| Title: | Analysis of Design of Experiments for Biological Research |
|---|---|
| Description: | Performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples. |
| Authors: | Raj Popat [aut, cre], Kanthesh Banakara [aut] |
| Maintainer: | Raj Popat <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-11-20 03:19:02 UTC |
| Source: | https://github.com/cran/doebioresearch |
Re-transform the arc sine transformed data. When arc sine transformation is done, the mean of the treatments needs to be re-transformed for comparison.
arcsineretransform(mean.vector, type)arcsineretransform(mean.vector, type)
mean.vector |
vector of mean which needs to be re-transformed |
type |
0 if data was in proportion prior to re-transformation, 1 if data was in percentage prior to re-transformation |
Arc sine re-transformed vector
data<-c(60,63.43495,71.56505,78.46304) #If data was in percentage prior to re-transformation arcsineretransform(data,1) #If data was in proportion prior to re-transformation arcsineretransform(data,0)data<-c(60,63.43495,71.56505,78.46304) #If data was in percentage prior to re-transformation arcsineretransform(data,1) #If data was in proportion prior to re-transformation arcsineretransform(data,0)
The function divide values by 100, does square root and than sin inverse of each values of vector. If any of the values of a vector is 0 or 100, it is replaced by 1/4n or 100-(1/4n), respectively.
arcsinetransform(numeric.vector, type, n)arcsinetransform(numeric.vector, type, n)
numeric.vector |
data vector to be transformed |
type |
0 if data is in percentage and 1 if data is in proportion |
n |
is the number of units upon which the percentage/proportion data is based |
Arc sine transformed data
vector<-c(23,0,29.6,35.6,33,35.6,10.5,100) # Arc sine trnasformation for percentage data and n=10 arcsinetransform(vector,0,10)vector<-c(23,0,29.6,35.6,33,35.6,10.5,100) # Arc sine trnasformation for percentage data and n=10 arcsinetransform(vector,0,10)
Convert the data frame into list of numeric nature
convert(data1)convert(data1)
data1 |
data-frame to be converted into list |
list of numeric vectors
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means
crd(data, trt.vector, MultipleComparisonTest)crd(data, trt.vector, MultipleComparisonTest)
data |
dependent variables |
trt.vector |
vector containing treatments |
MultipleComparisonTest |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result
data<-data.frame(Treatments=c("T1","T2","T3","T4","T5","T6","T7","T1","T2","T3","T4","T5","T6", "T7","T1","T2","T3","T4","T5","T6","T7"), yield=c(25,21,21,18,25,28,24,25,24,24,16,21,20,17,16,19,14,15,13,11,25), height=c(130,120,125,135,139,140,145,136,129,135,150,152,140,148,130,135,145,160,145,130,160)) #CRD analysis with LSD test for yield only crd(data[2],data$Treatments,1) #CRD analysis with LSD test for both yield and height crd(data[2:3],data$Treatments,1)data<-data.frame(Treatments=c("T1","T2","T3","T4","T5","T6","T7","T1","T2","T3","T4","T5","T6", "T7","T1","T2","T3","T4","T5","T6","T7"), yield=c(25,21,21,18,25,28,24,25,24,24,16,21,20,17,16,19,14,15,13,11,25), height=c(130,120,125,135,139,140,145,136,129,135,150,152,140,148,130,135,145,160,145,130,160)) #CRD analysis with LSD test for yield only crd(data[2],data$Treatments,1) #CRD analysis with LSD test for both yield and height crd(data[2:3],data$Treatments,1)
The data consists of three factors nitrogen, phosphorus and Potassium, replication and two dependent variables yield and plant height. The data is generated manually.
factorialdatafactorialdata
The data has 6 columns and 36 rows
Consist sequence of two nitrogen levels n0 and n1
Consist sequence of two phosphorus levels p0 and p1
Consist sequence of two potassium levels k0 and k1
Contains replication which has three levels
Yield as dependent variable
Plant height as dependent variable
The function gives ANOVA, R-square of the model, Normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means
fcrd2fact(data, fact.A, fact.B, Multiple.comparison.test)fcrd2fact(data, fact.A, fact.B, Multiple.comparison.test)
data |
dependent variables |
fact.A |
vector containing levels of first factor |
fact.B |
vector containing levels of second factor |
Multiple.comparison.test |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result for both the factors as well as interaction.
data(factorialdata) #Analysis of Factorial Completely Randomized design along with Dunccan test for Yield only fcrd2fact(factorialdata[5],factorialdata$Nitrogen,factorialdata$Phosphorus,2) #Analysis of Factorial Completely Randomized design along with Dunccan test for Yield & Plant Height fcrd2fact(factorialdata[5:6],factorialdata$Nitrogen,factorialdata$Phosphorus,2)data(factorialdata) #Analysis of Factorial Completely Randomized design along with Dunccan test for Yield only fcrd2fact(factorialdata[5],factorialdata$Nitrogen,factorialdata$Phosphorus,2) #Analysis of Factorial Completely Randomized design along with Dunccan test for Yield & Plant Height fcrd2fact(factorialdata[5:6],factorialdata$Nitrogen,factorialdata$Phosphorus,2)
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
fcrd3fact(data, fact.A, fact.B, fact.C, Multiple.comparison.test)fcrd3fact(data, fact.A, fact.B, fact.C, Multiple.comparison.test)
data |
dependent variables |
fact.A |
vector containing levels of first factor |
fact.B |
vector containing levels of second factor |
fact.C |
vector containing levels of third factor |
Multiple.comparison.test |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result for both the factors as well as interaction.
data(factorialdata) #FCRD analysis along with dunccan test for two dependent var. fcrd3fact(factorialdata[5:6],factorialdata$Nitrogen, factorialdata$Phosphorus,factorialdata$Potassium,2)data(factorialdata) #FCRD analysis along with dunccan test for two dependent var. fcrd3fact(factorialdata[5:6],factorialdata$Nitrogen, factorialdata$Phosphorus,factorialdata$Potassium,2)
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
frbd2fact(data, replicationvector, fact.A, fact.B, Multiple.comparison.test)frbd2fact(data, replicationvector, fact.A, fact.B, Multiple.comparison.test)
data |
dependent variables |
replicationvector |
vector containing replications |
fact.A |
vector containing levels of first factor |
fact.B |
vector containing levels of second factor |
Multiple.comparison.test |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test results for both the factors as well as interaction.
data(factorialdata) #FRBD analysis along with dunccan test for two dependent var. frbd2fact(factorialdata[5:6],factorialdata$Replication, factorialdata$Nitrogen,factorialdata$Phosphorus,2)data(factorialdata) #FRBD analysis along with dunccan test for two dependent var. frbd2fact(factorialdata[5:6],factorialdata$Replication, factorialdata$Nitrogen,factorialdata$Phosphorus,2)
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
frbd3fact( data, replicationvector, fact.A, fact.B, fact.C, Multiple.comparison.test )frbd3fact( data, replicationvector, fact.A, fact.B, fact.C, Multiple.comparison.test )
data |
dependent variables |
replicationvector |
vector containing replications |
fact.A |
vector containing levels of first factor |
fact.B |
vector containing levels of second factor |
fact.C |
vector containing levels of third factor |
Multiple.comparison.test |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result for the factors as well as the interaction.
data(factorialdata) #FRBD analysis along with dunccan test for two dependent var. frbd3fact(factorialdata[5:6],factorialdata$Replication,factorialdata$Nitrogen, factorialdata$Phosphorus,factorialdata$Potassium,2)data(factorialdata) #FRBD analysis along with dunccan test for two dependent var. frbd3fact(factorialdata[5:6],factorialdata$Replication,factorialdata$Nitrogen, factorialdata$Phosphorus,factorialdata$Potassium,2)
Re-transform the log transformed data. When log transformation is done, the mean of the treatments needs to be re-transformed for comparison.
logretransform(transformed.mean, if.zero.present)logretransform(transformed.mean, if.zero.present)
transformed.mean |
vector of mean which needs to be re-transformed |
if.zero.present |
0 if zero was present in the data prior to transformation of data. 1 if zero was absent in the data prior to transformation |
Log re-transformed values
vector<-c(0,2.004,1.114,1.491,1.431,1.415,1.845) #Re-transformation of data with zero present in data prior to transformation logretransform(vector,0)vector<-c(0,2.004,1.114,1.491,1.431,1.415,1.845) #Re-transformation of data with zero present in data prior to transformation logretransform(vector,0)
The function carries out log with base 10 transformation of each values of vector. If one of values of a vector is 0, 1 is added to each observation. Log transformation is carried out for the data when variance is proportional to square of the mean and treatment effects are multiplicative in nature.
logtransform(numeric.vector)logtransform(numeric.vector)
numeric.vector |
data vector to be transformed |
A list of
Ratio- A ratio of maximum and minimum values of the data
LogTransformedVector - A vector of the transformed data
Comment - A comment about zero being present in data or not
vector<-c(100,0,120,1000,52,30,60) logtransform(vector)vector<-c(100,0,120,1000,52,30,60) logtransform(vector)
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
lsd(data, treatmentvector, row, column, MultipleComparisonTest)lsd(data, treatmentvector, row, column, MultipleComparisonTest)
data |
dependent variables |
treatmentvector |
vector containing treatments |
row |
vector for rows |
column |
vector for columns |
MultipleComparisonTest |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result
data(lsddata) #LSD analysis with LSD test for Yield only lsd(lsddata[4],lsddata$Treatment,lsddata$Row,lsddata$Column,1) #LSD analysis with LSD test for Yield and Plant Height lsd(lsddata[4:5],lsddata$Treatment,lsddata$Row,lsddata$Column,1)data(lsddata) #LSD analysis with LSD test for Yield only lsd(lsddata[4],lsddata$Treatment,lsddata$Row,lsddata$Column,1) #LSD analysis with LSD test for Yield and Plant Height lsd(lsddata[4:5],lsddata$Treatment,lsddata$Row,lsddata$Column,1)
The data consists of Rows, Columns, Treatments and two dependent variables Yield and Plant Height. The data is generated manually.
lsddatalsddata
The data has 5 columns and 25 rows
Consist sequence of rows. Row consists of 5 levels
Consist sequence of column. Column consists of 5 levels
Consist sequence of treatments. There are 5 treatments A, B, C, D & E
Yield as dependent variable
Plant height as dependent variable
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
rcbd(data, treatmentvector, replicationvector, MultipleComparisonTest)rcbd(data, treatmentvector, replicationvector, MultipleComparisonTest)
data |
dependent variables |
treatmentvector |
vector containing treatments |
replicationvector |
vector containing replications |
MultipleComparisonTest |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result
data<-data.frame(GFY=c(16,13,14,16,16,17,16,17,16,16,17,16,15,15,15,13,15,14, 16,14,15,14,15,17,18,15,15,15,14,14,14,14,15,15,13,15,14,14,13,13,13,12,15,12,15), DMY=c(5,5,6,5,6,7,6,8,6,9,8,7,5,5,5,4,6,5,8,5,5,5,4,6,6,5,5,6,6,6,5,5,5,5,5,6,5,5,5,4,5,4,5,5,5), Rep=rep(c("R1","R2","R3"),each=15), Trt=rep(c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12","T13","T14","T15"),3)) #' #RCBD analysis with duncan test for GFY only rcbd(data[1],data$Trt,data$Rep,2) #RCBD analysis with duncan test for both GFY and DMY rcbd(data[1:2],data$Trt,data$Rep,2)data<-data.frame(GFY=c(16,13,14,16,16,17,16,17,16,16,17,16,15,15,15,13,15,14, 16,14,15,14,15,17,18,15,15,15,14,14,14,14,15,15,13,15,14,14,13,13,13,12,15,12,15), DMY=c(5,5,6,5,6,7,6,8,6,9,8,7,5,5,5,4,6,5,8,5,5,5,4,6,6,5,5,6,6,6,5,5,5,5,5,6,5,5,5,4,5,4,5,5,5), Rep=rep(c("R1","R2","R3"),each=15), Trt=rep(c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12","T13","T14","T15"),3)) #' #RCBD analysis with duncan test for GFY only rcbd(data[1],data$Trt,data$Rep,2) #RCBD analysis with duncan test for both GFY and DMY rcbd(data[1:2],data$Trt,data$Rep,2)
The data consists of replication, date of sowing (as main-plot), varieties (as sub-plot) and two dependent variables yield and plant height. The data is generated manually.
splitdatasplitdata
The data has 5 columns and 36 rows
Consist sequence of replications. Replications consists of 3 levels
Consist sequence of levels of date of sowing as Main-plot. Date of sowing consists of 2 levels
Consist sequence of levels of varities as Sub-plot. Varities consist of 6 levels
Yield as dependent variable
Plant height as dependent variable
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
splitplot(data, block, main.plot, sub.plot, mean.comparison.test)splitplot(data, block, main.plot, sub.plot, mean.comparison.test)
data |
dependent variables |
block |
vector containing replications |
main.plot |
vector containing main-plot levels |
sub.plot |
vector containing sub-plot levels |
mean.comparison.test |
0 for no test, 1 for LSD test, 2 for Dunccan test and 3 for HSD test |
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result
data(splitdata) #Using Date of sowing as Main-plot factor and varieties as sub-plot factor and using LSD test #Split plot analysis with LSD test for Yield splitplot(splitdata[4],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1) #Split plot analysis with LSD test for both Yield and Plant Height splitplot(splitdata[4:5],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)data(splitdata) #Using Date of sowing as Main-plot factor and varieties as sub-plot factor and using LSD test #Split plot analysis with LSD test for Yield splitplot(splitdata[4],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1) #Split plot analysis with LSD test for both Yield and Plant Height splitplot(splitdata[4:5],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)
Retransform the square root transformed data. When square root transformation is done, the mean of the treatments needs to be re-transformed for comparison.
sqrtretransform(transformed.mean, if.zero.present)sqrtretransform(transformed.mean, if.zero.present)
transformed.mean |
vector of mean which needs to be re-transformed |
if.zero.present |
0 if zero was present in the data prior to transformation of data. 1 if zero was absent in the data prior to transformation |
Square root re-transformed vector
vector<-c(19,10,30,60,50,10,5) #Square root re-transform and zero was absent in the data prior to transformation sqrtretransform(vector,1)vector<-c(19,10,30,60,50,10,5) #Square root re-transform and zero was absent in the data prior to transformation sqrtretransform(vector,1)
The function carries out square root transformation of each values of vector. If one of values of a vector is 0, 0.5 is added to each observation.
sqrttransform(numeric.vector)sqrttransform(numeric.vector)
numeric.vector |
data vector to be transformed |
Square root transformed data
vector<-c(0,25,36,6,9,25,70) sqrttransform(vector)vector<-c(0,25,36,6,9,25,70) sqrttransform(vector)
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means
stripplot(data, block, column, row, mean.comparison.test)stripplot(data, block, column, row, mean.comparison.test)
data |
dependent variables |
block |
vector containing replications |
column |
vector containing column strip levels |
row |
vector containing row strip levels |
mean.comparison.test |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result
data(splitdata) #Split data is used for sake of demonstration #Using Date of sowing as Column factor and varieties as Row factor and using LSD test for Yield only stripplot(splitdata[4],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1) #Using Date of sowing as Column factor and varieties as Row factor and using LSD test for both var. stripplot(splitdata[4:5],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)data(splitdata) #Split data is used for sake of demonstration #Using Date of sowing as Column factor and varieties as Row factor and using LSD test for Yield only stripplot(splitdata[4],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1) #Using Date of sowing as Column factor and varieties as Row factor and using LSD test for both var. stripplot(splitdata[4:5],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)
The function converts the yield data of plot into qtl/ha, tonnes/ha, qtl/acre or tonnes/acre depending on the option chosen.
yieldconvert(yield.in.kg, length.of.plot, width.of.plot, choose.convert.to)yieldconvert(yield.in.kg, length.of.plot, width.of.plot, choose.convert.to)
yield.in.kg |
yield data in kilograms |
length.of.plot |
length of plot in m |
width.of.plot |
width of the plot in m |
choose.convert.to |
0 for qtl/ha, 1 for tonnes/ha, 2 for qtl/acre and 3 for tonnes/acre |
converted yield
#Convert yield vector obtained from 10m x 5m plot into different forms yield<-c(10,15,12,16,19,25,30,25,11) #For converting into qtl/ha yieldconvert(yield,10,5,0) #For converting into tonnes/ha yieldconvert(yield,10,5,1) #For converting into qtl/acre yieldconvert(yield,10,5,2) #For converting into tonnes/acre yieldconvert(yield,10,5,3)#Convert yield vector obtained from 10m x 5m plot into different forms yield<-c(10,15,12,16,19,25,30,25,11) #For converting into qtl/ha yieldconvert(yield,10,5,0) #For converting into tonnes/ha yieldconvert(yield,10,5,1) #For converting into qtl/acre yieldconvert(yield,10,5,2) #For converting into tonnes/acre yieldconvert(yield,10,5,3)