问题
I want to extract the coefficients (estimates, tvalues, etc) of LM ouputs to a dataframe. I need to store all coefficients in a dataframe for all regression outputs, since I have 949 separate outputs. The PROBLEM is that some of the outputs include NA's for a number of variables. When I export these summaries, it excludes the NA's and only outputs the variables that have true values.
Since I need to bind all the values in rows, I want to maintain the same structure of all estimates (and so NA's included), otherwise the columns do not match the values anymore.
A minimal working example:
Call:
lm(formula = dy ~ ., data = x)
Residuals:
Min 1Q Median 3Q Max
-0.223091 -0.036780 -0.001159 0.039722 0.216093
Coefficients: (8 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.240e+00 1.192e+00 4.395 1.84e-05 ***
deltalnPrice -4.385e-01 7.486e-02 -5.858 2.02e-08 ***
deltalnAdvertising NA NA NA NA
deltalnDisplay 6.526e-03 2.701e-03 2.416 0.016643 *
deltaIntrayearCycles -3.591e-03 1.899e-02 -0.189 0.850257
deltalnCompetitorPrices -1.149e+00 3.389e-01 -3.389 0.000852 ***
deltalnCompADV 3.107e-04 1.225e-03 0.254 0.800020
deltalnCompDISP -5.746e-03 3.405e-03 -1.688 0.093112 .
deltaADVxDISP NA NA NA NA
deltaADVxCYC NA NA NA NA
deltaDISPxCYC -3.156e-03 1.824e-03 -1.730 0.085186 .
deltaADVxDISPxCYC NA NA NA NA
lnPriceLag1 1.003e-01 1.060e-01 0.946 0.345190
lnAdvertisingLag1 NA NA NA NA
lnDisplayLag1 -2.517e-05 2.917e-03 -0.009 0.993125
IntrayearCyclesLag1 2.086e-03 7.750e-03 0.269 0.788068
lnCompetitorPricesLag1 -1.509e-01 1.213e-01 -1.244 0.214992
lnCompADVLag1 6.551e-04 1.331e-03 0.492 0.623267
lnCompDISPLag1 -9.989e-03 4.112e-03 -2.430 0.016044 *
ADVxDISPLag1 NA NA NA NA
ADVxCYCLag1 NA NA NA NA
DISPxCYCLag1 -1.590e-03 2.412e-03 -0.659 0.510408
ADVxDISPxCYCLag1 NA NA NA NA
yLag1 -3.964e-01 5.747e-02 -6.898 7.52e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07287 on 191 degrees of freedom
Multiple R-squared: 0.5067, Adjusted R-squared: 0.468
F-statistic: 13.08 on 15 and 191 DF, p-value: < 2.2e-16
structure(list(call = lm(formula = dy ~ ., data = x), terms = dy ~
deltalnPrice + deltalnAdvertising + deltalnDisplay + deltaIntrayearCycles +
deltalnCompetitorPrices + deltalnCompADV + deltalnCompDISP +
deltaADVxDISP + deltaADVxCYC + deltaDISPxCYC + deltaADVxDISPxCYC +
lnPriceLag1 + lnAdvertisingLag1 + lnDisplayLag1 + IntrayearCyclesLag1 +
lnCompetitorPricesLag1 + lnCompADVLag1 + lnCompDISPLag1 +
ADVxDISPLag1 + ADVxCYCLag1 + DISPxCYCLag1 + ADVxDISPxCYCLag1 +
yLag1, residuals = structure(c(0.0313162134166014, 0.00182250788959792,
0.0635208598048524, 0.0612908975008862, 0.0513116273377191, 0.0480400382837013,
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"193", "194", "195", "196", "197", "198", "199", "200", "201",
"202", "203", "204", "205", "206", "207", "208")), coefficients = structure(c(5.24015901393355,
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4L), .Dimnames = list(c("(Intercept)", "deltalnPrice", "deltalnDisplay",
"deltaIntrayearCycles", "deltalnCompetitorPrices", "deltalnCompADV",
"deltalnCompDISP", "deltaDISPxCYC", "lnPriceLag1", "lnDisplayLag1",
"IntrayearCyclesLag1", "lnCompetitorPricesLag1", "lnCompADVLag1",
"lnCompDISPLag1", "DISPxCYCLag1", "yLag1"), c("Estimate", "Std. Error",
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FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE,
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"deltaDISPxCYC", "deltaADVxDISPxCYC", "lnPriceLag1", "lnAdvertisingLag1",
"lnDisplayLag1", "IntrayearCyclesLag1", "lnCompetitorPricesLag1",
"lnCompADVLag1", "lnCompDISPLag1", "ADVxDISPLag1", "ADVxCYCLag1",
"DISPxCYCLag1", "ADVxDISPxCYCLag1", "yLag1")), sigma = 0.0728717673227287,
df = c(16L, 191L, 24L), r.squared = 0.506700558626737, adj.r.squared = 0.467959764801612,
fstatistic = structure(c(13.0792507999182, 15, 191), .Names = c("value",
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-0.00156052231157431, 0.174029022237292, -0.00304719425523143,
0.00669394653700606, 0.00419699606459184, 0.621917114099314
), .Dim = c(16L, 16L), .Dimnames = list(c("(Intercept)",
"deltalnPrice", "deltalnDisplay", "deltaIntrayearCycles",
"deltalnCompetitorPrices", "deltalnCompADV", "deltalnCompDISP",
"deltaDISPxCYC", "lnPriceLag1", "lnDisplayLag1", "IntrayearCyclesLag1",
"lnCompetitorPricesLag1", "lnCompADVLag1", "lnCompDISPLag1",
"DISPxCYCLag1", "yLag1"), c("(Intercept)", "deltalnPrice",
"deltalnDisplay", "deltaIntrayearCycles", "deltalnCompetitorPrices",
"deltalnCompADV", "deltalnCompDISP", "deltaDISPxCYC", "lnPriceLag1",
"lnDisplayLag1", "IntrayearCyclesLag1", "lnCompetitorPricesLag1",
"lnCompADVLag1", "lnCompDISPLag1", "DISPxCYCLag1", "yLag1"
)))), .Names = c("call", "terms", "residuals", "coefficients",
"aliased", "sigma", "df", "r.squared", "adj.r.squared", "fstatistic",
"cov.unscaled"), class = "summary.lm")
These exports are also separate objects in my environment and I write a for loop to extract these values as dataframe:
for(X in c("0"){
ModelX <- get(paste0("C", X, "B2"))
allparamest <- ModelX$coefficients}
Model X then corresponds to the specific model in my environment.
If I want to read one summary output, I need to use the print() function instead of summary(). For one specific list object I would get this:
> print(C0B3)
Call:
lm(formula = dy ~ ., data = x)
Residuals:
Min 1Q Median 3Q Max
-0.223091 -0.036780 -0.001159 0.039722 0.216093
Coefficients: (8 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.240e+00 1.192e+00 4.395 1.84e-05 ***
deltalnPrice -4.385e-01 7.486e-02 -5.858 2.02e-08 ***
deltalnAdvertising NA NA NA NA
deltalnDisplay 6.526e-03 2.701e-03 2.416 0.016643 *
deltaIntrayearCycles -3.591e-03 1.899e-02 -0.189 0.850257
deltalnCompetitorPrices -1.149e+00 3.389e-01 -3.389 0.000852 ***
deltalnCompADV 3.107e-04 1.225e-03 0.254 0.800020
deltalnCompDISP -5.746e-03 3.405e-03 -1.688 0.093112 .
deltaADVxDISP NA NA NA NA
deltaADVxCYC NA NA NA NA
deltaDISPxCYC -3.156e-03 1.824e-03 -1.730 0.085186 .
deltaADVxDISPxCYC NA NA NA NA
lnPriceLag1 1.003e-01 1.060e-01 0.946 0.345190
lnAdvertisingLag1 NA NA NA NA
lnDisplayLag1 -2.517e-05 2.917e-03 -0.009 0.993125
IntrayearCyclesLag1 2.086e-03 7.750e-03 0.269 0.788068
lnCompetitorPricesLag1 -1.509e-01 1.213e-01 -1.244 0.214992
lnCompADVLag1 6.551e-04 1.331e-03 0.492 0.623267
lnCompDISPLag1 -9.989e-03 4.112e-03 -2.430 0.016044 *
ADVxDISPLag1 NA NA NA NA
ADVxCYCLag1 NA NA NA NA
DISPxCYCLag1 -1.590e-03 2.412e-03 -0.659 0.510408
ADVxDISPxCYCLag1 NA NA NA NA
yLag1 -3.964e-01 5.747e-02 -6.898 7.52e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07287 on 191 degrees of freedom
Multiple R-squared: 0.5067, Adjusted R-squared: 0.468
F-statistic: 13.08 on 15 and 191 DF, p-value: < 2.2e-16
回答1:
Here are two options using the stargazer or tidy functions.
set.seed(101)
#data
dat <- data.frame(one=c(sample(1000:1239)),
two=c(sample(200:439)),
three=c(sample(600:839)),
Jan=c(rep(1,20),rep(0,220)),
Feb=c(rep(0,20),rep(1,20),rep(0,200)),
Mar=c(rep(0,40),rep(1,20),rep(0,180)),
Apr=c(rep(0,60),rep(1,20),rep(0,160)),
May=c(rep(0,80),rep(1,20),rep(0,140)),
Jun=c(rep(0,100),rep(1,20),rep(0,120)),
Jul=c(rep(0,120),rep(1,20),rep(0,100)),
Aug=c(rep(0,140),rep(1,20),rep(0,80)),
Sep=c(rep(0,160),rep(1,20),rep(0,60)),
Oct=c(rep(0,180),rep(1,20),rep(0,40)),
Nov=c(rep(0,200),rep(1,20),rep(0,20)),
Dec=c(rep(0,220),rep(1,20)))
#model
model <- lm(one ~ two + three + Jan + Feb + Mar + Apr +
May + Jun + Jul + Aug + Sep + Oct + Nov + Dec,
data=dat)
summary(model) # NA for covariate Dec
## export
# with stargazer
library(stargazer)
stargazer(model, type = "text") # includes Dec
# with broom (convert lm result to data frame)
library(broom); library(dplyr)
tidy(model, quick = TRUE) # with Dec but without se, t.val, p.val
tidy(model, quick = FALSE) # with se, t.val, p.val but without Dec
df <- left_join(tidy(model, quick = TRUE),
tidy(model, quick = FALSE),
by = c("term", "estimate")) # includes Dec, se ...
来源：https://stackoverflow.com/questions/47891753/exporting-lm-summary-output-to-dataframe-including-na