The goal of criticalvalue is to …
You can install the development version of criticalESvalue like so:
# require(remotes)
remotes::install_github("psicostat/criticalESvalue")# loading the package
library(criticalESvalue)# t-test (welch)
x <- rnorm(30, 0.5, 1)
y <- rnorm(30, 0, 1)
ttest <- t.test(x, y)
critical(ttest)
#>
#> Welch Two Sample t-test
#>
#> data: x and y
#> t = 1.27, df = 57.7, p-value = 0.21
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -0.20152 0.90726
#> sample estimates:
#> mean of x mean of y
#> 0.290583 -0.062284
#>
#> |== Effect Size and Critical Value ==|
#> d = 0.329 dc = ± 0.51689 bc = ± 0.55439
#> g = 0.3247 gc = ± 0.51014
# t-test (standard)
ttest <- t.test(x, y, var.equal = TRUE)
critical(ttest)
#>
#> Two Sample t-test
#>
#> data: x and y
#> t = 1.27, df = 58, p-value = 0.21
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -0.20147 0.90721
#> sample estimates:
#> mean of x mean of y
#> 0.290583 -0.062284
#>
#> |== Effect Size and Critical Value ==|
#> d = 0.329 dc = ± 0.51684 bc = ± 0.55434
#> g = 0.32472 gc = ± 0.51012
# t-test (standard) with monodirectional hyp
ttest <- t.test(x, y, var.equal = TRUE, alternative = "less")
critical(ttest)
#>
#> Two Sample t-test
#>
#> data: x and y
#> t = 1.27, df = 58, p-value = 0.9
#> alternative hypothesis: true difference in means is less than 0
#> 95 percent confidence interval:
#> -Inf 0.81577
#> sample estimates:
#> mean of x mean of y
#> 0.290583 -0.062284
#>
#> |== Effect Size and Critical Value ==|
#> d = 0.329 dc = -0.43159 bc = -0.46291
#> g = 0.32472 gc = -0.42598
# within the t-test object saved from critical we have all the new values
ttest <- critical(ttest)
str(ttest)
#> List of 15
#> $ statistic : Named num 1.27
#> ..- attr(*, "names")= chr "t"
#> $ parameter : Named num 58
#> ..- attr(*, "names")= chr "df"
#> $ p.value : num 0.896
#> $ conf.int : num [1:2] -Inf 0.816
#> ..- attr(*, "conf.level")= num 0.95
#> $ estimate : Named num [1:2] 0.2906 -0.0623
#> ..- attr(*, "names")= chr [1:2] "mean of x" "mean of y"
#> $ null.value : Named num 0
#> ..- attr(*, "names")= chr "difference in means"
#> $ stderr : num 0.277
#> $ alternative: chr "less"
#> $ method : chr " Two Sample t-test"
#> $ data.name : chr "x and y"
#> $ g : num 0.325
#> $ gc : num 0.426
#> $ d : num 0.329
#> $ bc : num 0.463
#> $ dc : num 0.432
#> - attr(*, "class")= chr [1:3] "critvalue" "ttest" "htest"We can check the results using:
ttest <- t.test(x, y)
ttest <- critical(ttest)
t <- ttest$bc/ttest$stderr # critical numerator / standard error
# should be 0.05 (or alpha)
(1 - pt(ttest$bc/ttest$stderr, ttest$parameter)) * 2
#> [1] 0.05# cor.test
ctest <- cor.test(x, y)
critical(ctest)
#>
#> Pearson's product-moment correlation
#>
#> data: x and y
#> t = 1.25, df = 28, p-value = 0.22
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#> -0.14148 0.54550
#> sample estimates:
#> cor
#> 0.23054
#>
#> |== Critical Value ==|
#> |rc| = 0.36101# linear model (unstandardized)
z <- rnorm(30)
q <- rnorm(30)
dat <- data.frame(x, y, z, q)
fit <- lm(y ~ x + q + z, data = dat)
fit <- critical(fit)
fit
#>
#> Call:
#> lm(formula = y ~ x + q + z, data = dat)
#>
#> Coefficients:
#> (Intercept) x q z
#> 0.0822 0.3405 -0.0142 -0.4964
#>
#>
#> Critical |Coefficients|
#>
#> (Intercept) x q z
#> 0.46049 0.40167 0.36100 0.48140
summary(fit)
#>
#> Call:
#> lm(formula = y ~ x + q + z, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1.6531 -0.6124 -0.0963 0.4325 2.3414
#>
#> Coefficients:
#> Estimate |Critical Estimate| Std. Error t value Pr(>|t|)
#> (Intercept) 0.0822 0.4605 0.2240 0.37 0.717
#> x 0.3405 0.4017 0.1954 1.74 0.093 .
#> q -0.0142 0.3610 0.1756 -0.08 0.936
#> z -0.4964 0.4814 0.2342 -2.12 0.044 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 1.05 on 26 degrees of freedom
#> Multiple R-squared: 0.193, Adjusted R-squared: 0.0998
#> F-statistic: 2.07 on 3 and 26 DF, p-value: 0.128library(metafor)
#> Loading required package: Matrix
#> Loading required package: metadat
#> Loading required package: numDeriv
#>
#> Loading the 'metafor' package (version 4.6-0). For an
#> introduction to the package please type: help(metafor)
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
fit <- rma(yi, vi, mods=cbind(ablat, year), data=dat)
critical(fit)
#>
#> Mixed-Effects Model (k = 13; tau^2 estimator: REML)
#>
#> tau^2 (estimated amount of residual heterogeneity): 0.1108 (SE = 0.0845)
#> tau (square root of estimated tau^2 value): 0.3328
#> I^2 (residual heterogeneity / unaccounted variability): 71.98%
#> H^2 (unaccounted variability / sampling variability): 3.57
#> R^2 (amount of heterogeneity accounted for): 64.63%
#>
#> Test for Residual Heterogeneity:
#> QE(df = 10) = 28.3251, p-val = 0.0016
#>
#> Test of Moderators (coefficients 2:3):
#> QM(df = 2) = 12.2043, p-val = 0.0022
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> intrcpt -3.5455 29.0959 -0.1219 0.9030 -60.5724 53.4814
#> ablat -0.0280 0.0102 -2.7371 0.0062 -0.0481 -0.0080 **
#> year 0.0019 0.0147 0.1299 0.8966 -0.0269 0.0307
#>
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> |critical estimate|
#> intrcpt 62.85783
#> ablat 0.02211
#> year 0.03172x <- rnorm(30, 0.5, 1)
y <- rnorm(30, 0, 1)
ttest <- t.test(x, y)
m1 <- mean(x)
m2 <- mean(y)
sd1 <- sd(x)
sd2 <- sd(y)
n1 <- n2 <- 30
critical_t2s(m1, m2, sd1 = sd1, sd2 = sd2, n1 = n1, n2 = n2)
#> $d
#> [1] 0.35587
#>
#> $dc
#> [1] 0.51703
#>
#> $bc
#> [1] 0.50091
#>
#> $se
#> [1] 0.25015
#>
#> $df
#> [1] 57.018
#>
#> $g
#> [1] 0.35116
#>
#> $gc
#> [1] 0.51019
critical_t2s(t = ttest$statistic, se = ttest$stderr, n1 = n1, n2 = n2)
#> Warning in crit_from_t_t2s(t = t, n1 = n1, n2 = n2, se = se, conf.level =
#> conf.level, : When var.equal = FALSE the critical value calculated from t
#> assume sd1 = sd2!
#> $d
#> [1] 0.35587
#>
#> $dc
#> [1] 0.51684
#>
#> $bc
#> [1] 0.50073
#>
#> $se
#> [1] 0.25015
#>
#> $df
#> [1] 58
#>
#> $g
#> [1] 0.35124
#>
#> $gc
#> [1] 0.51012
critical_t2s(t = ttest$statistic, n1 = n1, n2 = n2)
#> Warning in crit_from_t_t2s(t = t, n1 = n1, n2 = n2, se = se, conf.level =
#> conf.level, : When var.equal = FALSE the critical value calculated from t
#> assume sd1 = sd2!
#> Warning in crit_from_t_t2s(t = t, n1 = n1, n2 = n2, se = se, conf.level =
#> conf.level, : When se = NULL bc cannot be computed, returning NA!
#> $d
#> [1] 0.35587
#>
#> $dc
#> [1] 0.51684
#>
#> $bc
#> [1] NA
#>
#> $se
#> NULL
#>
#> $df
#> [1] 58
#>
#> $g
#> [1] 0.35124
#>
#> $gc
#> [1] 0.51012