115 lines
2.3 KiB
R
115 lines
2.3 KiB
R
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# preberemo parametre 1->folderpath
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params <- commandArgs(trailingOnly=TRUE)
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path <- params[1]
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# preberemo sfiltrirano tabelo s podatki
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dataTable <- read.table(
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file = paste(path, 'admin/survey/R/TempData/ttest_data.tmp', sep=''),
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sep = ',',
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colClasses = 'numeric',
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comment.char = '',
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quote = '',
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header = FALSE
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)
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# ce ni checkbox moramo podatke preurediti (imamo 2 dodatna parametra)
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if(params[3] > 1){
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val1 <- params[2]
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val2 <- params[3]
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dataTableClean <- dataTable[sapply(dataTable[,1], function(x) all((x == val1) || (x == val2))), ]
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dataTable[,1][dataTable[,1] != val1] <- 0
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dataTable[,1][dataTable[,1] == val1] <- 1
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dataTable[,2][dataTable[,2] != val2] <- 0
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dataTable[,2][dataTable[,2] == val2] <- 1
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}
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# pocistimo vse vrstice, ki imajo za numeric (col 3) missing (< 0)
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dataTable <- dataTable[sapply(dataTable[,3], function(x) all(x > -1)), ]
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dataTableClean <- dataTableClean[sapply(dataTableClean[,3], function(x) all(x > -1)), ]
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# podmnozica vrednosti glede na 1. vrednost
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col1 <- subset(dataTable, dataTable[,1] == 1)
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# podmnozica vrednosti glede na 2. vrednost
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col2 <- subset(dataTable, dataTable[,2] == 1)
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# n
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n1 <- nrow(col1)
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n2 <- nrow(col2)
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# avg(x)
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avg1 <- mean(col1[,3])
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if(is.nan(avg1))
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avg1 <- 0
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avg2 <- mean(col2[,3])
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if(is.nan(avg2))
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avg2 <- 0
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# standardna deviacija (s^2??)
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sd1 <- sd(col1[,3])
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if(is.na(sd1))
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sd1 <- 0
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sd2 <- sd(col2[,3])
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if(is.na(sd2))
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sd2 <- 0
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# standardna napaka
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se1 = sd1 / sqrt(n1)
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if(is.nan(se1))
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se1 <- 0
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se2 = sd2 / sqrt(n2)
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if(is.nan(se2))
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se2 <- 0
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# kvadrat standardne napake
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se21 <- se1^2
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se22 <- se2^2
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# margini (1,96 * se)
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mar1 <- 1.96 * se1
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mar2 <- 1.96 * se2
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# izvedemo ttest (ce obstajata po vsaj dva primera vsake binarni vrednosti)
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if(n1 > 1 && n2 > 1){
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ttest <- t.test(dataTableClean[,3]~dataTableClean[,1], var.equal=FALSE)
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# razlika povpre<72>ij => $d = x1 -x2
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d <- avg1 - avg2
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# sed (std. error difference)
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#sed <- sqrt(se21 + se22)
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#sed <- ttest$estimate/ttest$statistic
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sed <- (-1 * diff(ttest$estimate) / ttest$statistic)
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#T <- d / sed
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T <- ttest$statistic
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# signifikanca
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sig <- ttest$p.value
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} else{
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d <- 0
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sed <- 0
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T <- 0
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sig <- 0
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}
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# podatke vrnemo v x1_x2_x3...--y1_y2...
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cat(paste(n1, avg1, sd1, se1, se21, mar1, sep="_"))
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cat("--")
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cat(paste(n2, avg2, sd2, se2, se22, mar2, sep="_"))
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cat("--")
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cat(paste(d, sed, T, sig, sep="_"))
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