Method_Correlation
Zachary Noel
May 2, 2016
A function to do a t-test to determine if varias values in a linear model is significantly different than others. Arguments: reg = the linear model coefnum = 1 = intercept, 2 = beta … val = value you would like to test, the default lm tests if value is significantly different from zero.
This function will run a linear model of the percent relative growth, as well as correlations in both pearson and spearman and will also plot it in ggplot, if desired.
data = data.frame concentration = concentration plot = logical
RelGrowth.lm <- function(data, concentration, plot){
lin.mod <- lm(data$ppmeanrelgrowth[data$conc == concentration] ~ data$odrelgrowth[data$conc == concentration])
sum.linmod <- summary(lin.mod)
pearson <- cor.test(data$ppmeanrelgrowth[data$conc == concentration],
data$odrelgrowth[data$conc == concentration], method = "pearson")
spearman <- cor.test(data$ppmeanrelgrowth[data$conc == concentration],
data$odrelgrowth[data$conc == concentration], method = "spearman")
beta <- ttest(lin.mod, 2, 1) # t-test for Beta significantly different than one
bias <- (sum.linmod[[4]][2] - 1)*100 #percent Bias
coeff.var <- (sum.linmod[[4]][4]/sum.linmod[[4]][2])*100
if(plot == TRUE){
library(ggplot2)
p <- ggplot(data[data$conc == concentration,], aes(y = odrelgrowth, x = ppmeanrelgrowth)) +
geom_point(aes(colour = factor(species))) +
guides(colour = guide_legend(title = "Species")) +
scale_y_continuous(limits = c(0, 110), breaks = c(0, 25, 50, 75, 100)) +
scale_x_continuous(limits = c(0, 110), breaks = c(0, 25, 50, 75, 100)) +
geom_smooth(method = "lm", se = FALSE, fullrange = TRUE, col = "black") +
xlab("% Poison Plate Growth, relative to control") +
ylab("% Optical Density growth, relative to control") +
theme_bw() +
theme(axis.text.x = element_text(size = 10, face = "bold"),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.x = element_text(size = 15, face = "bold"),
axis.title.y = element_text(size = 15, face = "bold"),
legend.text = element_text(size = 10, face = "bold.italic"),
legend.key = element_blank(),
legend.title = element_text(size = 15, face="bold"))
results <- list(sum.linmod, pearson, spearman, beta, bias, coeff.var, p)
names(results) <- c("lm", "pearson", "spearman", "beta.t", "per.bias","coeff.var", "plot")
return(results)
} else {
results <- list(sum.linmod, pearson, spearman, beta, bias, coeff.var)
names(results) <- c("lm", "pearson", "spearman", "beta.t", "per.bias", "coeff.var")
return(results)
}
}We want to look at the correlation of the two methods, poison plate and optical density, to determine if the optical density method is any good.
Ethaboxam relative growth correlations
eth.cor.pp <- ddply(cor[cor$chem == "ethaboxam" & cor$method == "poison_plate",], c("is", "species", "conc"),
summarize,
ppmeanrelgrowth = 100*mean(relgrowth, na.rm = TRUE))
eth.cor.od <- ddply(cor[cor$chem == "ethaboxam" & cor$method == "optical_density",], c("is", "species", "conc"),
summarize,
odmeanrelgrowth = 100*mean(relgrowth, na.rm = TRUE))
eth.cor <- cbind.data.frame(eth.cor.pp, eth.cor.od$odmeanrelgrowth)
colnames(eth.cor) <- c("is", "species", "conc", "ppmeanrelgrowth", "odrelgrowth")
eth.001 <- RelGrowth.lm(eth.cor, concentration = 0.01, plot = TRUE)
eth.001## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.0305 -1.9817 0.2627 1.7828 10.3217
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 9.87510 9.10676 1.084
## data$odrelgrowth[data$conc == concentration] 0.88127 0.09423 9.353
## Pr(>|t|)
## (Intercept) 0.287
## data$odrelgrowth[data$conc == concentration] 0.000000000413 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.003 on 28 degrees of freedom
## Multiple R-squared: 0.7575, Adjusted R-squared: 0.7489
## F-statistic: 87.47 on 1 and 28 DF, p-value: 0.0000000004127
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 9.3528, df = 28, p-value = 0.0000000004127
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7430963 0.9368618
## sample estimates:
## cor
## 0.8703579
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 1734, p-value = 0.0004029
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.614238
##
##
## $beta.t
## [1] 0.2180441
##
## $per.bias
## [1] -11.87307
##
## $coeff.var
## [1] 10.69199
##
## $plot
eth.01 <- RelGrowth.lm(eth.cor, concentration = 0.1, plot = TRUE)
eth.01## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.889 -7.973 2.162 5.245 23.011
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -25.9061 7.2021 -3.597
## data$odrelgrowth[data$conc == concentration] 1.1886 0.0814 14.602
## Pr(>|t|)
## (Intercept) 0.00122 **
## data$odrelgrowth[data$conc == concentration] 0.0000000000000128 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.086 on 28 degrees of freedom
## Multiple R-squared: 0.8839, Adjusted R-squared: 0.8798
## F-statistic: 213.2 on 1 and 28 DF, p-value: 0.00000000000001276
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 14.602, df = 28, p-value = 0.00000000000001288
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8769341 0.9714102
## sample estimates:
## cor
## 0.9401724
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 606, p-value = 0.0000005663
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8651835
##
##
## $beta.t
## [1] 0.02804733
##
## $per.bias
## [1] 18.85864
##
## $coeff.var
## [1] 6.848325
##
## $plot## Warning: Removed 2 rows containing missing values (geom_smooth).
eth.05 <- RelGrowth.lm(eth.cor, concentration = 0.5, plot = TRUE)
eth.05## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.9634 -6.6009 -0.1541 5.9694 30.9924
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -9.53909 5.75551 -1.657
## data$odrelgrowth[data$conc == concentration] 1.03313 0.08329 12.404
## Pr(>|t|)
## (Intercept) 0.109
## data$odrelgrowth[data$conc == concentration] 0.00000000000068 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.62 on 28 degrees of freedom
## Multiple R-squared: 0.846, Adjusted R-squared: 0.8405
## F-statistic: 153.9 on 1 and 28 DF, p-value: 0.0000000000006796
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 12.404, df = 28, p-value = 0.0000000000006795
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8368378 0.9614634
## sample estimates:
## cor
## 0.9197995
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 586, p-value = 0.0000005246
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8696329
##
##
## $beta.t
## [1] 0.6937975
##
## $per.bias
## [1] 3.313298
##
## $coeff.var
## [1] 8.062027
##
## $plot
eth.1 <- RelGrowth.lm(eth.cor, concentration = 1, plot = TRUE)
eth.1## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.60 -12.24 -3.15 8.12 38.86
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 5.7876 6.0603 0.955
## data$odrelgrowth[data$conc == concentration] 0.9177 0.1172 7.832
## Pr(>|t|)
## (Intercept) 0.348
## data$odrelgrowth[data$conc == concentration] 0.0000000157 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.49 on 28 degrees of freedom
## Multiple R-squared: 0.6866, Adjusted R-squared: 0.6754
## F-statistic: 61.34 on 1 and 28 DF, p-value: 0.00000001569
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 7.8318, df = 28, p-value = 0.00000001569
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6676330 0.9155582
## sample estimates:
## cor
## 0.8286008
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 1004, p-value = 0.000001908
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.7766407
##
##
## $beta.t
## [1] 0.4884646
##
## $per.bias
## [1] -8.226295
##
## $coeff.var
## [1] 12.76849
##
## $plot
eth.5 <- RelGrowth.lm(eth.cor, concentration = 5, plot = TRUE)## Warning in cor.test.default(data$ppmeanrelgrowth[data$conc ==
## concentration], : Cannot compute exact p-value with tieseth.5## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.5090 -2.7679 -0.7679 2.4037 18.6542
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -9.70995 1.67512 -5.797
## data$odrelgrowth[data$conc == concentration] 0.93803 0.04154 22.579
## Pr(>|t|)
## (Intercept) 0.00000317 ***
## data$odrelgrowth[data$conc == concentration] < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.622 on 28 degrees of freedom
## Multiple R-squared: 0.9479, Adjusted R-squared: 0.9461
## F-statistic: 509.8 on 1 and 28 DF, p-value: < 0.00000000000000022
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 22.579, df = 28, p-value < 0.00000000000000022
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9447310 0.9875068
## sample estimates:
## cor
## 0.973621
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 2067.7, p-value = 0.00207
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5399933
##
##
## $beta.t
## [1] 0.146986
##
## $per.bias
## [1] -6.196723
##
## $coeff.var
## [1] 4.428861
##
## $plot## Warning: Removed 9 rows containing missing values (geom_smooth).
eth.20 <- RelGrowth.lm(eth.cor, concentration = 20, plot = TRUE)## Warning in cor.test.default(data$ppmeanrelgrowth[data$conc ==
## concentration], : Cannot compute exact p-value with tieseth.20## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.383 -2.803 0.611 3.599 8.722
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -12.2340 1.5216 -8.04
## data$odrelgrowth[data$conc == concentration] 0.9993 0.0420 23.79
## Pr(>|t|)
## (Intercept) 0.00000000936 ***
## data$odrelgrowth[data$conc == concentration] < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.954 on 28 degrees of freedom
## Multiple R-squared: 0.9529, Adjusted R-squared: 0.9512
## F-statistic: 566.2 on 1 and 28 DF, p-value: < 0.00000000000000022
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 23.794, df = 28, p-value < 0.00000000000000022
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9499662 0.9887137
## sample estimates:
## cor
## 0.9761532
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 2402.7, p-value = 0.009538
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4654791
##
##
## $beta.t
## [1] 0.9862368
##
## $per.bias
## [1] -0.0730954
##
## $coeff.var
## [1] 4.202696
##
## $plot## Warning: Removed 6 rows containing missing values (geom_smooth).
Mefenoxam relative growth correlations
mef.cor.pp <- ddply(cor[cor$chem == "mefenoxam" & cor$method == "poison_plate",], c("is", "species", "conc"),
summarize,
ppmeanrelgrowth = 100*mean(relgrowth, na.rm = TRUE))
mef.cor.od <- ddply(cor[cor$chem == "mefenoxam" & cor$method == "optical_density",], c("is", "species", "conc"),
summarize,
odmeanrelgrowth = 100*mean(relgrowth, na.rm = TRUE))
mef.cor.od <- mef.cor.od[!mef.cor.od$conc == 100,]
mef.cor.pp <- mef.cor.pp[!mef.cor.pp$conc == 100,]
mef.cor <- cbind.data.frame(mef.cor.pp, mef.cor.od$odmeanrelgrowth)
colnames(mef.cor) <- c("is", "species", "conc", "ppmeanrelgrowth", "odrelgrowth")
mef.001 <- RelGrowth.lm(mef.cor, concentration = 0.01, plot = TRUE)
mef.001## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.7226 -2.4279 0.2544 4.7137 11.5604
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 52.53813 8.14792 6.448
## data$odrelgrowth[data$conc == concentration] 0.40612 0.08881 4.573
## Pr(>|t|)
## (Intercept) 0.00000217 ***
## data$odrelgrowth[data$conc == concentration] 0.000165 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.191 on 21 degrees of freedom
## Multiple R-squared: 0.4989, Adjusted R-squared: 0.4751
## F-statistic: 20.91 on 1 and 21 DF, p-value: 0.0001652
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 4.5729, df = 21, p-value = 0.0001652
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4149891 0.8663221
## sample estimates:
## cor
## 0.7063615
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 628, p-value = 0.000388
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.6897233
##
##
## $beta.t
## [1] 0.00000128355
##
## $per.bias
## [1] -59.38804
##
## $coeff.var
## [1] 21.86781
##
## $plot## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 17 rows containing missing values (geom_smooth).
mef.01 <- RelGrowth.lm(mef.cor, concentration = 0.1, plot = TRUE)
mef.01## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.4901 -9.9562 0.6523 9.6580 28.9574
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -6.2375 7.1482 -0.873
## data$odrelgrowth[data$conc == concentration] 0.9262 0.1184 7.825
## Pr(>|t|)
## (Intercept) 0.393
## data$odrelgrowth[data$conc == concentration] 0.000000117 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.31 on 21 degrees of freedom
## Multiple R-squared: 0.7446, Adjusted R-squared: 0.7325
## F-statistic: 61.23 on 1 and 21 DF, p-value: 0.0000001171
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 7.8253, df = 21, p-value = 0.0000001171
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6995462 0.9405659
## sample estimates:
## cor
## 0.8629212
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 406, p-value = 0.000006679
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.7994071
##
##
## $beta.t
## [1] 0.5398335
##
## $per.bias
## [1] -7.377004
##
## $coeff.var
## [1] 12.77914
##
## $plot## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
mef.05 <- RelGrowth.lm(mef.cor, concentration = 0.5, plot = TRUE)## Warning in cor.test.default(data$ppmeanrelgrowth[data$conc ==
## concentration], : Cannot compute exact p-value with tiesmef.05## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.970 -6.336 -3.075 5.887 23.732
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -1.24754 3.25583 -0.383
## data$odrelgrowth[data$conc == concentration] 1.03001 0.08439 12.205
## Pr(>|t|)
## (Intercept) 0.705
## data$odrelgrowth[data$conc == concentration] 0.0000000000532 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.17 on 21 degrees of freedom
## Multiple R-squared: 0.8764, Adjusted R-squared: 0.8706
## F-statistic: 149 on 1 and 21 DF, p-value: 0.00000000005324
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 12.205, df = 21, p-value = 0.00000000005324
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8532511 0.9729345
## sample estimates:
## cor
## 0.9361856
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 654.61, p-value = 0.0003928
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.6765746
##
##
## $beta.t
## [1] 0.7257129
##
## $per.bias
## [1] 3.000686
##
## $coeff.var
## [1] 8.193344
##
## $plot
mef.1 <- RelGrowth.lm(mef.cor, concentration = 1, plot = TRUE)## Warning in cor.test.default(data$ppmeanrelgrowth[data$conc ==
## concentration], : Cannot compute exact p-value with tiesmef.1## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.2073 -5.1524 -0.6551 6.1889 16.6888
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -2.75342 2.41411 -1.141
## data$odrelgrowth[data$conc == concentration] 0.95967 0.06523 14.711
## Pr(>|t|)
## (Intercept) 0.267
## data$odrelgrowth[data$conc == concentration] 0.00000000000156 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.912 on 21 degrees of freedom
## Multiple R-squared: 0.9115, Adjusted R-squared: 0.9073
## F-statistic: 216.4 on 1 and 21 DF, p-value: 0.000000000001565
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 14.711, df = 21, p-value = 0.000000000001565
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8946287 0.9809135
## sample estimates:
## cor
## 0.9547498
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 769.79, p-value = 0.001613
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.61967
##
##
## $beta.t
## [1] 0.5430472
##
## $per.bias
## [1] -4.033238
##
## $coeff.var
## [1] 6.797615
##
## $plot
mef.10 <- RelGrowth.lm(mef.cor, concentration = 10, plot = TRUE)## Warning in cor.test.default(data$ppmeanrelgrowth[data$conc ==
## concentration], : Cannot compute exact p-value with tiesmef.10## $lm
##
## Call:
## lm(formula = data$ppmeanrelgrowth[data$conc == concentration] ~
## data$odrelgrowth[data$conc == concentration])
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.708 -3.826 1.015 4.699 14.033
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -3.32081 2.01807 -1.646
## data$odrelgrowth[data$conc == concentration] 0.92239 0.05793 15.923
## Pr(>|t|)
## (Intercept) 0.115
## data$odrelgrowth[data$conc == concentration] 0.000000000000338 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.963 on 21 degrees of freedom
## Multiple R-squared: 0.9235, Adjusted R-squared: 0.9199
## F-statistic: 253.6 on 1 and 21 DF, p-value: 0.0000000000003381
##
##
## $pearson
##
## Pearson's product-moment correlation
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## t = 15.923, df = 21, p-value = 0.000000000000338
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9087863 0.9835784
## sample estimates:
## cor
## 0.9609959
##
##
## $spearman
##
## Spearman's rank correlation rho
##
## data: data$ppmeanrelgrowth[data$conc == concentration] and data$odrelgrowth[data$conc == concentration]
## S = 828.94, p-value = 0.003016
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5904454
##
##
## $beta.t
## [1] 0.1946402
##
## $per.bias
## [1] -7.760812
##
## $coeff.var
## [1] 6.280042
##
## $plot## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
This code iterates through every isolate and generates a relative and absolute EC50 using the LL.4 model of drc and saves the output. I already went through and picked out the isolates for both mefenoxam and ethaboxam that have an EC50 beyond the concentration range tested in this study.
# briefly we are going to split up the data frame into each concentration to make it easier to take out individual isolates
cor_eth <- cor[cor$chem == "ethaboxam",]
cor_mef <- cor[cor$chem == "mefenoxam",]
# taking out insensitive isolates, otherwise convergence would not occur with the ll.4 model
insens_iso_eth <- c("MISO_8-29.1", "C-MNSO2_2-21", "NDSO_L-8-6", "ILSO_6-15C")
insens_iso_mef <- c("V-MISO2_6-46", "23.4B", "1.18A")
cor_eth <- cor_eth[!cor_eth$is %in% insens_iso_eth,]
cor_mef <- cor_mef[!cor_mef$is %in% insens_iso_mef,]
cor_eth$is <- factor(cor_eth$is)
cor_mef$is <- factor(cor_mef$is)
cor <- rbind.data.frame(cor_eth, cor_mef)
relative <- function(data){
EC50.pp.rel <- data.frame(ED(data,
respLev = c(50),
type = "relative",
interval = "delta"),
level = 0.95)
rel.ec50.pp <- EC50.pp.rel[1][[1]]
return(rel.ec50.pp)
}
absolute <- function(data){
EC50.pp.abs <- data.frame(ED(data,
respLev = c(50),
type = "absolute",
interval = "delta"),
level = 0.95)
abs.ec50.pp <- EC50.pp.abs[1][[1]]
return(abs.ec50.pp)
}
plug_EC <- function(chemistry){
nm <- unique(cor$is[cor$chem == as.character(chemistry)])
dataframe_names <- c("is", "species", "method", "trial", "chem", "absolute", "relative")
meth.cor <- NULL
for (i in seq_along(nm)){
mefcor.pp.drc <- drm(100*relgrowth ~ conc, data = cor[cor$is == nm[[i]] & cor$method == "poison_plate" & cor$chem == as.character(chemistry),], curveid = trial, fct = LL.4(), na.action = na.omit)
mefcor.od.drc <- drm(100*relgrowth ~ conc, data = cor[cor$is == nm[[i]] & cor$method == "optical_density" & cor$chem == as.character(chemistry),], curveid = trial, fct = LL.4(), na.action = na.omit)
paste(print(nm[[i]]))
# RELATIVE
rel.pp.mef <- relative(mefcor.pp.drc)
rel.od.mef <- relative(mefcor.od.drc)
# ABSOLUTE
abs.pp.mef <- absolute(mefcor.pp.drc)
abs.od.mef <- absolute(mefcor.od.drc)
mef.cor_od <- data.frame(cbind.data.frame(
nm[[i]],
as.character(unique(cor$species[cor$is == nm[[i]]])),
as.character(unique(cor$method[cor$is == nm[[i]] & cor$method == "optical_density"])),
unique(cor$trial[cor$is == nm[[i]] & cor$method == "optical_density"]),
as.character(chemistry),
as.numeric(abs.od.mef),
as.numeric(rel.od.mef)))
colnames(mef.cor_od) <- dataframe_names
mef.cor_pp <- data.frame(cbind.data.frame(
nm[[i]],
as.character(unique(cor$species[cor$is == nm[[i]]])),
as.character(unique(cor$method[cor$is == nm[[i]] & cor$method == "poison_plate"])),
unique(cor$trial[cor$is == nm[[i]] & cor$method == "poison_plate"]),
as.character(chemistry),
as.numeric(abs.pp.mef),
as.numeric(rel.pp.mef)))
colnames(mef.cor_pp) <- dataframe_names
meth.cor <- rbind.data.frame(meth.cor, mef.cor_od, mef.cor_pp)
}
return(meth.cor)
}
mefenoxam_ec50 <- plug_EC("mefenoxam")
ethaboxam_ec50 <- plug_EC("ethaboxam")
ec50_cor <- rbind.data.frame(ethaboxam_ec50, mefenoxam_ec50)
# not sure why one of the isolates had duplicates but they did, so this code will find duplicates in the absolute ec50s and delete the entire row.
ec50_cor <- ec50_cor[-which(duplicated(ec50_cor$absolute)),] Table of EC50 values for every isolate tested for both mefenoxam and ethaboxam on both poison plate and optical density method.
| is | species | chem | method | mean.abs | std.abs | mean.rel | std.rel |
|---|---|---|---|---|---|---|---|
| AR_127.S.2.3.A | Pythium irregulare | ethaboxam | optical_density | 0.6017397 | 0.4608413 | 0.3258476 | 0.2118367 |
| AR_127.S.2.3.A | Pythium irregulare | ethaboxam | poison_plate | 0.6474189 | 0.0242181 | 0.7462384 | 0.1600082 |
| AR_127.S.2.3.A | Pythium irregulare | mefenoxam | optical_density | 0.1831292 | 0.0722302 | 0.1036954 | 0.0178995 |
| AR_127.S.2.3.A | Pythium irregulare | mefenoxam | poison_plate | 0.1259046 | 0.0503855 | 0.1228201 | 0.0557412 |
| AR_262.S.1.6.A | Pythium aphanidermatum | ethaboxam | optical_density | 0.4890281 | 0.0532135 | 0.4472674 | 0.0672784 |
| AR_262.S.1.6.A | Pythium aphanidermatum | ethaboxam | poison_plate | 0.2093399 | 0.0147487 | 0.1991737 | 0.0172343 |
| ARS_284.S1.4.A | Pythium lutarium | ethaboxam | optical_density | 0.2669444 | 0.0983561 | 0.2488793 | 0.0977931 |
| ARS_284.S1.4.A | Pythium lutarium | ethaboxam | poison_plate | 0.2260918 | 0.0293079 | 0.1949081 | 0.0054857 |
| C-NDSO2_1-11 | Pythium perplexum | ethaboxam | optical_density | 0.1673536 | 0.0045666 | 0.1324687 | 0.0026915 |
| C-NDSO2_1-11 | Pythium perplexum | ethaboxam | poison_plate | 0.0983410 | 0.0112713 | 0.0977276 | 0.0100928 |
| IASO_10-37.8RT | Pythium aff. dissotocum | ethaboxam | optical_density | 1.1413192 | 0.3019554 | 1.0115718 | 0.2570090 |
| IASO_10-37.8RT | Pythium aff. dissotocum | ethaboxam | poison_plate | 1.0220161 | 0.6068654 | 1.0072584 | 0.5928676 |
| IASO_10-38.14RT | Pythium aff. dissotocum | ethaboxam | optical_density | 1.0125252 | 0.2669542 | 0.9121016 | 0.1950198 |
| IASO_10-38.14RT | Pythium aff. dissotocum | ethaboxam | poison_plate | 1.7250786 | 0.2861206 | 1.8877662 | 0.3800344 |
| IASO_10-39.16rt | Pythium aff. dissotocum | ethaboxam | optical_density | 0.6248467 | 0.0759800 | 0.5562301 | 0.0206426 |
| IASO_10-39.16rt | Pythium aff. dissotocum | ethaboxam | poison_plate | 1.6056028 | 0.1531086 | 1.7370847 | 0.1606027 |
| IASO_6-10.15h | Pythium oopapillum | ethaboxam | optical_density | 0.5136203 | 0.0286944 | 0.4836774 | 0.0102900 |
| IASO_6-10.15h | Pythium oopapillum | ethaboxam | poison_plate | 1.1130479 | 0.2173577 | 1.0919763 | 0.2239991 |
| ILSO_1-31 | Pythium irregulare | ethaboxam | optical_density | 0.8616940 | 0.1247902 | 0.8372427 | 0.0556342 |
| ILSO_1-31 | Pythium irregulare | ethaboxam | poison_plate | 0.8058171 | 0.0387544 | 1.0671747 | 0.0765512 |
| ILSO_3-48C | Pythium irregulare | ethaboxam | optical_density | 0.9968918 | 0.2276545 | 0.5450930 | 0.1574642 |
| ILSO_3-48C | Pythium irregulare | ethaboxam | poison_plate | 0.7286362 | 0.0211646 | 0.9552630 | 0.0460969 |
| ILSO_3-48C | Pythium irregulare | mefenoxam | optical_density | 0.2218693 | 0.0780914 | 0.1509015 | 0.0575959 |
| ILSO_3-48C | Pythium irregulare | mefenoxam | poison_plate | 0.1263038 | 0.0133000 | 0.1136575 | 0.0248296 |
| ILSO_5-42C | Pythium oopapillum | ethaboxam | optical_density | 1.0026325 | 0.4214322 | 0.9374770 | 0.3795077 |
| ILSO_5-42C | Pythium oopapillum | ethaboxam | poison_plate | 1.3388329 | 0.0166538 | 1.5284298 | 0.1501364 |
| ILSO_5-42C | Pythium oopapillum | mefenoxam | optical_density | 0.0934891 | 0.0727647 | 0.0828173 | 0.0621251 |
| ILSO_5-42C | Pythium oopapillum | mefenoxam | poison_plate | 0.0176811 | 0.0013969 | 0.0190659 | 0.0028424 |
| ILSO_6-2B | Pythium oopapillum | ethaboxam | optical_density | 0.7590296 | 0.0573760 | 0.6391139 | 0.0124021 |
| ILSO_6-2B | Pythium oopapillum | ethaboxam | poison_plate | 0.9836553 | 0.0599357 | 0.9708861 | 0.0039751 |
| ILSO_6-2B | Pythium oopapillum | mefenoxam | optical_density | 0.1616391 | 0.0430944 | 0.1512861 | 0.0385509 |
| ILSO_6-2B | Pythium oopapillum | mefenoxam | poison_plate | 0.1374047 | 0.0218105 | 0.1122750 | 0.0353251 |
| INSO_1-10C | Pythium sylvaticum | ethaboxam | optical_density | 0.6021485 | 0.2261376 | 0.4896181 | 0.2288380 |
| INSO_1-10C | Pythium sylvaticum | ethaboxam | poison_plate | 0.1023615 | 0.0310532 | 0.1085730 | 0.0178761 |
| INSO_1-10C | Pythium sylvaticum | mefenoxam | optical_density | 0.0556356 | 0.0142788 | 0.0540463 | 0.0146426 |
| INSO_1-10C | Pythium sylvaticum | mefenoxam | poison_plate | 0.0240773 | 0.0109447 | 0.0240587 | 0.0109250 |
| INSO_1-8A | Pythium lutarium | ethaboxam | optical_density | 0.6975005 | 0.1292836 | 0.6548995 | 0.0999101 |
| INSO_1-8A | Pythium lutarium | ethaboxam | poison_plate | 1.4568919 | 0.1234575 | 1.5417715 | 0.0704994 |
| KSSO_6-1 | Pythium ultimum var. ultimum | ethaboxam | optical_density | 2.1738744 | 0.1268949 | 1.8935903 | 0.0802024 |
| KSSO_6-1 | Pythium ultimum var. ultimum | ethaboxam | poison_plate | 1.4760269 | 0.1444716 | 1.6207549 | 0.0955815 |
| KSSO_6-47 | Phytophthora sansomeana | ethaboxam | optical_density | 0.0634017 | 0.0315296 | 0.0526138 | 0.0286371 |
| KSSO_6-47 | Phytophthora sansomeana | ethaboxam | poison_plate | 0.0324276 | 0.0043185 | 0.0324150 | 0.0039692 |
| MISO_1-4 | Pythium lutarium | ethaboxam | optical_density | 0.9515964 | 0.1695581 | 0.8880759 | 0.1894814 |
| MISO_1-4 | Pythium lutarium | ethaboxam | poison_plate | 0.6058080 | 0.0382837 | 0.6033125 | 0.0375648 |
| MISO_5-19H | Pythium torulosum | ethaboxam | optical_density | 1.3033651 | 0.3326300 | 0.9839949 | 0.2931130 |
| MISO_5-19H | Pythium torulosum | ethaboxam | poison_plate | 1.1133215 | 0.0441768 | 1.1914970 | 0.0877672 |
| MISO_8-10 | Pythium ultimum var. ultimum | ethaboxam | optical_density | 0.7494623 | 0.2384325 | 0.6596120 | 0.2148583 |
| MISO_8-10 | Pythium ultimum var. ultimum | ethaboxam | poison_plate | 1.2705559 | 0.0327570 | 1.3791302 | 0.0529770 |
| NDSO_1-42 | Pythium sylvaticum | ethaboxam | optical_density | 0.5083377 | 0.0455087 | 0.4342847 | 0.0397514 |
| NDSO_1-42 | Pythium sylvaticum | ethaboxam | poison_plate | 0.5029008 | 0.0315540 | 0.5163838 | 0.0454677 |
| NDSO_1-42 | Pythium sylvaticum | mefenoxam | optical_density | 0.0843794 | 0.0395403 | 0.0776105 | 0.0309785 |
| NDSO_1-42 | Pythium sylvaticum | mefenoxam | poison_plate | 0.0584407 | 0.0271372 | 0.0592125 | 0.0288098 |
| NESO_2-13 | Pythium sylvaticum | ethaboxam | optical_density | 0.5351099 | 0.1587134 | 0.4859459 | 0.1842366 |
| NESO_2-13 | Pythium sylvaticum | ethaboxam | poison_plate | 0.2755640 | 0.0195702 | 0.3037442 | 0.0022381 |
| NESO_2-13 | Pythium sylvaticum | mefenoxam | optical_density | 0.1626739 | 0.0513231 | 0.1500946 | 0.0446556 |
| NESO_2-13 | Pythium sylvaticum | mefenoxam | poison_plate | 0.0659475 | 0.0136608 | 0.0657198 | 0.0179300 |
| NESO_4-29 | Pythium perplexum | ethaboxam | optical_density | 0.0997645 | 0.0070532 | 0.0880328 | 0.0143253 |
| NESO_4-29 | Pythium perplexum | ethaboxam | poison_plate | 0.1238281 | 0.0031861 | 0.1199439 | 0.0006862 |
| V-KSSO2_1-7 | Phytophthora sansomeana | ethaboxam | optical_density | 0.0477238 | 0.0204292 | 0.0408790 | 0.0145521 |
| V-KSSO2_1-7 | Phytophthora sansomeana | ethaboxam | poison_plate | 0.0164446 | 0.0004557 | 0.0161274 | 0.0007975 |
| V-KSSO2_3-6 | Phytophthora sansomeana | ethaboxam | optical_density | 0.0527088 | 0.0075370 | 0.0435138 | 0.0123592 |
| V-KSSO2_3-6 | Phytophthora sansomeana | ethaboxam | poison_plate | 0.0331940 | 0.0092453 | 0.0336671 | 0.0089418 |
| V-MISO2_2-57 | Pythium perplexum | ethaboxam | optical_density | 0.1575673 | 0.0323676 | 0.1342038 | 0.0319532 |
| V-MISO2_2-57 | Pythium perplexum | ethaboxam | poison_plate | 0.0746280 | 0.0000147 | 0.0735610 | 0.0014948 |
| WISO_4-13 | Pythium ultimum var. ultimum | ethaboxam | optical_density | 1.4114181 | 0.3025662 | 1.2496867 | 0.2475802 |
| WISO_4-13 | Pythium ultimum var. ultimum | ethaboxam | poison_plate | 1.6811566 | 0.0761205 | 1.7426576 | 0.0073123 |
| AR_96.S.2.1.A | Pythium spinosum | mefenoxam | optical_density | 0.0640711 | 0.0155281 | 0.0552741 | 0.0148170 |
| AR_96.S.2.1.A | Pythium spinosum | mefenoxam | poison_plate | 0.0408055 | 0.0210795 | 0.0416514 | 0.0219245 |
| C-KSSO2_1-25 | Phytopythium litorale | mefenoxam | optical_density | 1.1289415 | 0.2611411 | 0.6216587 | 0.0899566 |
| C-KSSO2_1-25 | Phytopythium litorale | mefenoxam | poison_plate | 0.9949350 | 0.1711380 | 0.6115304 | 0.1309216 |
| C-SDSO2_5-35 | Pythium intermedium | mefenoxam | optical_density | 0.1038246 | 0.0015915 | 0.1042345 | 0.0085674 |
| C-SDSO2_5-35 | Pythium intermedium | mefenoxam | poison_plate | 0.2039187 | 0.0848682 | 0.1791958 | 0.0740958 |
| IASO_3-41.17 | Phytophthora sojae | mefenoxam | optical_density | 0.0564770 | 0.0357976 | 0.0646445 | 0.0374231 |
| IASO_3-41.17 | Phytophthora sojae | mefenoxam | poison_plate | 0.0193404 | 0.0062906 | 0.0193835 | 0.0063338 |
| IASO_6-10.15H | Pythium oopapillum | mefenoxam | optical_density | 0.2521354 | 0.0684946 | 0.2051422 | 0.0498267 |
| IASO_6-10.15H | Pythium oopapillum | mefenoxam | poison_plate | 0.1369200 | 0.0624607 | 0.1212678 | 0.0535314 |
| ILSO_3-21A | Pythium ultimum var. sporangiiferum | mefenoxam | optical_density | 0.0171647 | 0.0110661 | 0.0164300 | 0.0103643 |
| ILSO_3-21A | Pythium ultimum var. sporangiiferum | mefenoxam | poison_plate | 0.0444185 | 0.0319651 | 0.0486010 | 0.0361461 |
| INSO_3-10 | Pythium pleroticum | mefenoxam | optical_density | 0.0354960 | 0.0029223 | 0.0307489 | 0.0010692 |
| INSO_3-10 | Pythium pleroticum | mefenoxam | poison_plate | 0.0199470 | 0.0063061 | 0.0201349 | 0.0064933 |
| INSO_3-43 | Pythium spinosum | mefenoxam | optical_density | 0.2043519 | 0.0807916 | 0.1498540 | 0.0461110 |
| INSO_3-43 | Pythium spinosum | mefenoxam | poison_plate | 0.0983442 | 0.0584485 | 0.0984697 | 0.0614379 |
| INSO_4-40 | Pythium spinosum | mefenoxam | optical_density | 0.0905818 | 0.0403220 | 0.0878968 | 0.0396769 |
| INSO_4-40 | Pythium spinosum | mefenoxam | poison_plate | 0.0502867 | 0.0279427 | 0.0467290 | 0.0252301 |
| INSO_5-50 | Pythium ultimum var. sporangiiferum | mefenoxam | optical_density | 0.0193989 | 0.0011695 | 0.0195310 | 0.0012364 |
| INSO_5-50 | Pythium ultimum var. sporangiiferum | mefenoxam | poison_plate | 0.0394601 | 0.0130432 | 0.0409852 | 0.0183301 |
| KSSO_6-30 | Pythium ultimum var. sporangiiferum | mefenoxam | optical_density | 0.0155855 | 0.0055027 | 0.0140713 | 0.0044018 |
| KSSO_6-30 | Pythium ultimum var. sporangiiferum | mefenoxam | poison_plate | 0.0729989 | 0.0409378 | 0.0843618 | 0.0553740 |
| V-IASO2_6-55_1 | Phytopythium litorale | mefenoxam | optical_density | 0.9960691 | 0.2530901 | 0.4135866 | 0.1211882 |
| V-IASO2_6-55_1 | Phytopythium litorale | mefenoxam | poison_plate | 2.4032623 | 0.9107058 | 0.9779876 | 0.5341688 |
| V-SDSO2_1-53 | Phytophthora sojae | mefenoxam | optical_density | 0.0846261 | 0.0036287 | 0.0807847 | 0.0015759 |
| V-SDSO2_1-53 | Phytophthora sojae | mefenoxam | poison_plate | 0.0525604 | 0.0387617 | 0.0529119 | 0.0391127 |
Lets do an ANOVA for each chemistry and the effect of method. We have log transformed these data for homogeneity of variance.
model 1: method as fixed effect and isolate as random effect
We are treating isolate as a random effect because we we sampled these isolates from a larger possible population of isolates and we want to generalize over all isolates
lm_mef <- lmer(absolute ~ method + (1|is), data = ec50_cor[ec50_cor$chem == "mefenoxam",])
car::Anova(lm_mef, test.statistic="F", type = 2) # using type II ANOVA for unbalanced data. Some isolates have more technical replicates than others. So the mean over all isolates is different. ## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: absolute
## F Df Df.res Pr(>F)
## method 0.8358 1 84.598 0.3632plot(lm_mef, type = c("p", "smooth"), id = 0.05) # regression diagnostics
lsmeans_mef <- lsmeans::lsmeans(lm_mef, "method")
plot(lsmeans_mef)
This is what we expected, no significant differnces for the method.
Lets do the same for ethaboxam.
lm_eth <- lmer(absolute ~ method + (1|is), data = ec50_cor[ec50_cor$chem == "ethaboxam",])
car::Anova(lm_eth, test.statistic="F", type = 2) # using type II ANOVA for unbalanced data. Some isolates have more technical replicates than others. So the mean over all isolates is different. ## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: absolute
## F Df Df.res Pr(>F)
## method 1.1121 1 82.098 0.2947plot(lm_eth, type = c("p", "smooth"), id = 0.05)# regression diagnostics
lsmeans_eth <- lsmeans::lsmeans(lm_eth, "method")
plot(lsmeans_eth)
Lets do correlation analysis between the two methods separated by chemistry.
We are testing the correlation of the absolute EC50s between the two methods. We are going to use spearman’s correlation coeffiecient since it is rank based it can handle outliers with high leverage.
cor_mef <- lm(mean.abs.pp ~ mean.abs.od, data = EC50[EC50$chem == "mefenoxam",])
summary(cor_mef)##
## Call:
## lm(formula = mean.abs.pp ~ mean.abs.od, data = EC50[EC50$chem ==
## "mefenoxam", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.69793 -0.08873 0.00269 0.05529 0.91905
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.07988 0.07755 -1.030 0.317
## mean.abs.od 1.57027 0.21667 7.247 0.000000972 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2866 on 18 degrees of freedom
## Multiple R-squared: 0.7448, Adjusted R-squared: 0.7306
## F-statistic: 52.52 on 1 and 18 DF, p-value: 0.000000972par(mfrow = c(2,2))
plot(cor_mef)
ttest(cor_mef, 1, 0) # tests if intercept is significantly different than 0## [1] 0.3166105ttest(cor_mef, 2, 1) # tests if slope (beta) is significantly different than 1## [1] 0.01692273There is a significant linear relationship between the mean absolute EC50 using either method. Since it looked like there were some points with a bit of leverage we will use spearman’s correlation to test the significance of the correlation.We will also look at the spearman:pearson correlation ratio to see if the correlation is more monotonic or linear.
spear.cor <- cor.test(EC50$mean.abs.pp[EC50$chem == "mefenoxam"],
EC50$mean.abs.od[EC50$chem == "mefenoxam"],
method = "spearman")
pear.cor <- cor.test(EC50$mean.abs.pp[EC50$chem == "mefenoxam"],
EC50$mean.abs.od[EC50$chem == "mefenoxam"],
method = "pearson")
spear.cor##
## Spearman's rank correlation rho
##
## data: EC50$mean.abs.pp[EC50$chem == "mefenoxam"] and EC50$mean.abs.od[EC50$chem == "mefenoxam"]
## S = 358, p-value = 0.0003757
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.7308271pear.cor##
## Pearson's product-moment correlation
##
## data: EC50$mean.abs.pp[EC50$chem == "mefenoxam"] and EC50$mean.abs.od[EC50$chem == "mefenoxam"]
## t = 7.2471, df = 18, p-value = 0.000000972
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6802565 0.9447289
## sample estimates:
## cor
## 0.8629927Since the spearman correlation coeficient is lower than the pearson coefficient, this indicates we have more of a linear relationship than a monotonic one. This is a good thing because we would expect a perfect linear relationship between the methods.
cor_eth <- lm(mean.abs.pp ~ mean.abs.od, data = EC50[EC50$chem == "ethaboxam",])
summary(cor_eth)##
## Call:
## lm(formula = mean.abs.pp ~ mean.abs.od, data = EC50[EC50$chem ==
## "ethaboxam", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5862 -0.1956 -0.1347 0.2654 0.9172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1342 0.1364 0.984 0.335
## mean.abs.od 0.8869 0.1625 5.457 0.0000131 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4029 on 24 degrees of freedom
## Multiple R-squared: 0.5538, Adjusted R-squared: 0.5352
## F-statistic: 29.78 on 1 and 24 DF, p-value: 0.00001312par(mfrow = c(2,2))
plot(cor_eth)
ttest(cor_eth, 1, 0) # tests if intercept is significantly different than 0## [1] 0.3350091ttest(cor_eth, 2, 1) # tests if slope (beta) is significantly different than 1## [1] 0.4931334There is a significant linear relationship between the mean absolute EC50 using either method. Since it looked like there were some points with a bit of leverage we will use spearman’s correlation to test the significance of the correlation.
spear.cor <- cor.test(EC50$mean.abs.pp[EC50$chem == "ethaboxam"],
EC50$mean.abs.od[EC50$chem == "ethaboxam"],
method = "spearman")
pear.cor <- cor.test(EC50$mean.abs.pp[EC50$chem == "ethaboxam"],
EC50$mean.abs.od[EC50$chem == "ethaboxam"],
method = "pearson")
spear.cor##
## Spearman's rank correlation rho
##
## data: EC50$mean.abs.pp[EC50$chem == "ethaboxam"] and EC50$mean.abs.od[EC50$chem == "ethaboxam"]
## S = 482, p-value = 0.000001932
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8352137pear.cor##
## Pearson's product-moment correlation
##
## data: EC50$mean.abs.pp[EC50$chem == "ethaboxam"] and EC50$mean.abs.od[EC50$chem == "ethaboxam"]
## t = 5.4573, df = 24, p-value = 0.00001312
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5012888 0.8783242
## sample estimates:
## cor
## 0.7441461Since the spearman correlation coeficient is higher than the pearson coefficient, this indicates we have more of a monotonic relationship than a linear one. This is ok, but we would like to see it flipped around.
Lets look at the correlation of the relative growth values
ggplot(EC50, aes(mean.abs.pp, mean.abs.od)) +
geom_point(aes(colour = species_od)) +
#geom_errorbarh(aes(xmax = mean.rel.pp + std.rel.pp, xmin = mean.rel.pp - std.rel.pp, height = .01)) +
#geom_errorbar(aes(ymax = mean.rel.od + std.rel.od, ymin = mean.rel.od - std.rel.od, width = .01)) +
geom_smooth(method = "lm", se = FALSE) +
theme_bw() +
facet_wrap(~chem)
EC50_spec <- ddply(EC50, c("species_od","chem", "method"),
summarize,
mean.abs.pp = mean(mean.abs.pp, na.rm = TRUE),
std.abs.pp = std.error(std.abs.pp, na.rm = TRUE),
mean.abs.od = mean(mean.abs.od, na.rm = TRUE),
std.abs.od = std.error(std.abs.od, na.rm = TRUE))
ggplot(EC50_spec, aes(mean.abs.pp, mean.abs.od)) +
geom_point(aes(colour = species_od, shape = chem)) +
#geom_errorbarh(aes(xmax = mean.abs.pp + std.abs.pp, xmin = mean.abs.pp - std.abs.pp, height = .01)) +
#geom_errorbar(aes(ymax = mean.abs.od + std.abs.od, ymin = mean.abs.od - std.abs.od, width = .01)) +
geom_smooth(method = "lm", se = FALSE) +
theme_bw() +
facet_wrap(~chem)
summary(lm(mean.abs.pp ~ mean.abs.od, data = EC50_spec))##
## Call:
## lm(formula = mean.abs.pp ~ mean.abs.od, data = EC50_spec)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36128 -0.11452 -0.02151 0.06983 0.50524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04456 0.07842 -0.568 0.577
## mean.abs.od 1.16557 0.12007 9.707 0.0000000239 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2351 on 17 degrees of freedom
## Multiple R-squared: 0.8472, Adjusted R-squared: 0.8382
## F-statistic: 94.23 on 1 and 17 DF, p-value: 0.00000002389