array I2
a <- setExposure(exposure=exposure, exp1=exp1, exp2=exp2,
insecticideUsed = "insecticide2",
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
inputI2 <- setInputOneScenario(a=a,
max_gen = max_gen,
P_1 = P_1,
P_2 = P_2,
recomb_rate = recomb_rate,
phi.SS1_a0 = phi.SS1_a0 ,
phi.SS1_A0 = phi.SS1_A0 ,
phi.SS2_0b = phi.SS2_0b ,
phi.SS2_0B = phi.SS2_0B ,
W.SS1_00 = W.SS1_00 ,
W.SS2_00 = W.SS2_00 ,
h.RS1_00 = h.RS1_00 ,
h.RS1_a0 = h.RS1_a0 ,
h.RS1_A0 = h.RS1_A0 ,
h.RS2_00 = h.RS2_00 ,
h.RS2_0b = h.RS2_0b ,
h.RS2_0B = h.RS2_0B ,
s.RR1_a0 = s.RR1_a0 ,
s.RR1_A0 = s.RR1_A0 ,
s.RR2_0b = s.RR2_0b ,
s.RR2_0B = s.RR2_0B ,
# 14/6/16
rr_restoration_ins1 = rr_restoration_ins1,
rr_restoration_ins2 = rr_restoration_ins2,
z.RR1_00 = z.RR1_00 ,
z.RR2_00 = z.RR2_00 ,
sexLinked = sexLinked ,
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
#fill exposure array mix
a <- setExposure(exposure=exposure, exp1=exp1, exp2=exp2,
insecticideUsed = "mixture",
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
inputmix <- setInputOneScenario(a=a,
max_gen = max_gen,
P_1 = P_1,
P_2 = P_2,
recomb_rate = recomb_rate,
phi.SS1_a0 = phi.SS1_a0 ,
phi.SS1_A0 = phi.SS1_A0 ,
phi.SS2_0b = phi.SS2_0b ,
phi.SS2_0B = phi.SS2_0B ,
W.SS1_00 = W.SS1_00 ,
W.SS2_00 = W.SS2_00 ,
h.RS1_00 = h.RS1_00 ,
h.RS1_a0 = h.RS1_a0 ,
h.RS1_A0 = h.RS1_A0 ,
h.RS2_00 = h.RS2_00 ,
h.RS2_0b = h.RS2_0b ,
h.RS2_0B = h.RS2_0B ,
s.RR1_a0 = s.RR1_a0 ,
s.RR1_A0 = s.RR1_A0 ,
s.RR2_0b = s.RR2_0b ,
s.RR2_0B = s.RR2_0B ,
# 14/6/16
rr_restoration_ins1 = rr_restoration_ins1,
rr_restoration_ins2 = rr_restoration_ins2,
z.RR1_00 = z.RR1_00 ,
z.RR2_00 = z.RR2_00 ,
sexLinked = sexLinked ,
male_exposure_prop = male_exposure_prop,
correct_mix_deploy = correct_mix_deploy )
#bind inputs together
input <- cbind(inputI1, inputI2, inputmix)
#run model
listOut <- runModel2(input=input, produce.plots = FALSE)
#this is a modified version of Beths function
#allowing curtis fig2 to be applied to any scenario
#plotcurtis_f2_generic( listOutMix$results[[1]], listOutI2$results[[1]], listOutI1$results[[1]] )
#be careful of order
#mix,2,1
plotcurtis_f2_generic( listOut$results[[3]], listOut$results[[2]], listOut$results[[1]],
addCombinedStrategy = addCombinedStrategy,
addStrategyLabels = addStrategyLabels,
strategyLabels = strategyLabels,
xlab = xlab,
xlabs = xlabs,
ylab = ylab,
ylabs = ylabs,
yticks = yticks,
cex.axis = cex.axis,
addLegend = addLegend,
main = main,
vlines = vlines,
maxX = maxX,
labelMixSeqRatio = labelMixSeqRatio
)
#add returning the results
invisible(listOut)
}
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