Zen

Translation

data('mtcars')

tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl)
dat <- as.data.frame(tab)

logLinear(data = dat, factors = vars(gear, cyl), counts = Freq,
blocks = list(list("gear", "cyl", c("gear", "cyl"))),
refLevels = list(
list(var="gear", ref="3"),
list(var="cyl", ref="4")))

#
# LOG-LINEAR REGRESSION
#
# Model Fit Measures
# ───────────────────────────────────────
# Model Deviance AIC R²-McF
# ───────────────────────────────────────
# 1 4.12e-10 41.4 1.000
# ───────────────────────────────────────
#
#
# MODEL SPECIFIC RESULTS
#
# MODEL 1
#
# Model Coefficients
# ──────────────────────────────────────────────────────────────────
# Predictor Estimate SE Z p
# ──────────────────────────────────────────────────────────────────
# Intercept -4.71e-16 1.00 -4.71e-16 1.000
# gear:
# 4 – 3 2.079 1.06 1.961 0.050
# 5 – 3 0.693 1.22 0.566 0.571
# cyl:
# 6 – 4 0.693 1.22 0.566 0.571
# 8 – 4 2.485 1.04 2.387 0.017
# gear:cyl:
# (4 – 3):(6 – 4) -1.386 1.37 -1.012 0.311
# (5 – 3):(6 – 4) -1.386 1.73 -0.800 0.423
# (4 – 3):(8 – 4) -26.867 42247.17 -6.36e -4 0.999
# (5 – 3):(8 – 4) -2.485 1.44 -1.722 0.085
# ──────────────────────────────────────────────────────────────────
#
#
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English Catalan Actions
data('iris')

mancova(data = iris,
deps = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width),
factors = Species)

#
# MANCOVA
#
# Multivariate Tests
# ───────────────────────────────────────────────────────────────────────────
# value F df1 df2 p
# ───────────────────────────────────────────────────────────────────────────
# Species Pillai's Trace 1.19 53.5 8 290 < .001
# Wilks' Lambda 0.0234 199 8 288 < .001
# Hotelling's Trace 32.5 581 8 286 < .001
# Roy's Largest Root 32.2 1167 4 145 < .001
# ───────────────────────────────────────────────────────────────────────────
#
#
# Univariate Tests
# ───────────────────────────────────────────────────────────────────────────────────────────────
# Dependent Variable Sum of Squares df Mean Square F p
# ───────────────────────────────────────────────────────────────────────────────────────────────
# Species Sepal.Length 63.21 2 31.6061 119.3 < .001
# Sepal.Width 11.34 2 5.6725 49.2 < .001
# Petal.Length 437.10 2 218.5514 1180.2 < .001
# Petal.Width 80.41 2 40.2067 960.0 < .001
# Residuals Sepal.Length 38.96 147 0.2650
# Sepal.Width 16.96 147 0.1154
# Petal.Length 27.22 147 0.1852
# Petal.Width 6.16 147 0.0419
# ───────────────────────────────────────────────────────────────────────────────────────────────
#
data('iris')

mancova(data = iris,
deps = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width),
factors = Species)

#
# MANCOVA
#
# Proves Multivariable
# ───────────────────────────────────────────────────────────────────────────
# valor F gl1 gl2 p
# ───────────────────────────────────────────────────────────────────────────
# Species Pillai's Trace 1,19 53,5 8 290 < ,001
# Wilks' Lambda 0,0234 199 8 288 < ,001
# Hotelling's Trace 32,5 581 8 286 < ,001
# Roy's Largest Root 32,2 1167 4 145 < ,001
# ───────────────────────────────────────────────────────────────────────────
#
#
# Proves Univariable
# ───────────────────────────────────────────────────────────────────────────────────────────────
# Variable dependent Suma Quadràtica gl Mitjana Quadràtica F p
# ───────────────────────────────────────────────────────────────────────────────────────────────
# Species Sepal.Length 63,21 2 31,6061 119,3 < ,001
# Sepal.Width 11,34 2 5,6725 49,2 < ,001
# Petal.Length 437,10 2 218,5514 1180,2 < ,001
# Petal.Width 80,41 2 40,2067 960,0 < ,001
# residus Sepal.Length 38,96 147 0,2650
# Sepal.Width 16,96 147 0,1154
# Petal.Length 27,22 147 0,1852
# Petal.Width 6,16 147 0,0419
# ───────────────────────────────────────────────────────────────────────────────────────────────
#
data('iris')

pca(iris, vars = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))

#
# PRINCIPAL COMPONENT ANALYSIS
#
# Component Loadings
# ────────────────────────────────────────
# 1 Uniqueness
# ────────────────────────────────────────
# Sepal.Length 0.890 0.2076
# Sepal.Width -0.460 0.7883
# Petal.Length 0.992 0.0168
# Petal.Width 0.965 0.0688
# ────────────────────────────────────────
# Note. 'varimax' rotation was used
#
data('iris')

pca(iris, vars = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))

#
# ANÀLISI DE COMPONENTS PRINCIPALS
#
# Càrregues de components
# ────────────────────────────────────────
# 1 Unicitat
# ────────────────────────────────────────
# Sepal.Length 0,890 0,2076
# Sepal.Width -0,460 0,7883
# Petal.Length 0,992 0,0168
# Petal.Width 0,965 0,0688
# ────────────────────────────────────────
# Nota. S'ha utilitzat rotació 'varimax'
#
data('iris')

reliability(iris, vars = c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width'),
omegaScale = TRUE)

#
# RELIABILITY ANALYSIS
#
# Scale Reliability Statistics
# ─────────────────────────────────────────
# Cronbach's α McDonald's ω
# ─────────────────────────────────────────
# scale 0.708 0.848
# ─────────────────────────────────────────
#
data('iris')

reliability(iris, vars = c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width'),
omegaScale = TRUE)

#
# ANÀLISI DE FIABILITAT
#
# Estadístics de Fiabilitat de la escala
# ─────────────────────────────────────────
# α de Cronbach ω de McDonald
# ─────────────────────────────────────────
# escala 0,708 0,848
# ─────────────────────────────────────────
#
data('mtcars')

corrMatrix(mtcars, vars = vars(mpg, cyl, disp, hp))

#
# CORRELATION MATRIX
#
# Correlation Matrix
# ──────────────────────────────────────────────────────────────
# mpg cyl disp hp
# ──────────────────────────────────────────────────────────────
# mpg Pearson's r — -0.852 -0.848 -0.776
# p-value — < .001 < .001 < .001
#
# cyl Pearson's r — 0.902 0.832
# p-value — < .001 < .001
#
# disp Pearson's r — 0.791
# p-value — < .001
#
# hp Pearson's r —
# p-value —
# ──────────────────────────────────────────────────────────────
#
data('mtcars')

corrMatrix(mtcars, vars = vars(mpg, cyl, disp, hp))

#
# MATRIU DE CORRELACIONS
#
# Matriu de correlacions
# ──────────────────────────────────────────────────────────────
# mpg cyl disp hp
# ──────────────────────────────────────────────────────────────
# mpg r de Pearson — -0,852 -0,848 -0,776
# p-valor — < ,001 < ,001 < ,001
#
# cyl r de Pearson — 0,902 0,832
# p-valor — < ,001 < ,001
#
# disp r de Pearson — 0,791
# p-valor — < ,001
#
# hp r de Pearson —
# p-valor —
# ──────────────────────────────────────────────────────────────
#
data('mtcars')

corrPart(mtcars, vars = vars(mpg, cyl, disp), controls = vars(hp))

#
# PARTIAL CORRELATION
#
# Partial Correlation
# ────────────────────────────────────────────────────
# mpg cyl disp
# ────────────────────────────────────────────────────
# mpg Pearson's r —
# p-value —
#
# cyl Pearson's r -0.590 —
# p-value < .001 —
#
# disp Pearson's r -0.606 0.719 —
# p-value < .001 < .001 —
# ────────────────────────────────────────────────────
# Note. controlling for 'hp'
#
data('mtcars')

corrPart(mtcars, vars = vars(mpg, cyl, disp), controls = vars(hp))

#
# CORRELACIÓ PARCIAL
#
# Correlació Parcial
# ────────────────────────────────────────────────────
# mpg cyl disp
# ────────────────────────────────────────────────────
# mpg r de Pearson —
# p-valor —
#
# cyl r de Pearson -0,590 —
# p-valor < ,001 —
#
# disp r de Pearson -0,606 0,719 —
# p-valor < ,001 < ,001 —
# ────────────────────────────────────────────────────
# Nota. Controlant per 'hp'
#
data('mtcars')

tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl)
dat <- as.data.frame(tab)

logLinear(data = dat, factors = vars(gear, cyl), counts = Freq,
blocks = list(list("gear", "cyl", c("gear", "cyl"))),
refLevels = list(
list(var="gear", ref="3"),
list(var="cyl", ref="4")))

#
# LOG-LINEAR REGRESSION
#
# Model Fit Measures
# ───────────────────────────────────────
# Model Deviance AIC R²-McF
# ───────────────────────────────────────
# 1 4.12e-10 41.4 1.000
# ───────────────────────────────────────
#
#
# MODEL SPECIFIC RESULTS
#
# MODEL 1
#
# Model Coefficients
# ──────────────────────────────────────────────────────────────────
# Predictor Estimate SE Z p
# ──────────────────────────────────────────────────────────────────
# Intercept -4.71e-16 1.00 -4.71e-16 1.000
# gear:
# 4 – 3 2.079 1.06 1.961 0.050
# 5 – 3 0.693 1.22 0.566 0.571
# cyl:
# 6 – 4 0.693 1.22 0.566 0.571
# 8 – 4 2.485 1.04 2.387 0.017
# gear:cyl:
# (4 – 3):(6 – 4) -1.386 1.37 -1.012 0.311
# (5 – 3):(6 – 4) -1.386 1.73 -0.800 0.423
# (4 – 3):(8 – 4) -26.867 42247.17 -6.36e -4 0.999
# (5 – 3):(8 – 4) -2.485 1.44 -1.722 0.085
# ──────────────────────────────────────────────────────────────────
#
#
data('mtcars')

tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl)
dat <- as.data.frame(tab)

logLinear(data = dat, factors = vars(gear, cyl), counts = Freq,
blocks = list(list("gear", "cyl", c("gear", "cyl"))),
refLevels = list(
list(var="gear", ref="3"),
list(var="cyl", ref="4")))

#
# REGRESSIÓ LOG-LINEAL
#
# Mesures ajustament del model
# ───────────────────────────────────────
# model Desviació AIC R²-McF
# ───────────────────────────────────────
# 1 4,12e-10 41,4 1,000
# ───────────────────────────────────────
#
#
# RESULTATS ESPECÍFICS DEL MODEL
#
# MODEL 1
#
# Coeficients de model
# ──────────────────────────────────────────────────────────────────
# Predictor Estimació EE Z p
# ──────────────────────────────────────────────────────────────────
# Constant -4,71e-16 1,00 -4,71e-16 1,000
# gear:
# 4 – 3 2,079 1,06 1,961 0,050
# 5 – 3 0,693 1,22 0,566 0,571
# cyl:
# 6 – 4 0,693 1,22 0,566 0,571
# 8 – 4 2,485 1,04 2,387 0,017
# gear:cyl:
# (4 – 3):(6 – 4) -1,386 1,37 -1,012 0,311
# (5 – 3):(6 – 4) -1,386 1,73 -0,800 0,423
# (4 – 3):(8 – 4) -26,867 42247,17 6,36e-4 0,999
# (5 – 3):(8 – 4) -2,485 1,44 -1,722 0,085
# ──────────────────────────────────────────────────────────────────
#
#
data('mtcars')
dat <- mtcars

# frequency tables can be provided for factors
dat$gear <- as.factor(dat$gear)

descriptives(dat, vars = vars(mpg, cyl, disp, gear), freq = TRUE)

#
# DESCRIPTIVES
#
# Descriptives
# ───────────────────────────────────────────
# mpg cyl disp gear
# ───────────────────────────────────────────
# N 32 32 32 32
# Missing 0 0 0 0
# Mean 20.1 6.19 231 3.69
# Median 19.2 6.00 196 4.00
# Minimum 10.4 4.00 71.1 3
# Maximum 33.9 8.00 472 5
# ───────────────────────────────────────────
#
#
# FREQUENCIES
#
# Frequencies of gear
# ────────────────────
# Levels Counts
# ────────────────────
# 3 15
# 4 12
# 5 5
# ────────────────────
#

# spliting by a variable
descriptives(formula = disp + mpg ~ cyl, dat,
median=F, min=F, max=F, n=F, missing=F)

# providing histograms
descriptives(formula = mpg ~ cyl, dat, hist=T,
median=F, min=F, max=F, n=F, missing=F)

# splitting by multiple variables
descriptives(formula = mpg ~ cyl:gear, dat,
median=F, min=F, max=F, missing=F)
data('mtcars')
dat <- mtcars

# Es poden proporcionar taules de freqüència pels factors
dat$gear <- as.factor(dat$gear)

descriptives(dat, vars = vars(mpg, cyl, disp, gear), freq = TRUE)

#
# DESCRIPTIUS
#
# Descriptius
# ───────────────────────────────────────────
# mpg cyl disp gear
# ───────────────────────────────────────────
# N 32 32 32 32
# Perduts 0 0 0 0
# Mitjana 20,1 6,19 231 3,69
# Mediana 19,2 6,00 196 4,00
# Mínim 10,4 4,00 71,1 3
# Màxim 33,9 8,00 472 5
# ───────────────────────────────────────────
#
#
# FREQÜÈNCIES
#
# Freqüêncies de gear
# ────────────────────
# nivells Casos
# ────────────────────
# 3 15
# 4 12
# 5 5
# ────────────────────
#

# Segmentant per una variable
descriptives(formula = disp + mpg ~ cyl, dat,
median=F, min=F, max=F, n=F, missing=F)

# Proporcionant histogrames
descriptives(formula = mpg ~ cyl, dat, hist=T,
median=F, min=F, max=F, n=F, missing=F)

# Segmentant per múltiples variables
descriptives(formula = mpg ~ cyl:gear, dat,
median=F, min=F, max=F, missing=F)
data('Prestige', package='carData')

linReg(data = Prestige, dep = income,
covs = vars(education, prestige, women),
blocks = list(list('education', 'prestige', 'women')))

#
# LINEAR REGRESSION
#
# Model Fit Measures
# ───────────────────────────
# Model R R²
# ───────────────────────────
# 1 0.802 0.643
# ───────────────────────────
#
#
# MODEL SPECIFIC RESULTS
#
# MODEL 1
#
#
# Model Coefficients
# ────────────────────────────────────────────────────────
# Predictor Estimate SE t p
# ────────────────────────────────────────────────────────
# Intercept -253.8 1086.16 -0.234 0.816
# women -50.9 8.56 -5.948 < .001
# prestige 141.4 29.91 4.729 < .001
# education 177.2 187.63 0.944 0.347
# ────────────────────────────────────────────────────────
#
data('Prestige', package='carData')

linReg(data = Prestige, dep = income,
covs = vars(education, prestige, women),
blocks = list(list('education', 'prestige', 'women')))

#
# REGRESSIÓ LINEAL
#
# Mesures ajustament del model
# ────────────────────────────
# model R R²
# ────────────────────────────
# 1 0,802 0,643
# ────────────────────────────
#
#
# RESULTATS ESPECÍFICS DEL MODEL
#
# MODEL 1
#
#
# Coeficients de model
# ────────────────────────────────────────────────────────
# Predictor Estimació SE t p
# ────────────────────────────────────────────────────────
# Constant -253,8 1086,16 -0,234 0,816
# women -50,9 8,56 -5,948 < ,001
# prestige 141,4 29,91 4,729 < ,001
# education 177,2 187,63 0,944 0,347
# ────────────────────────────────────────────────────────
#
data('ToothGrowth')

ancova(formula = len ~ supp + dose, data = ToothGrowth)

#
# ANCOVA
#
# ANCOVA
# ───────────────────────────────────────────────────────────────────────
# Sum of Squares df Mean Square F p
# ───────────────────────────────────────────────────────────────────────
# supp 205 1 205.4 11.4 0.001
# dose 2224 1 2224.3 124.0 < .001
# Residuals 1023 57 17.9
# ───────────────────────────────────────────────────────────────────────
#

ancova(
formula = len ~ supp + dose,
data = ToothGrowth,
postHoc = ~ supp,
emMeans = ~ supp)
data('ToothGrowth')

ancova(formula = len ~ supp + dose, data = ToothGrowth)

#
# ANCOVA
#
# ANCOVA
# ───────────────────────────────────────────────────────────────────────
# Suma Quadràtica gl Mitjana Quadràtica F p
# ───────────────────────────────────────────────────────────────────────
# supp 205 1 205,4 11,4 0,001
# dose 2224 1 2224,3 124,0 < ,001
# residu 1023 57 17,9
# ───────────────────────────────────────────────────────────────────────
#

ancova(
formula = len ~ supp + dose,
data = ToothGrowth,
postHoc = ~ supp,
emMeans = ~ supp)
data('ToothGrowth')

ANOVA(formula = len ~ dose * supp, data = ToothGrowth)

#
# ANOVA
#
# ANOVA
# ───────────────────────────────────────────────────────────────────────
# Sum of Squares df Mean Square F p
# ───────────────────────────────────────────────────────────────────────
# dose 2426 2 1213.2 92.00 < .001
# supp 205 1 205.4 15.57 < .001
# dose:supp 108 2 54.2 4.11 0.022
# Residuals 712 54 13.2
# ───────────────────────────────────────────────────────────────────────
#

ANOVA(
formula = len ~ dose * supp,
data = ToothGrowth,
emMeans = ~ supp + dose:supp, # est. marginal means for supp and dose:supp
emmPlots = TRUE, # produce plots of those marginal means
emmTables = TRUE) # produce tables of those marginal means
data('ToothGrowth')

ANOVA(formula = len ~ dose * supp, data = ToothGrowth)

#
# ANOVA
#
# ANOVA
# ───────────────────────────────────────────────────────────────────────
# Suma Quadràtica gl mitjana Quadràtica F p
# ───────────────────────────────────────────────────────────────────────
# dose 2426 2 1213,2 92,00 < ,001
# supp 205 1 205,4 15,57 < ,001
# dose:supp 108 2 54,2 4,11 0,022
# residu 712 54 13,2
# ───────────────────────────────────────────────────────────────────────
#

ANOVA(
formula = len ~ dose * supp,
data = ToothGrowth,
emMeans = ~ supp + dose:supp, # est. mitjanes marginals per a supp i dose:supp
emmPlots = TRUE, # genera gràfics per a aquestes mitjanes marginals
emmTables = TRUE) # genera taules per a aquestes mitjanes marginals
data('ToothGrowth')

anovaNP(formula = len ~ dose, data=ToothGrowth)

#
# ONE-WAY ANOVA (NON-PARAMETRIC)
#
# Kruskal-Wallis
# ───────────────────────────────
# χ² df p
# ───────────────────────────────
# len 40.7 2 < .001
# ───────────────────────────────
#
data('ToothGrowth')

anovaNP(formula = len ~ dose, data=ToothGrowth)

#
# ANOVA UNIDIRECCIONAL (NO-PARAMÈTRIC)
#
# Kruskal-Wallis
# ───────────────────────────────
# χ² gl p
# ───────────────────────────────
# len 40,7 2 < ,001
# ───────────────────────────────
#

Loading…

User avatar fxmiguel

Translation uploaded

  1. jamovi
  2. jmv-i18n
  3. Catalan
data('mtcars')

tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl)
dat <- as.data.frame(tab)

logLinear(data = dat, factors = vars(gear, cyl), counts = Freq,
blocks = list(list("gear", "cyl", c("gear", "cyl"))),
refLevels = list(
list(var="gear", ref="3"),
list(var="cyl", ref="4")))

#
# LOG-LINEAR REGRESSION
#
# Model Fit Measures
# ───────────────────────────────────────
# Model Deviance AIC R²-McF
# ───────────────────────────────────────
# 1 4.12e-10 41.4 1.000
# ───────────────────────────────────────
#
#
# MODEL SPECIFIC RESULTS
#
# MODEL 1
#
# Model Coefficients
# ──────────────────────────────────────────────────────────────────
# Predictor Estimate SE Z p
# ──────────────────────────────────────────────────────────────────
# Intercept -4.71e-16 1.00 -4.71e-16 1.000
# gear:
# 4 – 3 2.079 1.06 1.961 0.050
# 5 – 3 0.693 1.22 0.566 0.571
# cyl:
# 6 – 4 0.693 1.22 0.566 0.571
# 8 – 4 2.485 1.04 2.387 0.017
# gear:cyl:
# (4 – 3):(6 – 4) -1.386 1.37 -1.012 0.311
# (5 – 3):(6 – 4) -1.386 1.73 -0.800 0.423
# (4 – 3):(8 – 4) -26.867 42247.17 -6.36e -4 0.999
# (5 – 3):(8 – 4) -2.485 1.44 -1.722 0.085
# ──────────────────────────────────────────────────────────────────
#
#
data('mtcars')

tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl)
dat <- as.data.frame(tab)

logLinear(data = dat, factors = vars(gear, cyl), counts = Freq,
blocks = list(list("gear", "cyl", c("gear", "cyl"))),
refLevels = list(
list(var="gear", ref="3"),
list(var="cyl", ref="4")))

#
# REGRESSIÓ LOG-LINEAL
#
# Mesures ajustament del model
# ───────────────────────────────────────
# model Desviació AIC R²-McF
# ───────────────────────────────────────
# 1 4,12e-10 41,4 1,000
# ───────────────────────────────────────
#
#
# RESULTATS ESPECÍFICS DEL MODEL
#
# MODEL 1
#
# Coeficients de model
# ──────────────────────────────────────────────────────────────────
# Predictor Estimació EE Z p
# ──────────────────────────────────────────────────────────────────
# Constant -4,71e-16 1,00 -4,71e-16 1,000
# gear:
# 4 – 3 2,079 1,06 1,961 0,050
# 5 – 3 0,693 1,22 0,566 0,571
# cyl:
# 6 – 4 0,693 1,22 0,566 0,571
# 8 – 4 2,485 1,04 2,387 0,017
# gear:cyl:
# (4 – 3):(6 – 4) -1,386 1,37 -1,012 0,311
# (5 – 3):(6 – 4) -1,386 1,73 -0,800 0,423
# (4 – 3):(8 – 4) -26,867 42247,17 6,36e-4 0,999
# (5 – 3):(8 – 4) -2,485 1,44 -1,722 0,085
# ──────────────────────────────────────────────────────────────────
#
#
2 years ago
User avatar None

String updated in the repository

  1. jamovi
  2. jmv-i18n
  3. Catalan
data('mtcars')

tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl)
dat <- as.data.frame(tab)

logLinear(data = dat, factors = vars(gear, cyl), counts = Freq,
blocks = list(list("gear", "cyl", c("gear", "cyl"))),
refLevels = list(
list(var="gear", ref="3"),
list(var="cyl", ref="4")))

#
# LOG-LINEAR REGRESSION
#
# Model Fit Measures
# ───────────────────────────────────────
# Model Deviance AIC R²-McF
# ───────────────────────────────────────
# 1 4.12e-10 41.4 1.000
# ───────────────────────────────────────
#
#
# MODEL SPECIFIC RESULTS
#
# MODEL 1
#
# Model Coefficients
# ──────────────────────────────────────────────────────────────────
# Predictor Estimate SE Z p
# ──────────────────────────────────────────────────────────────────
# Intercept -4.71e-16 1.00 -4.71e-16 1.000
# gear:
# 4 – 3 2.079 1.06 1.961 0.050
# 5 – 3 0.693 1.22 0.566 0.571
# cyl:
# 6 – 4 0.693 1.22 0.566 0.571
# 8 – 4 2.485 1.04 2.387 0.017
# gear:cyl:
# (4 – 3):(6 – 4) -1.386 1.37 -1.012 0.311
# (5 – 3):(6 – 4) -1.386 1.73 -0.800 0.423
# (4 – 3):(8 – 4) -26.867 42247.17 -6.36e -4 0.999
# (5 – 3):(8 – 4) -2.485 1.44 -1.722 0.085
# ──────────────────────────────────────────────────────────────────
#
#
 
2 years ago
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Consecutive duplicated words

Text contains the same word twice in a row: MODEL

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Glossary

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String information

Flags
ignore-multiple-failures, ignore-newline-count, ignore-punctuation-spacing, ignore-same
Source string location
logLinear/options.description.R.usage
String age
2 years ago
Last updated
a year ago
Source string age
2 years ago
Translation file
ca.po, string 543