getwd()## [1] "C:/Users/YSELoaner/Desktop"A<-read.csv("D:/oct2023/All initial data set.csv")##1ST STEP_DATA CLEANING
Format variables name
A2<-clean_names(A)
remove empty rows and column
A3<-remove_empty(A2, which = c (“rows”,“cols”), quiet = FALSE)
Duplicate rows remove
A4<-distinct(A3)
remove Na
A5<-na.omit(A4)
##AVERAGE & CALCULATE SLA, LDMC
A5\(FW <- (A5\)fw1 + A5\(fw2 + A5\)fw3 + A5\(fw4 + A5\)fw5) / 5
dw1,.. & la1,… covert to non numeric
A5\(dw1<-as.numeric(A5\)dw1)
A5\(dw2<-as.numeric(A5\)dw2)
A5\(dw3<-as.numeric(A5\)dw3)
A5\(dw4<-as.numeric(A5\)dw4)
A5\(dw5<-as.numeric(A5\)dw5)
A5\(DW <- (A5\)dw1 + A5\(dw2 + A5\)dw3 + A5\(dw4 + A5\)dw5) / 5
A5\(LT <- (A5\)lt1 + A5\(lt2 + A5\)lt3 + A5\(lt4 + A5\)lt5) / 5
A5\(LA <- (A5\)la1 + A5\(la2 + A5\)la3 + A5\(la4 + A5\)la5) / 5
A5\(la1<-as.numeric(A5\)la1)
A5\(la2<-as.numeric(A5\)la2)
A5\(la3<-as.numeric(A5\)la3)
A5\(la4<-as.numeric(A5\)la4)
A5\(la5<-as.numeric(A5\)la5)
###A5\(SLA <- (A5\)LA / A5$DW )
###A5\(LDMC<-(A5\)DW/A5$FW)
Save data frame A5 as a CSV file
write.csv(A5, file = “D:/oct2023/average.csv”, row.names = FALSE)
select sp,SLA,LT,LA,LDMC
A6<-select(sp,SLA,LA,LT,LDMC)
Remove outliers FROM SLA
remove_outliers <- function(A6, SLA) { Q1 <- quantile(A6[[SLA]], 0.25, na.rm = TRUE) Q3 <- quantile(A6[[SLA]], 0.75, na.rm = TRUE) IQR_value <- Q3 - Q1 lower_bound <- Q1 - 1.5 * IQR_value upper_bound <- Q3 + 1.5 * IQR_value data_no_outliers <- A6[A6[[SLA]] >= lower_bound & A6[[SLA]] <= upper_bound, ] return(data_no_outliers) }
A6_cleaned <- remove_outliers(A6, “SLA”)
LA
remove_outliers <- function(A6, LA) { Q1 <- quantile(A6[[LA]], 0.25, na.rm = TRUE) Q3 <- quantile(A6[[LA]], 0.75, na.rm = TRUE) IQR_value <- Q3 - Q1 lower_bound <- Q1 - 1.5 * IQR_value upper_bound <- Q3 + 1.5 * IQR_value data_no_outliers <- A6[A6[[LA]] >= lower_bound & A6[[LA]] <= upper_bound, ] return(data_no_outliers) }
A6_cleanedLA <- remove_outliers(A6, “LA”)
LT
remove_outliers <- function(A6, LT) { Q1 <- quantile(A6[[LT]], 0.25, na.rm = TRUE) Q3 <- quantile(A6[[LT]], 0.75, na.rm = TRUE) IQR_value <- Q3 - Q1 lower_bound <- Q1 - 1.5 * IQR_value upper_bound <- Q3 + 1.5 * IQR_value data_no_outliers <- A6[A6[[LT]] >= lower_bound & A6[[LT]] <= upper_bound, ] return(data_no_outliers) }
A6_cleanedLT <- remove_outliers(A6, “LT”)
LDMC
remove_outliers <- function(A6, LDMC) { Q1 <- quantile(A6[[LDMC]], 0.25, na.rm = TRUE) Q3 <- quantile(A6[[LDMC]], 0.75, na.rm = TRUE) IQR_value <- Q3 - Q1 lower_bound <- Q1 - 1.5 * IQR_value upper_bound <- Q3 + 1.5 * IQR_value data_no_outliers <- A6[A6[[LDMC]] >= lower_bound & A6[[LDMC]] <= upper_bound, ] return(data_no_outliers) }
A6_cleanedLDMC <- remove_outliers(A6, “LDMC”)
Save file Each files each column outliers are removed
write.csv(A6_cleaned, file = “D:/oct2023/Cleane_sla.csv”, row.names = FALSE) write.csv(A6_cleanedLA, file = “D:/oct2023/Cleane_la.csv”, row.names = FALSE) write.csv(A6_cleanedLT, file = “D:/oct2023/Cleane_lt.csv”, row.names = FALSE) write.csv(A6_cleanedLDMC, file = “D:/oct2023/Cleane_ldmc.csv”, row.names = FALSE)
According outliers removin data (range of each traits)
SLA = 32.09648-241.3107 LA = 8.5328-183.8428 LT = 0.0844-0.3372 LDMC=0.15247-0.643027
A6_cleanedSLA<-read.csv("D:/oct2023/Cleane_sla.csv")
A6_cleanedLA<-read.csv("D:/oct2023/Cleane_la.csv")
A6_cleanedLT<-read.csv("D:/oct2023/Cleane_lt.csv")
A6_cleanedLDMC<-read.csv("D:/oct2023/Cleane_ldmc.csv")
A6<-read.csv("D:/oct2023/clean_average.csv")Descriptive statistics(all data)
summary(A6$SLA)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.4631 109.7685 140.5328 150.8865 164.0677 2117.9079 8summary(A6$LA)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 8.533 29.588 52.746 74.838 91.564 6074.160 7summary(A6$LT)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0844 0.1696 0.1990 0.2778 0.2368 35.9388summary(A6$LDMC)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00237 0.31850 0.39540 0.59953 0.45054 82.05165 1boxplot(A6$SLA, main="Boxplot of SLA", xlab="SLA Values", horizontal=TRUE)
boxplot(A6$LA, main="Boxplot of LA", xlab="LA Values", horizontal=TRUE)
boxplot(A6$LT, main="Boxplot of LT", xlab="LT Values", horizontal=TRUE)
boxplot(A6$LDMC, main="Boxplot of LDMC", xlab="LDMC Values", horizontal=TRUE)
Descriptive statistics(after removing outliers)
summary(A6_cleanedSLA$SLA)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 32.1 110.1 139.0 137.0 160.9 241.3 8boxplot(A6_cleanedSLA$SLA, main="Boxplot of SLA", xlab="SLA Values", horizontal=TRUE)
LEAF SIZE
summary(A6_cleanedLA$LA)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 8.533 28.111 49.607 59.783 83.345 183.843 7boxplot(A6_cleanedLA$LA, main="Boxplot of LA", xlab="LA Values", horizontal=TRUE)
summary(A6_cleanedLT$LT)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0844 0.1665 0.1924 0.2003 0.2243 0.3372boxplot(A6_cleanedLT$LT, main="Boxplot of LT", xlab="LT Values", horizontal=TRUE)
summary(A6_cleanedLDMC$LDMC)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.1524 0.3185 0.3934 0.3873 0.4471 0.6430 1boxplot(A6_cleanedLDMC$LDMC, main="Boxplot of LDMC", xlab="LDMC Values", horizontal=TRUE)
result <- tapply(A6_cleanedSLA$SLA,A6_cleanedSLA$sp, mean)
print(result)## ANISCI BHESCE CALOTH CRYPWI CULLCE CULLRO DIOSAC DIOSHI
## 153.59860 174.94582 125.74018 99.35112 150.35628 106.16789 158.43355 111.98432
## GARCHE HOPEJU HUMBLA HYDNOC LITSLO MANGZE MASTTE MESUFE
## 99.27585 140.27534 145.20874 125.65261 116.87689 124.62553 159.15809 102.80868
## MESUNA MYRIDA PALACA PALAPE PALATH SEMEGA SEMEWA SHORAF
## 148.79173 116.39390 135.13369 167.83573 154.35482 82.89166 106.07374 155.74026
## SHORCN SHORCR SHORDI SHORME SHORST SHORTR SHORWO SYZYNE
## 141.28650 140.42032 115.19562 97.97234 121.03768 160.17615 139.28058 127.58933
## SYZYSP SYZYWI XYLOCH
## 84.11494 186.29410 191.65213std_dev <- aggregate(SLA ~ sp, data =A6_cleanedSLA , FUN = sd)
# Calculate standard error for each species (standard deviation divided by the square root of the number of observations)
std_error <- std_dev$SLA/ sqrt(table(A6_cleanedSLA$sp))
# Calculate coefficient of variation (CV) for each species (standard deviation divided by the mean)
cv <- std_dev$SLA / aggregate(SLA ~ sp, data = A6_cleanedSLA, FUN = mean)$SLA
# Calculate the mean for each species
mean_value <- aggregate(SLA ~ sp, data = A6_cleanedSLA, FUN = mean)
# Create a new data frame with the results
resultSLA <- data.frame(
Species = std_dev$sp,
MeanValue = mean_value$SLA,
StandardDeviation = std_dev$SLA,
StandardError = std_error,
CoefficientOfVariation = cv
)
print(resultSLA)## Species MeanValue StandardDeviation StandardError.Var1 StandardError.Freq
## 1 ANISCI 153.59860 27.482637 ANISCI 3.926091
## 2 BHESCE 174.94582 39.558860 BHESCE 8.074919
## 3 CALOTH 125.74018 15.089686 CALOTH 3.772421
## 4 CRYPWI 99.35112 8.443645 CRYPWI 2.047885
## 5 CULLCE 150.35628 23.399608 CULLCE 3.046370
## 6 CULLRO 106.16789 15.135455 CULLRO 2.308135
## 7 DIOSAC 158.43355 17.066824 DIOSAC 3.483751
## 8 DIOSHI 111.98432 36.714487 DIOSHI 10.182767
## 9 GARCHE 99.27585 14.015354 GARCHE 1.856379
## 10 HOPEJU 140.27534 23.965925 HOPEJU 5.991481
## 11 HUMBLA 145.20874 18.146482 HUMBLA 3.558818
## 12 HYDNOC 125.65261 27.068876 HYDNOC 7.507555
## 13 LITSLO 116.87689 21.502891 LITSLO 5.963829
## 14 MANGZE 124.62553 19.573682 MANGZE 5.053903
## 15 MASTTE 159.15809 14.315162 MASTTE 3.471937
## 16 MESUFE 102.80868 25.011195 MESUFE 3.573028
## 17 MESUNA 148.79173 29.206752 MESUNA 3.974536
## 18 MYRIDA 116.39390 25.837053 MYRIDA 3.986744
## 19 PALACA 135.13369 22.939893 PALACA 4.120126
## 20 PALAPE 167.83573 19.856668 PALAPE 2.630080
## 21 PALATH 154.35482 15.272468 PALATH 2.699816
## 22 SEMEGA 82.89166 53.809780 SEMEGA 14.381269
## 23 SEMEWA 106.07374 20.011810 SEMEWA 4.716829
## 24 SHORAF 155.74026 29.119255 SHORAF 4.340842
## 25 SHORCN 141.28650 32.686447 SHORCN 10.336362
## 26 SHORCR 140.42032 34.185926 SHORCR 5.096137
## 27 SHORDI 115.19562 27.438399 SHORDI 4.510845
## 28 SHORME 97.97234 10.186015 SHORME 2.940449
## 29 SHORST 121.03768 44.979135 SHORST 6.560881
## 30 SHORTR 160.17615 10.647943 SHORTR 1.912427
## 31 SHORWO 139.28058 26.051446 SHORWO 4.678978
## 32 SYZYNE 127.58933 28.223334 SYZYNE 7.543003
## 33 SYZYSP 84.11494 12.308182 SYZYSP 4.652056
## 34 SYZYWI 186.29410 31.901924 SYZYWI 14.266974
## 35 XYLOCH 191.65213 26.524108 XYLOCH 3.576510
## CoefficientOfVariation
## 1 0.17892505
## 2 0.22612063
## 3 0.12000687
## 4 0.08498792
## 5 0.15562774
## 6 0.14256151
## 7 0.10772229
## 8 0.32785381
## 9 0.14117587
## 10 0.17084917
## 11 0.12496825
## 12 0.21542629
## 13 0.18397898
## 14 0.15705997
## 15 0.08994304
## 16 0.24327902
## 17 0.19629284
## 18 0.22197945
## 19 0.16975702
## 20 0.11831013
## 21 0.09894391
## 22 0.64915791
## 23 0.18865941
## 24 0.18697320
## 25 0.23134870
## 26 0.24345426
## 27 0.23818960
## 28 0.10396828
## 29 0.37161266
## 30 0.06647646
## 31 0.18704291
## 32 0.22120450
## 33 0.14632575
## 34 0.17124495
## 35 0.13839715save in csv file
write.csv(resultSLA, file = "D:/oct2023/decritive SLA1.csv", row.names = FALSE)LT
std_dev <- aggregate(LA ~ sp, data =A6_cleanedLA , FUN = sd)
std_error <- std_dev$LA/ sqrt(table(A6_cleanedLA$sp))
cv <- std_dev$LA / aggregate(LA ~ sp, data = A6_cleanedLA, FUN = mean)$LA
mean_value <- aggregate(LA ~ sp, data = A6_cleanedLA, FUN = mean)
resultLA <- data.frame(
Species = std_dev$sp,
MeanValue = mean_value$LA,
StandardDeviation = std_dev$LA,
StandardError = std_error,
CoefficientOfVariation = cv
)
print(resultLA)## Species MeanValue StandardDeviation StandardError.Var1 StandardError.Freq
## 1 ANISCI 45.60805 19.078928 ANISCI 2.6715843
## 2 BHESCE 82.05314 25.339979 BHESCE 5.0679957
## 3 CALOTH 32.17164 6.446855 CALOTH 1.6117136
## 4 CRYPWI 90.50178 33.902422 CRYPWI 8.2225451
## 5 CULLCE 37.87183 8.678791 CULLCE 1.1204271
## 6 CULLRO 104.77849 31.421012 CULLRO 4.6839679
## 7 DIOSAC 18.64885 3.682601 DIOSAC 0.7517077
## 8 DIOSHI 41.96996 19.171340 DIOSHI 5.1237561
## 9 GARCHE 72.36371 20.918866 GARCHE 2.7467817
## 10 HOPEJU 25.14734 7.333223 HOPEJU 1.8333056
## 11 HUMBLA 165.87253 12.029440 HUMBLA 4.9109984
## 12 HYDNOC 38.25049 9.103077 HYDNOC 2.5247393
## 13 LITSLO 121.43935 22.479064 LITSLO 6.2345707
## 14 MANGZE 118.40377 31.189494 MANGZE 8.3357428
## 15 MASTTE 40.92779 5.998414 MASTTE 1.4548291
## 16 MESUFE 111.56748 36.347565 MESUFE 5.0896771
## 17 MESUNA 46.12961 15.077814 MESUNA 2.0330920
## 18 MYRIDA 92.81768 24.096653 MYRIDA 3.7181942
## 19 PALACA 72.90794 30.616966 PALACA 5.3297298
## 20 PALAPE 64.04355 20.643954 PALAPE 2.6876140
## 21 PALATH 16.72893 4.367596 PALATH 0.7720892
## 22 SEMEGA 72.48893 32.991399 SEMEGA 9.5237965
## 23 SEMEWA 140.44895 31.470821 SEMEWA 8.7284352
## 24 SHORAF 25.08289 19.001870 SHORAF 2.6101076
## 25 SHORCN 50.83472 28.845619 SHORCN 8.3270131
## 26 SHORCR 60.30833 32.789833 SHORCR 4.4621311
## 27 SHORDI 77.16232 23.958398 SHORDI 3.7881553
## 28 SHORME 64.70923 38.706647 SHORME 14.6297376
## 29 SHORST 74.03442 30.209984 SHORST 4.1496604
## 30 SHORTR 21.33768 2.995442 SHORTR 0.5295244
## 31 SHORWO 29.41034 5.437338 SHORWO 0.9765748
## 32 SYZYNE 40.10060 10.528076 SYZYNE 2.7183376
## 33 SYZYSP 158.55580 NA SYZYSP NA
## 34 SYZYWI 65.15248 33.682529 SYZYWI 10.6513510
## 35 XYLOCH 20.82333 4.815218 XYLOCH 0.6322691
## CoefficientOfVariation
## 1 0.41832369
## 2 0.30882398
## 3 0.20038938
## 4 0.37460504
## 5 0.22916219
## 6 0.29988037
## 7 0.19747065
## 8 0.45678721
## 9 0.28907952
## 10 0.29161030
## 11 0.07252219
## 12 0.23798587
## 13 0.18510527
## 14 0.26341639
## 15 0.14656091
## 16 0.32578997
## 17 0.32685757
## 18 0.25961276
## 19 0.41994009
## 20 0.32234247
## 21 0.26108050
## 22 0.45512325
## 23 0.22407302
## 24 0.75756290
## 25 0.56743937
## 26 0.54370319
## 27 0.31049351
## 28 0.59816271
## 29 0.40805323
## 30 0.14038279
## 31 0.18487849
## 32 0.26254162
## 33 NA
## 34 0.51698000
## 35 0.23124143write.csv(resultLA, file = "D:/oct2023/decritive LA.csv", row.names = FALSE)std_dev <- aggregate(LT ~ sp, data =A6_cleanedLT , FUN = sd)
std_error <- std_dev$LT/ sqrt(table(A6_cleanedLT$sp))
cv <- std_dev$LT / aggregate(LT ~ sp, data = A6_cleanedLT, FUN = mean)$LT
mean_value <- aggregate(LT ~ sp, data = A6_cleanedLT, FUN = mean)
resultLT <- data.frame(
Species = std_dev$sp,
MeanValue = mean_value$LT,
StandardDeviation = std_dev$LT,
StandardError = std_error,
CoefficientOfVariation = cv
)
print(resultLT)## Species MeanValue StandardDeviation StandardError.Var1 StandardError.Freq
## 1 ANISCI 0.1621192 0.02044007 ANISCI 0.002834528
## 2 BHESCE 0.1871231 0.02898734 BHESCE 0.005684885
## 3 CALOTH 0.2527375 0.02679281 CALOTH 0.006698202
## 4 CRYPWI 0.2274444 0.02470509 CRYPWI 0.005823045
## 5 CULLCE 0.1550767 0.01198152 CULLCE 0.001546807
## 6 CULLRO 0.2148273 0.01261122 CULLRO 0.001901213
## 7 DIOSAC 0.1469333 0.04435182 DIOSAC 0.009053278
## 8 DIOSHI 0.2122143 0.02104946 DIOSHI 0.005625705
## 9 GARCHE 0.3322000 NA GARCHE NA
## 10 HOPEJU 0.1443500 0.02347134 HOPEJU 0.005867836
## 11 HUMBLA 0.1629724 0.02162569 HUMBLA 0.004015791
## 12 HYDNOC 0.2502154 0.02789617 HYDNOC 0.007737006
## 13 LITSLO 0.2586714 0.03388711 LITSLO 0.009056710
## 14 MANGZE 0.1750800 0.01302060 MANGZE 0.003361904
## 15 MASTTE 0.1935059 0.01645784 MASTTE 0.003991613
## 16 MESUFE 0.2143654 0.02047613 MESUFE 0.002839528
## 17 MESUNA 0.1859491 0.01554655 MESUNA 0.002096296
## 18 MYRIDA 0.2816524 0.02193952 MYRIDA 0.003385341
## 19 PALACA 0.3008000 0.02753051 PALACA 0.005298250
## 20 PALAPE 0.2109051 0.02058223 PALAPE 0.002679578
## 21 PALATH 0.2976600 0.01483046 PALATH 0.002707659
## 22 SEMEGA 0.3153200 0.01883075 SEMEGA 0.005954808
## 23 SEMEWA 0.3051818 0.02289916 SEMEWA 0.006904357
## 24 SHORAF 0.1524436 0.02353550 SHORAF 0.003173526
## 25 SHORCN 0.1424333 0.01229185 SHORCN 0.003548353
## 26 SHORCR 0.1633236 0.02510533 SHORCR 0.003385202
## 27 SHORDI 0.2202900 0.02041382 SHORDI 0.003227708
## 28 SHORME 0.1959474 0.01416205 SHORME 0.003248998
## 29 SHORST 0.1986909 0.02598572 SHORST 0.003503913
## 30 SHORTR 0.1302187 0.01600108 SHORTR 0.002828618
## 31 SHORWO 0.2080000 0.01655544 SHORWO 0.002973445
## 32 SYZYNE 0.1939333 0.03221186 SYZYNE 0.008317066
## 33 SYZYSP 0.3165000 0.02340399 SYZYSP 0.011701994
## 34 SYZYWI 0.1779400 0.02094162 SYZYWI 0.006622323
## 35 XYLOCH 0.1955759 0.02243494 XYLOCH 0.002945852
## CoefficientOfVariation
## 1 0.12608048
## 2 0.15491055
## 3 0.10601041
## 4 0.10862032
## 5 0.07726189
## 6 0.05870402
## 7 0.30184996
## 8 0.09918965
## 9 NA
## 10 0.16260024
## 11 0.13269543
## 12 0.11148864
## 13 0.13100445
## 14 0.07436943
## 15 0.08505087
## 16 0.09551974
## 17 0.08360647
## 18 0.07789574
## 19 0.09152432
## 20 0.09759000
## 21 0.04982348
## 22 0.05971951
## 23 0.07503449
## 24 0.15438818
## 25 0.08629900
## 26 0.15371525
## 27 0.09266793
## 28 0.07227478
## 29 0.13078463
## 30 0.12287844
## 31 0.07959347
## 32 0.16609758
## 33 0.07394625
## 34 0.11768924
## 35 0.11471220write.csv(resultLT, file = "D:/oct2023/decritive LT.csv", row.names = FALSE)std_dev <- aggregate(LDMC ~ sp, data =A6_cleanedLDMC , FUN = sd)
std_error <- std_dev$LDMC/ sqrt(table(A6_cleanedLDMC$sp))
cv <- std_dev$LDMC / aggregate(LDMC ~ sp, data = A6_cleanedLDMC, FUN = mean)$LDMC
mean_value <- aggregate(LDMC ~ sp, data = A6_cleanedLDMC, FUN = mean)
resultLDMC <- data.frame(
Species = std_dev$sp,
MeanValue = mean_value$LDMC,
StandardDeviation = std_dev$LDMC,
StandardError = std_error,
CoefficientOfVariation = cv
)
print(resultLDMC)## Species MeanValue StandardDeviation StandardError.Var1 StandardError.Freq
## 1 ANISCI 0.4197033 0.04372431 ANISCI 0.006122629
## 2 BHESCE 0.3174149 0.09224440 BHESCE 0.018448880
## 3 CALOTH 0.3567781 0.02105615 CALOTH 0.005264037
## 4 CRYPWI 0.4313181 0.02246142 CRYPWI 0.005294208
## 5 CULLCE 0.4315855 0.05168562 CULLCE 0.006845927
## 6 CULLRO 0.4506685 0.05190740 CULLRO 0.007825335
## 7 DIOSAC 0.5029345 0.05187852 DIOSAC 0.010589659
## 8 DIOSHI 0.4172761 0.05910233 DIOSHI 0.016392036
## 9 GARCHE 0.2772784 0.03940161 GARCHE 0.005218871
## 10 HOPEJU 0.4694373 0.03509664 HOPEJU 0.008774161
## 11 HUMBLA 0.3841742 0.04784889 HUMBLA 0.009208524
## 12 HYDNOC 0.3233385 0.07192716 HYDNOC 0.019949004
## 13 LITSLO 0.3310386 0.02255877 LITSLO 0.006029085
## 14 MANGZE 0.3974500 0.02836821 MANGZE 0.007581724
## 15 MASTTE 0.3441357 0.04421103 MASTTE 0.010722751
## 16 MESUFE 0.5180143 0.03200482 MESUFE 0.004526165
## 17 MESUNA 0.4804401 0.04468635 MESUNA 0.006196882
## 18 MYRIDA 0.2884896 0.02685642 MYRIDA 0.004095566
## 19 PALACA 0.3032367 0.02657776 PALACA 0.004698329
## 20 PALAPE 0.3226024 0.02931795 PALAPE 0.003816872
## 21 PALATH 0.2889841 0.02071210 PALATH 0.003661417
## 22 SEMEGA 0.3112090 0.02417514 SEMEGA 0.006461078
## 23 SEMEWA 0.2986692 0.02602638 SEMEWA 0.005679422
## 24 SHORAF 0.4441892 0.06456664 SHORAF 0.008953781
## 25 SHORCN 0.4589071 0.03960436 SHORCN 0.011432793
## 26 SHORCR 0.4346391 0.02731314 SHORCR 0.003682903
## 27 SHORDI 0.4023488 0.02936029 SHORDI 0.004642269
## 28 SHORME 0.4064000 0.02526937 SHORME 0.005650403
## 29 SHORST 0.3864879 0.08209182 SHORST 0.011609537
## 30 SHORTR 0.4109065 0.02284915 SHORTR 0.004103828
## 31 SHORWO 0.3824048 0.06269791 SHORWO 0.011260877
## 32 SYZYNE 0.3607906 0.04166278 SYZYNE 0.010757284
## 33 SYZYSP 0.3156368 0.02241511 SYZYSP 0.008472113
## 34 SYZYWI 0.3595276 0.02947519 SYZYWI 0.009320873
## 35 XYLOCH 0.3354407 0.03454167 XYLOCH 0.004700526
## CoefficientOfVariation
## 1 0.10417911
## 2 0.29061147
## 3 0.05901749
## 4 0.05207624
## 5 0.11975755
## 6 0.11517868
## 7 0.10315165
## 8 0.14163843
## 9 0.14210128
## 10 0.07476322
## 11 0.12454999
## 12 0.22245157
## 13 0.06814543
## 14 0.07137554
## 15 0.12846975
## 16 0.06178366
## 17 0.09301129
## 18 0.09309318
## 19 0.08764692
## 20 0.09087952
## 21 0.07167213
## 22 0.07768136
## 23 0.08714117
## 24 0.14535841
## 25 0.08630147
## 26 0.06284096
## 27 0.07297222
## 28 0.06217857
## 29 0.21240463
## 30 0.05560670
## 31 0.16395690
## 32 0.11547634
## 33 0.07101549
## 34 0.08198311
## 35 0.10297400write.csv(resultLDMC, file = "D:/oct2023/decritive LT.csv", row.names = FALSE)