How to Save Summary(Lm) to a File

How to save summary(lm) to a file?

I think one option could be sink() which will output the results to a text file rather than the console. In the absence of your dataset I've used cars for an example:

sink("lm.txt")
print(summary(lm(cars$speed ~ cars$dist)))
sink()  # returns output to the console

lm.txt now looks like this:

Call:
lm(formula = cars$speed ~ cars$dist)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5293 -2.1550  0.3615  2.4377  6.4179 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  8.28391    0.87438   9.474 1.44e-12 ***
cars$dist    0.16557    0.01749   9.464 1.49e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.156 on 48 degrees of freedom
Multiple R-squared:  0.6511,    Adjusted R-squared:  0.6438 
F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12

@Roland 's suggestion of knitr is a bit more involved, but could be worth it because you can knit input, text output, and figures in to one report or html file easily.

How to save multiple summary(lm) to a file/files?

I don't know enough about R's internals to say why you need to do this, but if you wrap the summary call with a call to print your code works:

output_folder<-c("C:/temp/")
for(counter in 1:3){ #for loop it saves empty files
#counter<-2
x<-rnorm(100,0,1)
y<-rnorm(100,0,2)

lm.model<-lm(x~y)

sink(file=paste(output_folder, "RegressionSummary_", counter,".txt", sep = ""))
print(summary(lm.model))
sink()
}

How can I save my multiple lm summary output to a data frame or csv?

Sorry I wrongly closed this as a dupe at the beginning. Thanks to my friend for reopening it.

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For "mlm" model class this is very efficient.

First, you need to Fitting a linear model with multiple LHS

Then let fit be your fitted model object (of "mlm" and "lm" class), extract its coefficients by

beta <- coef(summary(fit))

This is a list of coefficient tables.

Then, let's collapse it into a data frame:

tab <- do.call(rbind.data.frame, beta)

Now you just write this data frame into a "csv" file as usual.

As a quick test, you can use the toy dataset provided in the linked thread.

Write Regression summary to the csv file in R

Provided your data set and your comments, I would do something like

abc <- read.table(text = "
Order.Week..BV.    Product.Number    Quantity    Net.ASP    Net.Price
1         2013-W44        ABCDEF       92  823.66       749
2         2013-W44        ABCDEF       24  898.89       749
3         2013-W44        ABCDEF      243  892.00       749
4         2013-W45        ABCDEF       88  796.84       699
5         2013-W45        ABCDEF       18  744.80       699", header = T) # Yor data

fit <- lm(Net.Price ~ Quantity + Net.ASP, data = abc)
x <- cbind(abc, t(as.numeric(coefficients(fit))), t(as.numeric(summary(fit)$coefficients[, 4])), summary(fit)$r.squared)
names(x)[(length(x) - 6):length(x)] <- c(paste("coeff", names(coefficients(fit))), paste("P-value", names(summary(fit)$coefficients[, 4])), "R-squared")

Which will return

  Order.Week..BV. Product.Number Quantity Net.ASP Net.Price coeff (Intercept) coeff Quantity coeff Net.ASP P-value (Intercept) P-value Quantity
1        2013-W44         ABCDEF       92  823.66       749          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
2        2013-W44         ABCDEF       24  898.89       749          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
3        2013-W44         ABCDEF      243  892.00       749          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
4        2013-W45         ABCDEF       88  796.84       699          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
5        2013-W45         ABCDEF       18  744.80       699          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
  P-value Net.ASP R-squared
1       0.1865054 0.7165826
2       0.1865054 0.7165826
3       0.1865054 0.7165826
4       0.1865054 0.7165826
5       0.1865054 0.7165826

Is there a way to export the linear regression summaries from R as an image? I need to export the summaries as .jpeg or as .png

The package "Stargazer" would let you export the summary into beautifully formatted html, LaTex or plain ASCII text.
Just open the resulting html file in a browser, copy it as is or capture it as an image and paste it into your presentation.

library(stargazer)
data <- as.data.frame(cbind(a = rnorm(30), b = rnorm(30)))
fit_lm <- lm(data, formula = a ~ b)
stargazer(fit_lm, type = "html", out = "fit_lm.html")

PNG with the lm summary

Save the result from summary(lm) to use in PowerBi

Try something like this:

fit <- lm(hwy ~ displ, data = mpg)
txt = capture.output(print(summary(fit)))

plot(NULL,xlim=c(-1,1),ylim=c(-1,1),xaxt="n",yaxt="n",bty="n",xlab="",ylab="")
text(x=0,y=0,paste(txt,collapse="\n"))

You might need to look at using stringr::str_pad to make the text prettier.. but this should get you something that works.

This is how you put it on ggplot2:

ggplot() + xlim(c(-1,1)) + ylim(c(-1,1)) + 
geom_text(aes(x=0,y=0,label=paste(txt,collapse="\n"))) + 
    theme_void()

Print OLS regression summary to text file

In order to write out the result of pandas.stats.api.ols, use a text file to match the output format, for instance:

from pandas.stats.api import ols
grps = df.groupby(['FID'])
for fid, grp in grps:
    result = ols(y=grp.loc[:, 'MEAN'], x=grp.loc[:, ['Accum_Prcp', 'Accum_HDD']])

    text_file = open("Output {}.txt".format(fid), "w")
    text_file.write(result.summary)
    text_file.close()

How to export summary statistics of a regression from R studio to Excel

Let's assume we have model called "mod"
mod <- lm(v1 ~ v2, data= df)

You can use "broom" function.

library(broom)

#create dataframe with results
res_mod <- as.data.frame(tidy(mod))
res_mod2 <- as.data.frame(glance(mod))

#then export as csv
write.csv(res_mod, "res_mod.csv)
write.csv(res_mod2, "res_mod.csv)

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