significance

Significant figures in the decimal module

由 故事扮演 提交于 2019-12-29 08:40:14
问题 So I've decided to try to solve my physics homework by writing some python scripts to solve problems for me. One problem that I'm running into is that significant figures don't always seem to come out properly. For example this handles significant figures properly: from decimal import Decimal >>> Decimal('1.0') + Decimal('2.0') Decimal("3.0") But this doesn't: >>> Decimal('1.00') / Decimal('3.00') Decimal("0.3333333333333333333333333333") So two questions: Am I right that this isn't the

How to put significance level (p-value) on barplot in ggplot2 automatically?

由 徘徊边缘 提交于 2019-12-13 04:18:19
问题 I saw a similar answer already asked here, but the mine is little bit different. Is there a way to calculate significance and put the stars on the barplot, automatically in ggplot? I have a plot with barplot that represent the mean of an entire season, for some variables (compounds), and other three layers with point, line and errorbars of the average of specific time windows (phenological stages) into the complete season. I want just to put asterisks over barplots of the total season average

Including standardized coefficients in a stargazer table

由 a 夏天 提交于 2019-12-12 17:56:06
问题 I have a series of linear models and I'd like to report the standardized coefficients for each. However, when I print the models in stargazer, it looks like stargazer automatically prints the significance stars for the standardized coefficients as if they were unstandardized coefficients. You can see how the differences emerge below. Is it statistically wrong to print the significance stars based on the unstandardized values? How is this done in stargazer? Thanks! #load libraries library

Test for significance of specific multiple groups in R

由 a 夏天 提交于 2019-12-12 05:49:46
问题 The data looks as follows: > data <- read.csv("data.csv") > head(data) ï..class.1 rev.1 class.2 rev.2 1 7 136.9900 1318 31.9900 2 1223 24.0984 1001 0.0000 3 1318 61.9900 6851 104.2655 4 1014 39.9800 1318 29.9800 5 7 32.9800 7 52.9900 6 291 107.6674 797 31.2741 I want to perform a significance test to compare the means of rev.1 and rev.2 ONLY where groups class.1=class.2. I tried doing this with ANOVA but am having issues only comparing the groups that I want. Any guidance would be greatly

How to calculate statistical significance of correlation in pandas or some other python module

由 天大地大妈咪最大 提交于 2019-12-08 12:04:15
问题 I am using the Pandas library in Python. So, I am calculating correlations between attributes in my data-set. df.corr() This is a nice function that returns a data-frame of correlations between all attributes. There is this term called statistical significance of correlation, which test the probability that this difference was obtain by chance (description here). So does anybody know if there is a function to return this in pandas or in some other Python library. 来源： https://stackoverflow.com

C# Finding relevant document snippets for search result display

由 被刻印的时光 ゝ 提交于 2019-12-03 09:34:44
问题 In developing search for a site I am building, I decided to go the cheap and quick way and use Microsoft Sql Server's Full Text Search engine instead of something more robust like Lucene.Net. One of the features I would like to have, though, is google-esque relevant document snippets. I quickly found determining "relevant" snippets is more difficult than I realized. I want to choose snippets based on search term density in the found text. So, essentially, I need to find the most search term

Hatch area using pcolormesh in Basemap

由 主宰稳场 提交于 2019-12-02 03:05:53
问题 I try to hatch only the regions where I have statistically significant results. How can I do this using Basemap and pcolormesh? plt.figure(figsize=(12,12)) lons = iris_cube.coord('longitude').points lats = iris_cube.coord('latitude').points m = Basemap(llcrnrlon=lons[0], llcrnrlat=lats[0], urcrnrlon=lons[-1], urcrnrlat=lats[-1], resolution='l') lon, lat = np.meshgrid(lons, lats) plt.subplot(111) cs = m.pcolormesh(lon, lat, significant_data, cmap=cmap, norm=norm, hatch='/') 回答1: It seems

Hatch area using pcolormesh in Basemap

由 前提是你 提交于 2019-12-02 01:19:04
I try to hatch only the regions where I have statistically significant results. How can I do this using Basemap and pcolormesh? plt.figure(figsize=(12,12)) lons = iris_cube.coord('longitude').points lats = iris_cube.coord('latitude').points m = Basemap(llcrnrlon=lons[0], llcrnrlat=lats[0], urcrnrlon=lons[-1], urcrnrlat=lats[-1], resolution='l') lon, lat = np.meshgrid(lons, lats) plt.subplot(111) cs = m.pcolormesh(lon, lat, significant_data, cmap=cmap, norm=norm, hatch='/') Ed Smith It seems pcolormesh does not support hatching (see https://github.com/matplotlib/matplotlib/issues/3058 ).

Barplot with significant differences and interactions?

由 99封情书 提交于 2019-11-30 12:14:44
问题 I would like to visualize my data and ANOVA statistics. It is common to do this using a barplot with added lines indicating significant differences and interactions. How do you make plot like this using R? This is what I would like: Significant differences: Significant interactions: Background I am currently using barplot2{ggplots} to plot bars and confidence intervals, but I am willing to use any package/procedure to get the job done. To get the statistics I am currently using TukeyHSD{stats

Polychoric correlation matrix with significance in R

由 半城伤御伤魂 提交于 2019-11-30 10:28:29
I have been desperately looking for a way to compute a polychoric correlation matrix, with significance in R. If that is very hard then polychoric correlation between two variables with significance would be sufficient. What I have tried so far: library(polychor) poly <- polychor(var1,var2) poly <- polychor(DatM) #where DatM is a DF converted to matrix library(polycor) hetcor(Dat2) #I am however uncertain hetcor is something I would want if I am looking for polychoric correlation. library(psych) polychoric(Dat$for2a,smooth=TRUE,global=TRUE,polycor=FALSE, ML = FALSE, std.err=TRUE) None of these
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