Complete database
All individuals are included
num_ind <- 23
Color <- viridis(num_ind)
df_ITD <- data %>%
group_by(Specie) %>%
summarise_at(vars(ITD), list(mean=mean, sd=sd)) %>%
as.data.frame()
colnames(df_ITD) <-c("SPECIE", "ITD_MEAN", "STD_ITD")
df_MG <- data %>%
group_by(Specie) %>%
summarise_at(vars(dryweight), list(mean=mean, sd=sd)) %>%
as.data.frame()
colnames(df_MG) <-c("SPECIE", "MG_MEAN", "STD_MG")
data_cross <- inner_join(df_ITD, df_MG, by="SPECIE")
Genus<-c(rep("Eufrisea",4), rep("Euglossa",15), rep("Eulaema",3), rep("Exaraete",1))
data_cross<-data_frame(Genus, data_cross$SPECIE, data_cross$ITD_MEAN, data_cross$STD_ITD,
data_cross$MG_MEAN,data_cross$STD_MG)
colnames(data_cross) <-c("Genus","SPECIE", "ITD_MEAN", "STD_ITD", "MG_MEAN", "STD_MG")
library(kableExtra)
DataA<- kable(data_cross, format = "markdown", col.names = c("Genus", "Specie", "ITD_Mean", "ITD_SD", "DryWeight_Mean", "DryWeight_SD"))
DataA
| Eufrisea |
Eufrisea anisochlora |
4.866000 |
0.2023648 |
0.0926000 |
0.0109909 |
| Eufrisea |
Eufrisea machroglossa |
6.015200 |
0.0281549 |
0.1312000 |
0.0214639 |
| Eufrisea |
Eufrisea pulchra |
6.187400 |
0.0623602 |
0.1534000 |
0.0432296 |
| Eufrisea |
Eufrisea surinamensis |
5.729200 |
0.0534154 |
0.1614000 |
0.0244499 |
| Euglossa |
Euglossa asarophora |
4.195200 |
0.1207506 |
0.0570000 |
0.0106536 |
| Euglossa |
Euglossa cognota |
3.980200 |
0.1699109 |
0.0442000 |
0.0106864 |
| Euglossa |
Euglossa cybella |
4.075200 |
0.0492768 |
0.0482000 |
0.0059749 |
| Euglossa |
Euglossa dadsoni |
3.557800 |
0.0493477 |
0.0336000 |
0.0043932 |
| Euglossa |
Euglossa despecta |
3.460400 |
0.3272939 |
0.0256000 |
0.0055946 |
| Euglossa |
Euglossa imperiallis |
4.145400 |
0.0696118 |
0.0676000 |
0.0094499 |
| Euglossa |
Euglossa maculilabris |
3.692000 |
0.1514034 |
0.0363333 |
0.0056862 |
| Euglossa |
Euglossa mixta |
3.731200 |
0.1403627 |
0.0346000 |
0.0051769 |
| Euglossa |
Euglossa purpurea |
3.542000 |
0.0420000 |
0.0423333 |
0.0083865 |
| Euglossa |
Euglossa sapphirrina |
3.043800 |
0.0360375 |
0.0234000 |
0.0060663 |
| Euglossa |
Euglossa tridentata |
3.804600 |
0.1427806 |
0.0410000 |
0.0067454 |
| Euglossa |
Euglossa turbinifex |
3.300667 |
0.0773649 |
0.0280000 |
0.0095394 |
| Euglossa |
Euglossa variabilis |
3.142333 |
0.1014413 |
0.0280000 |
0.0072111 |
| Euglossa |
Euglossa villosa |
3.390200 |
0.2044228 |
0.0328000 |
0.0074967 |
| Euglossa |
Euglossa villosiventris |
3.552333 |
0.0490748 |
0.0436667 |
0.0056862 |
| Eulaema |
Eulaema cirgulata |
6.468000 |
0.0626259 |
0.2124000 |
0.0439010 |
| Eulaema |
Eulaema meriana |
7.733000 |
0.1623946 |
0.4058000 |
0.0306953 |
| Eulaema |
Eulaema nigrita |
5.043800 |
0.0592427 |
0.1552000 |
0.0145671 |
| Exaraete |
Exaerete smragdina |
4.848000 |
0.0959557 |
0.1552000 |
0.0188600 |
Power regression between dry body mass and ITD (mm). AVERAGE
model_power_cross <- nls(ITD_MEAN ~ a * MG_MEAN^b, data = data_cross, start = list(a = 1, b = 1))
model1 <- lm (log (ITD_MEAN) ~ log (MG_MEAN), data = data_cross)
Coefficients
summary(model_power_cross)
##
## Formula: ITD_MEAN ~ a * MG_MEAN^b
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## a 10.18147 0.43194 23.57 < 2e-16 ***
## b 0.31425 0.01711 18.36 2.05e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3227 on 21 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 4.979e-06
Adjusted R-squared and p-value
summary(model1)
##
## Call:
## lm(formula = log(ITD_MEAN) ~ log(MG_MEAN), data = data_cross)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15215 -0.03977 0.01427 0.03821 0.11582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.31006 0.04938 46.78 < 2e-16 ***
## log(MG_MEAN) 0.31097 0.01719 18.09 2.77e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06482 on 21 degrees of freedom
## Multiple R-squared: 0.9397, Adjusted R-squared: 0.9368
## F-statistic: 327.2 on 1 and 21 DF, p-value: 2.768e-14
Graphic
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.1
ggplot(data_cross, aes(x = MG_MEAN, y = ITD_MEAN)) +
geom_smooth(method = "nls", formula = y ~ a * x^b, se = FALSE, color = "blue", method.args = list(start = list(a = 1, b = 1))) +
labs(x = "Dry body mass (g)", y = "Intertegular distance (mm)") +
geom_errorbar(aes(ymin = ITD_MEAN - STD_ITD, ymax = ITD_MEAN + STD_ITD)) +
theme_classic() + geom_point(aes(shape=Genus, color=Genus),size=3)
Power regression between dry body mass and ITD (mm) ALL
INDIVIDUALS
model_potencial <- nls(ITD ~ a * dryweight^b, data = data, start = list(a = 1, b = 1))
model2 <- lm (log (ITD) ~ log (dryweight), data = data)
Coefficients
summary(model_potencial)
##
## Formula: ITD ~ a * dryweight^b
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## a 9.952767 0.227917 43.67 <2e-16 ***
## b 0.303193 0.009388 32.30 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3864 on 103 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.641e-07
Adjusted R-squared and p-value
summary(model2)
##
## Call:
## lm(formula = log(ITD) ~ log(dryweight), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.179061 -0.053562 0.007899 0.050463 0.206432
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.280855 0.026771 85.20 <2e-16 ***
## log(dryweight) 0.297740 0.009425 31.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07903 on 103 degrees of freedom
## Multiple R-squared: 0.9065, Adjusted R-squared: 0.9055
## F-statistic: 998 on 1 and 103 DF, p-value: < 2.2e-16
Graphic
ggplot(data, aes(x = dryweight, y = ITD)) +
geom_smooth(method = "nls", formula = y ~ a * x^b, se = FALSE, color = "blue", method.args = list(start = list(a = 1, b = 1))) +
labs(x = "Dry body mass (g)", y = "Intertegular distance (mm)") +
theme_classic() + geom_point(aes(shape=Genus, color=Genus),size=3)
Power regression between dry body mass and ITD (mm). EUGLOSSA
INDIVIDUALS ONLY
library(readxl)
dataE = read_excel("/Users/yoss/Desktop/ITD_Euglossini.xlsx", sheet= "Euglossa")
model_potencialE <- nls(ITD ~ a * dryweight^b, data = dataE, start = list(a = 1, b = 1))
modelE <- lm (log (ITD) ~ log (dryweight), data = dataE)
Coefficients
summary(model_potencialE)
##
## Formula: ITD ~ a * dryweight^b
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## a 8.04247 0.54069 14.88 <2e-16 ***
## b 0.23955 0.02069 11.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2124 on 63 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 3.775e-08
Adjusted R-squared and p-value
summary(modelE)
##
## Call:
## lm(formula = log(ITD) ~ log(dryweight), data = dataE)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.119818 -0.044036 0.008616 0.037251 0.130009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.08703 0.06922 30.15 <2e-16 ***
## log(dryweight) 0.24076 0.02093 11.50 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.059 on 63 degrees of freedom
## Multiple R-squared: 0.6774, Adjusted R-squared: 0.6723
## F-statistic: 132.3 on 1 and 63 DF, p-value: < 2.2e-16
Graphic
ggplot(dataE, aes(x = dryweight, y = ITD)) +
geom_smooth(method = "nls", formula = y ~ a * x^b, se = FALSE, color = "blue", method.args = list(start = list(a = 1, b = 1))) +
labs(x = "Dry body mass (g)", y = "Intertegular distance (mm)") +
theme_classic() + geom_point(aes(shape=Genus, color=Genus),size=3)
