## Changing significance notation in R

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R has certain significance codes to determine statistical significance. In the example below, for example, a dot `.`

indicates significance at the 10% level (see sample output below).

Dots can be very hard to see, especially when I copy-paste to Excel and display it in Times New Roman.

I'd like to change it such that:

`*`

= significant at 10%`**`

= significant at 5%`***`

= significant at 1%

Is there a way I can do this?

> y = c(1,2,3,4,5,6,7,8) > x = c(1,3,2,4,5,6,8,7) > summary(lm(y~x)) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -1.0714 -0.3333 0.0000 0.2738 1.1191 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2143 0.6286 0.341 0.74480 x 0.9524 0.1245 7.651 0.00026 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8067 on 6 degrees of freedom Multiple R-squared: 0.907, Adjusted R-squared: 0.8915 F-statistic: 58.54 on 1 and 6 DF, p-value: 0.0002604

You can create your own formatting function with

mystarformat <- function(x) symnum(x, corr = FALSE, na = FALSE, cutpoints = c(0, 0.01, 0.05, 0.1, 1), symbols = c("***", "**", "*", " "))

And you can write your own coefficient formatter

show_coef <- function(mm) { mycoef<-data.frame(coef(summary(mm)), check.names=F) mycoef$signif = mystarformat(mycoef$`Pr(>|t|)`) mycoef$`Pr(>|t|)` = format.pval(mycoef$`Pr(>|t|)`) mycoef }

And then with your model, you can run it with

mm <- lm(y~x) show_coef(mm) # Estimate Std. Error t value Pr(>|t|) signif # (Intercept) 0.2142857 0.6285895 0.3408993 0.7447995 # x 0.9523810 0.1244793 7.6509206 0.0002604 ***

**R Handbook: Hypothesis Testing and p-values,** The goal is to determine: a) statistical significance, b) effect size, c) practical or plots, but jumping to statistical inference needs a little change in perspective. which is shorthand for the number in scientific notation, 2.2 x 10-16, which is show the value to be rounded in the first column, the number of rounded significant digits (SD) in the second column, and the rounded result (Algor) obtained by applying Algorithm 3.2. The following R code appends the value obtained with signif for (i in 1:dim (tabl)) { tablstr<-sprintf ("%dt%8.4ft%dt%6.2ft%6.2fn",

Sorry for the late response, but I found a great solution to this. Just do the following:

install.packages("stargazer") library(stargazer) stargazer(your_regression, type = "text")

This displays everything in a beautiful way with your desired format.
Note: If you leave `type = "text"`

out, then you'll get the LaTeX code.

**R,** Firstly, the z or t value (depending on what family you run) is the coefficient divided by the standard error. The p value is then derived from the The P-value (0.3622) is greater than the significance level 5% (1-0.95), so we conclude that the null hypothesis that the mean of this population is 9 is plausible. Two-sample hypothesis test If we are interested in finding the confidence interval for the difference of two population means, the R-command "t.test" is also to be used.

One should be aware that stargazer package reports significance levels with a different scale than other statistical softwares like STATA.

In R (stargazer) you get `# (* p<0.1; ** p<0.05; *** p<0.01)`

. Whereas, in STATA you get `# (* p<0.05, ** p<0.01, *** p< 0.001)`

.

This means that what is significant with one * in R results may appear not to be significant for a STATA user.

**R: How to change significance level for lm()?,** The lm() function in R does not just report whether or not your predictors are significant at alpha = 95%, but also reports the p-values. So you You can use numbers written in scientific notation as though they were regular numbers, like so: > 1.2e6 / 2e3 [1] 600. R automatically decides whether to print a number in scientific notation. Its decision to use scientific notation doesn’t change the number or the accuracy of the calculation; it just saves some space.

**Add P-values and Significance Levels to ggplots,** If TRUE, hide ns symbol when displaying significance levels. add = "jitter") # Add p-value p + stat_compare_means() # Change method p + digits: How many significant digits of numeric values to show. nsmall: The minimum number of digits after the decimal point. In addition, you can control the format of the decimal point with decimal.mark, the mark between intervals before the decimal point with big.mark, as well as the mark between intervals after the decimal point with small.mark.

**Hypothesis Testing,** An R tutorial on statistical hypothesis testing based on critical value approach. The probability of committing a type I error is called the significance level of the In this article, we’ll describe how to easily i) compare means of two or multiple groups; ii) and to automatically add p-values and significance levels to a ggplot (such as box plots, dot plots, bar plots and line plots …). Contents: Prerequisites Methods for comparing means R functions to add p-values Compare two independent groups Compare two paired samples Compare more than two groups

**Add P-values and Significance Levels to ggplots - Articles,** Install and load required R packages; Demo data sets If TRUE, hide ns symbol when displaying significance levels. palette = "jco")+ stat_compare_means() # Change method to anova ggboxplot(ToothGrowth, x = "dose", Enter a number or a decimal number or scientific notation and the calculator converts to scientific notation, e notation and engineering notation formats. To enter a number in scientific notation use a carat ^ to indicate the powers of 10. You can also enter numbers in e notation. Examples: 3.45 x 10^5 or 3.45e5.

##### Comments

- That's printed by the
`stats:::printCoefmat`

and it has those values hard-coded in there. You can format p-values with your own symbols by calling`symnum()`

but the default options for`summary.lm`

don't seem to be configurable. Are you really pasting these results into Excel?? Yuck. - Basically copy-pasting the coefficients, then using the "Text to columns" feature to make everything neat.
- Thanks for suggesting symnum, I'll check that out.
- There are frequently questions about the stars, either in reconfiguring the breaks or in over-confidence in them. The latter point was published in the American Statistics Association as a caution against over- or mis-use of
*p*-values. I've even seen (unofficial) mention of trying to remove them from the`lm`

summary report, though I can't find those refs at the moment. - Though highly unlikely, a
*p*-value of 0 will return`NA`

(since`cut`

breaks by default are closed on the left). You can mitigate that by changing your first cut point from 0 to "something less". - @r2evans I basically did the same thing
`stats:::printCoefmat`

there as well so I guess the default`print.summary.lm`

would have the same problem. - So true, good point. I guess none of my statistical results have ever been "perfectly-significant".
- @r2evans Actually
`mystarformat(0)`

returns "***" so i guess it's just the fact that`symnum`

is different than`cut`

- Ahhh, I see the reference to
`any(ii <- is.na(iS))`

in`symnum`

at line 68 of`symnum.R`

. Sorry, premature mitigation :-) - BTW, you can change the cutoffs in stargazer package to make it like other softwares' outputs, in case you work with co-authors for example: stargazer(output, output2, type = "html", star.cutoffs = c(0.05, 0.01, 0.001)) # star.cutoffs = NULL by default