In [5]:
hist(data$topic_num)
library("ggplot2")
library('interactions')
library('stargazer')
library("latex2exp")
data<-read.csv('zhihu_cateroy_spanning_interaction_2.csv')
head(data)
| X | title_length | log_lasting_days | max_level | content_distance | content_distance2 | content_distance_by_max_level | content_distance2_by_max_level | Mon | Tue | ⋯ | H_12_15 | H_15_18 | H_18_21 | log_follower_num | topic_num | knowledge_granularity | log_content_distance | log_content_distance2 | log_content_distance_by_max_level | log_content_distance2_by_max_level | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <int> | <int> | <dbl> | <int> | <dbl> | <dbl> | <dbl> | <dbl> | <int> | <int> | ⋯ | <int> | <int> | <int> | <dbl> | <int> | <int> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | 0 | 13 | 7.889084 | 5 | 0.3053453 | 0.09323573 | 1.5267263 | 0.4661786 | 0 | 0 | ⋯ | 0 | 0 | 0 | 8.494334 | 3 | 5 | -0.5137888 | 0.26397897 | 1.3198949 | 1.3198949 |
| 2 | 1 | 21 | 7.878913 | 7 | 0.5132803 | 0.26345670 | 3.5929623 | 1.8441969 | 0 | 0 | ⋯ | 1 | 0 | 0 | 8.128880 | 2 | 7 | -0.2888001 | 0.08340549 | 0.5838384 | 0.5838384 |
| 3 | 2 | 21 | 7.866339 | 6 | 0.5172706 | 0.26756884 | 3.1036234 | 1.6054130 | 0 | 0 | ⋯ | 0 | 0 | 0 | 4.927254 | 2 | 6 | -0.2854435 | 0.08147797 | 0.4888678 | 0.4888678 |
| 4 | 3 | 15 | 7.862882 | 5 | 0.1460233 | 0.02132279 | 0.7301163 | 0.1066140 | 0 | 0 | ⋯ | 0 | 1 | 0 | 5.209486 | 5 | 5 | -0.8326139 | 0.69324596 | 3.4662298 | 3.4662298 |
| 5 | 4 | 18 | 7.858641 | 8 | 0.0000000 | 0.00000000 | 0.0000000 | 0.0000000 | 1 | 0 | ⋯ | 0 | 0 | 1 | 5.627621 | 1 | 8 | -3.0000000 | 9.00000000 | 72.0000000 | 72.0000000 |
| 6 | 5 | 13 | 7.852828 | 7 | 0.1550947 | 0.02405435 | 1.0856626 | 0.1683805 | 0 | 1 | ⋯ | 0 | 0 | 0 | 9.427466 | 5 | 7 | -0.8066120 | 0.65062285 | 4.5543599 | 4.5543599 |
colnames(data)
max(data$topic_num)
quantile(data$topic_num, c(.7, .8, .9, .95, .99))
hist(data$topic_num)
model_1 = lm(log_follower_num ~poly(log_content_distance, 2) ,
data = data)
model_2 = lm(log_follower_num ~
poly(log_content_distance, 2)+ knowledge_granularity
+ title_length+log_lasting_days+Mon+Tue+Wed+Thu+Fri,
data = data)
model_3 = lm(log_follower_num ~
poly(log_content_distance, 2)*knowledge_granularity
+ title_length+log_lasting_days+Mon+Tue+Wed+Thu+Fri,
data = data)
model1 = lm(log_follower_num ~
poly(log_content_distance, 2) + knowledge_granularity
+title_length+log_lasting_days+
+Mon+Tue+Wed+Thu+Fri+H_0_3+H_3_6+H_6_9+H_9_12+H_12_15+H_15_18+H_18_21,
data = data)
model2 = lm(scale(log_follower_num) ~
poly(scale(log_content_distance), 2) + scale(knowledge_granularity)
+scale(title_length)+scale(log_lasting_days)+
+Mon+Tue+Wed+Thu+Fri+H_0_3+H_3_6+H_6_9+H_9_12+H_12_15+H_15_18+H_18_21,
data = data)
model3 = lm(log_follower_num ~
poly(log_content_distance, 2)*knowledge_granularity
+title_length+log_lasting_days+
+Mon+Tue+Wed+Thu+Fri+H_0_3+H_3_6+H_6_9+H_9_12+H_12_15+H_15_18+H_18_21,
data = data)
model4 = lm(scale(log_follower_num) ~
poly(scale(log_content_distance), 2)*scale(knowledge_granularity)
+scale(title_length)+scale(log_lasting_days)+
+Mon+Tue+Wed+Thu+Fri+H_0_3+H_3_6+H_6_9+H_9_12+H_12_15+H_15_18+H_18_21,
data = data)
library("lm.beta")
lm.beta(model3)
Call:
lm(formula = log_follower_num ~ poly(log_content_distance, 2) *
knowledge_granularity + title_length + log_lasting_days +
+Mon + Tue + Wed + Thu + Fri + H_0_3 + H_3_6 + H_6_9 + H_9_12 +
H_12_15 + H_15_18 + H_18_21, data = data)
Standardized Coefficients::
(Intercept)
NA
poly(log_content_distance, 2)1
2.360905e-01
poly(log_content_distance, 2)2
-2.993078e-02
knowledge_granularity
-1.271282e-02
title_length
-4.911254e-02
log_lasting_days
5.519531e-01
Mon
1.293321e-03
Tue
1.570922e-03
Wed
3.356974e-03
Thu
8.780974e-05
Fri
2.463941e-04
H_0_3
3.893721e-03
H_3_6
1.902362e-03
H_6_9
-1.509158e-04
H_9_12
-2.732252e-04
H_12_15
2.608573e-03
H_15_18
-2.536101e-03
H_18_21
5.640434e-04
poly(log_content_distance, 2)1:knowledge_granularity
-3.834813e-02
poly(log_content_distance, 2)2:knowledge_granularity
-2.401251e-02
install.packages("simpleboot")
The downloaded binary packages are in /var/folders/8b/hhnbt0nd4zsg2qhxc28q23w80000gn/T//RtmpC7gy6f/downloaded_packages
data$log_content_distance2[0:3]
# lm.boot Linear model bootstrap
library('simpleboot')
model_1 = lm(log_follower_num ~log_content_distance ,
data = data)
lboot <- lm.boot(model_1, R=10) # 199 bootstrap samples--too small to be useful
summary(lboot) # default summary
BOOTSTRAP OF LINEAR MODEL (method = rows)
Original Model Fit
------------------
Call:
lm(formula = log_follower_num ~ log_content_distance, data = data)
Coefficients:
(Intercept) log_content_distance
5.0534 0.7136
Bootstrap SD's:
(Intercept) log_content_distance
0.008198888 0.004218208
model_1R = lm(log_follower_num ~poly(log_content_distance, 2, raw = TRUE) ,
data = data)
summary(model_1R)
Call:
lm(formula = log_follower_num ~ poly(log_content_distance, 2,
raw = TRUE), data = data)
Residuals:
Min 1Q Median 3Q Max
-4.6111 -1.6659 -0.3541 1.4727 9.3109
Coefficients:
Estimate Std. Error t value
(Intercept) 4.620411 0.014875 310.622
poly(log_content_distance, 2, raw = TRUE)1 0.039602 0.021334 1.856
poly(log_content_distance, 2, raw = TRUE)2 -0.184738 0.005762 -32.062
Pr(>|t|)
(Intercept) <2e-16 ***
poly(log_content_distance, 2, raw = TRUE)1 0.0634 .
poly(log_content_distance, 2, raw = TRUE)2 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.123 on 404574 degrees of freedom
Multiple R-squared: 0.08921, Adjusted R-squared: 0.0892
F-statistic: 1.981e+04 on 2 and 404574 DF, p-value: < 2.2e-16
summary(model_1)
Call:
lm(formula = log_follower_num ~ poly(log_content_distance, 2),
data = data)
Residuals:
Min 1Q Median 3Q Max
-4.6111 -1.6659 -0.3541 1.4727 9.3109
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.018363 0.003338 1203.81 <2e-16 ***
poly(log_content_distance, 2)1 417.131008 2.123201 196.46 <2e-16 ***
poly(log_content_distance, 2)2 -68.074045 2.123201 -32.06 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.123 on 404574 degrees of freedom
Multiple R-squared: 0.08921, Adjusted R-squared: 0.0892
F-statistic: 1.981e+04 on 2 and 404574 DF, p-value: < 2.2e-16
summary(model_2)
Call:
lm(formula = log_follower_num ~ poly(log_content_distance, 2) +
knowledge_granularity + title_length + log_lasting_days +
Mon + Tue + Wed + Thu + Fri, data = data)
Residuals:
Min 1Q Median 3Q Max
-5.8617 -1.1492 -0.1254 1.0077 8.5124
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.592e-01 1.486e-02 24.172 < 2e-16 ***
poly(log_content_distance, 2)1 2.806e+02 1.831e+00 153.239 < 2e-16 ***
poly(log_content_distance, 2)2 -7.694e+01 1.771e+00 -43.454 < 2e-16 ***
knowledge_granularity -9.572e-03 1.599e-03 -5.985 2.16e-09 ***
title_length -1.075e-02 2.690e-04 -39.960 < 2e-16 ***
log_lasting_days 7.239e-01 1.642e-03 440.866 < 2e-16 ***
Mon 7.327e-03 8.897e-03 0.823 0.4102
Tue 8.452e-03 8.800e-03 0.961 0.3368
Wed 1.928e-02 8.761e-03 2.201 0.0278 *
Thu -2.644e-04 8.950e-03 -0.030 0.9764
Fri 6.313e-04 9.149e-03 0.069 0.9450
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.738 on 404566 degrees of freedom
Multiple R-squared: 0.3895, Adjusted R-squared: 0.3895
F-statistic: 2.581e+04 on 10 and 404566 DF, p-value: < 2.2e-16
summary(model_3)
Call:
lm(formula = log_follower_num ~ poly(log_content_distance, 2) *
knowledge_granularity + title_length + log_lasting_days +
Mon + Tue + Wed + Thu + Fri, data = data)
Residuals:
Min 1Q Median 3Q Max
-5.8478 -1.1494 -0.1253 1.0084 8.5337
Coefficients:
Estimate Std. Error
(Intercept) 3.978e-01 1.541e-02
poly(log_content_distance, 2)1 3.342e+02 5.091e+00
poly(log_content_distance, 2)2 -4.251e+01 6.281e+00
knowledge_granularity -1.575e-02 1.711e-03
title_length -1.073e-02 2.690e-04
log_lasting_days 7.236e-01 1.642e-03
Mon 7.463e-03 8.896e-03
Tue 8.751e-03 8.798e-03
Wed 1.967e-02 8.760e-03
Thu -7.504e-05 8.948e-03
Fri 7.868e-04 9.148e-03
poly(log_content_distance, 2)1:knowledge_granularity -1.040e+01 9.376e-01
poly(log_content_distance, 2)2:knowledge_granularity -5.813e+00 1.069e+00
t value Pr(>|t|)
(Intercept) 25.818 < 2e-16 ***
poly(log_content_distance, 2)1 65.640 < 2e-16 ***
poly(log_content_distance, 2)2 -6.769 1.30e-11 ***
knowledge_granularity -9.203 < 2e-16 ***
title_length -39.874 < 2e-16 ***
log_lasting_days 440.670 < 2e-16 ***
Mon 0.839 0.4015
Tue 0.995 0.3199
Wed 2.245 0.0247 *
Thu -0.008 0.9933
Fri 0.086 0.9315
poly(log_content_distance, 2)1:knowledge_granularity -11.097 < 2e-16 ***
poly(log_content_distance, 2)2:knowledge_granularity -5.438 5.39e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.738 on 404564 degrees of freedom
Multiple R-squared: 0.3897, Adjusted R-squared: 0.3897
F-statistic: 2.153e+04 on 12 and 404564 DF, p-value: < 2.2e-16
# write regression tabel to word
stargazer(model1, model2, model3, model4,
type = 'html',
out = 'regression_table2023_html.doc',
summary=FALSE
)
<table style="text-align:center"><tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr> <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr> <tr><td style="text-align:left"></td><td>log_follower_num</td><td>scale(log_follower_num)</td><td>log_follower_num</td><td>scale(log_follower_num)</td></tr> <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr> <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">poly(log_content_distance, 2)1</td><td>280.480<sup>***</sup></td><td></td><td>334.086<sup>***</sup></td><td></td></tr> <tr><td style="text-align:left"></td><td>(1.832)</td><td></td><td>(5.091)</td><td></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">poly(log_content_distance, 2)2</td><td>-76.863<sup>***</sup></td><td></td><td>-42.354<sup>***</sup></td><td></td></tr> <tr><td style="text-align:left"></td><td>(1.771)</td><td></td><td>(6.281)</td><td></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">knowledge_granularity</td><td>-0.010<sup>***</sup></td><td></td><td>-0.016<sup>***</sup></td><td></td></tr> <tr><td style="text-align:left"></td><td>(0.002)</td><td></td><td>(0.002)</td><td></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">title_length</td><td>-0.011<sup>***</sup></td><td></td><td>-0.011<sup>***</sup></td><td></td></tr> <tr><td style="text-align:left"></td><td>(0.0003)</td><td></td><td>(0.0003)</td><td></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">log_lasting_days</td><td>0.724<sup>***</sup></td><td></td><td>0.724<sup>***</sup></td><td></td></tr> <tr><td style="text-align:left"></td><td>(0.002)</td><td></td><td>(0.002)</td><td></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">poly(scale(log_content_distance), 2)1</td><td></td><td>126.073<sup>***</sup></td><td></td><td>124.914<sup>***</sup></td></tr> <tr><td style="text-align:left"></td><td></td><td>(0.823)</td><td></td><td>(0.845)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">poly(scale(log_content_distance), 2)2</td><td></td><td>-34.549<sup>***</sup></td><td></td><td>-33.177<sup>***</sup></td></tr> <tr><td style="text-align:left"></td><td></td><td>(0.796)</td><td></td><td>(0.807)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">scale(knowledge_granularity)</td><td></td><td>-0.008<sup>***</sup></td><td></td><td>-0.013<sup>***</sup></td></tr> <tr><td style="text-align:left"></td><td></td><td>(0.001)</td><td></td><td>(0.001)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">scale(title_length)</td><td></td><td>-0.049<sup>***</sup></td><td></td><td>-0.049<sup>***</sup></td></tr> <tr><td style="text-align:left"></td><td></td><td>(0.001)</td><td></td><td>(0.001)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">scale(log_lasting_days)</td><td></td><td>0.552<sup>***</sup></td><td></td><td>0.552<sup>***</sup></td></tr> <tr><td style="text-align:left"></td><td></td><td>(0.001)</td><td></td><td>(0.001)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">Mon</td><td>0.008</td><td>0.004</td><td>0.008</td><td>0.004</td></tr> <tr><td style="text-align:left"></td><td>(0.009)</td><td>(0.004)</td><td>(0.009)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">Tue</td><td>0.009</td><td>0.004</td><td>0.010</td><td>0.004</td></tr> <tr><td style="text-align:left"></td><td>(0.009)</td><td>(0.004)</td><td>(0.009)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">Wed</td><td>0.020<sup>**</sup></td><td>0.009<sup>**</sup></td><td>0.021<sup>**</sup></td><td>0.009<sup>**</sup></td></tr> <tr><td style="text-align:left"></td><td>(0.009)</td><td>(0.004)</td><td>(0.009)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">Thu</td><td>0.0004</td><td>0.0002</td><td>0.001</td><td>0.0002</td></tr> <tr><td style="text-align:left"></td><td>(0.009)</td><td>(0.004)</td><td>(0.009)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">Fri</td><td>0.001</td><td>0.001</td><td>0.002</td><td>0.001</td></tr> <tr><td style="text-align:left"></td><td>(0.009)</td><td>(0.004)</td><td>(0.009)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">H_0_3</td><td>0.032<sup>***</sup></td><td>0.014<sup>***</sup></td><td>0.032<sup>***</sup></td><td>0.014<sup>***</sup></td></tr> <tr><td style="text-align:left"></td><td>(0.012)</td><td>(0.005)</td><td>(0.012)</td><td>(0.005)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">H_3_6</td><td>0.032</td><td>0.014</td><td>0.032</td><td>0.014</td></tr> <tr><td style="text-align:left"></td><td>(0.021)</td><td>(0.010)</td><td>(0.021)</td><td>(0.010)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">H_6_9</td><td>-0.002</td><td>-0.001</td><td>-0.002</td><td>-0.001</td></tr> <tr><td style="text-align:left"></td><td>(0.014)</td><td>(0.006)</td><td>(0.014)</td><td>(0.006)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">H_9_12</td><td>-0.002</td><td>-0.001</td><td>-0.002</td><td>-0.001</td></tr> <tr><td style="text-align:left"></td><td>(0.009)</td><td>(0.004)</td><td>(0.009)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">H_12_15</td><td>0.016<sup>*</sup></td><td>0.007<sup>*</sup></td><td>0.016<sup>*</sup></td><td>0.007<sup>*</sup></td></tr> <tr><td style="text-align:left"></td><td>(0.009)</td><td>(0.004)</td><td>(0.009)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">H_15_18</td><td>-0.015</td><td>-0.007</td><td>-0.015</td><td>-0.007</td></tr> <tr><td style="text-align:left"></td><td>(0.009)</td><td>(0.004)</td><td>(0.009)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">H_18_21</td><td>0.003</td><td>0.002</td><td>0.003</td><td>0.002</td></tr> <tr><td style="text-align:left"></td><td>(0.009)</td><td>(0.004)</td><td>(0.009)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">poly(log_content_distance, 2)1:knowledge_granularity</td><td></td><td></td><td>-10.410<sup>***</sup></td><td></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td>(0.938)</td><td></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">poly(log_content_distance, 2)2:knowledge_granularity</td><td></td><td></td><td>-5.828<sup>***</sup></td><td></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td>(1.069)</td><td></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">poly(scale(log_content_distance), 2)1:scale(knowledge_granularity)</td><td></td><td></td><td></td><td>-8.430<sup>***</sup></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.759)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">poly(scale(log_content_distance), 2)2:scale(knowledge_granularity)</td><td></td><td></td><td></td><td>-4.720<sup>***</sup></td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.866)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td style="text-align:left">Constant</td><td>0.355<sup>***</sup></td><td>-0.004</td><td>0.394<sup>***</sup></td><td>-0.002</td></tr> <tr><td style="text-align:left"></td><td>(0.016)</td><td>(0.003)</td><td>(0.016)</td><td>(0.004)</td></tr> <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr> <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>404,577</td><td>404,577</td><td>404,577</td><td>404,577</td></tr> <tr><td style="text-align:left">R<sup>2</sup></td><td>0.390</td><td>0.390</td><td>0.390</td><td>0.390</td></tr> <tr><td style="text-align:left">Adjusted R<sup>2</sup></td><td>0.390</td><td>0.390</td><td>0.390</td><td>0.390</td></tr> <tr><td style="text-align:left">Residual Std. Error</td><td>1.738 (df = 404559)</td><td>0.781 (df = 404559)</td><td>1.738 (df = 404557)</td><td>0.781 (df = 404557)</td></tr> <tr><td style="text-align:left">F Statistic</td><td>15,185.870<sup>***</sup> (df = 17; 404559)</td><td>15,185.870<sup>***</sup> (df = 17; 404559)</td><td>13,598.870<sup>***</sup> (df = 19; 404557)</td><td>13,598.870<sup>***</sup> (df = 19; 404557)</td></tr> <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr> </table>
| Dependent variable: | ||||
| log_follower_num | scale(log_follower_num) | log_follower_num | scale(log_follower_num) | |
| (1) | (2) | (3) | (4) | |
| poly(log_content_distance, 2)1 | 280.480*** | 334.086*** | ||
| (1.832) | (5.091) | |||
| poly(log_content_distance, 2)2 | -76.863*** | -42.354*** | ||
| (1.771) | (6.281) | |||
| knowledge_granularity | -0.010*** | -0.016*** | ||
| (0.002) | (0.002) | |||
| title_length | -0.011*** | -0.011*** | ||
| (0.0003) | (0.0003) | |||
| log_lasting_days | 0.724*** | 0.724*** | ||
| (0.002) | (0.002) | |||
| poly(scale(log_content_distance), 2)1 | 126.073*** | 124.914*** | ||
| (0.823) | (0.845) | |||
| poly(scale(log_content_distance), 2)2 | -34.549*** | -33.177*** | ||
| (0.796) | (0.807) | |||
| scale(knowledge_granularity) | -0.008*** | -0.013*** | ||
| (0.001) | (0.001) | |||
| scale(title_length) | -0.049*** | -0.049*** | ||
| (0.001) | (0.001) | |||
| scale(log_lasting_days) | 0.552*** | 0.552*** | ||
| (0.001) | (0.001) | |||
| Mon | 0.008 | 0.004 | 0.008 | 0.004 |
| (0.009) | (0.004) | (0.009) | (0.004) | |
| Tue | 0.009 | 0.004 | 0.010 | 0.004 |
| (0.009) | (0.004) | (0.009) | (0.004) | |
| Wed | 0.020** | 0.009** | 0.021** | 0.009** |
| (0.009) | (0.004) | (0.009) | (0.004) | |
| Thu | 0.0004 | 0.0002 | 0.001 | 0.0002 |
| (0.009) | (0.004) | (0.009) | (0.004) | |
| Fri | 0.001 | 0.001 | 0.002 | 0.001 |
| (0.009) | (0.004) | (0.009) | (0.004) | |
| H_0_3 | 0.032*** | 0.014*** | 0.032*** | 0.014*** |
| (0.012) | (0.005) | (0.012) | (0.005) | |
| H_3_6 | 0.032 | 0.014 | 0.032 | 0.014 |
| (0.021) | (0.010) | (0.021) | (0.010) | |
| H_6_9 | -0.002 | -0.001 | -0.002 | -0.001 |
| (0.014) | (0.006) | (0.014) | (0.006) | |
| H_9_12 | -0.002 | -0.001 | -0.002 | -0.001 |
| (0.009) | (0.004) | (0.009) | (0.004) | |
| H_12_15 | 0.016* | 0.007* | 0.016* | 0.007* |
| (0.009) | (0.004) | (0.009) | (0.004) | |
| H_15_18 | -0.015 | -0.007 | -0.015 | -0.007 |
| (0.009) | (0.004) | (0.009) | (0.004) | |
| H_18_21 | 0.003 | 0.002 | 0.003 | 0.002 |
| (0.009) | (0.004) | (0.009) | (0.004) | |
| poly(log_content_distance, 2)1:knowledge_granularity | -10.410*** | |||
| (0.938) | ||||
| poly(log_content_distance, 2)2:knowledge_granularity | -5.828*** | |||
| (1.069) | ||||
| poly(scale(log_content_distance), 2)1:scale(knowledge_granularity) | -8.430*** | |||
| (0.759) | ||||
| poly(scale(log_content_distance), 2)2:scale(knowledge_granularity) | -4.720*** | |||
| (0.866) | ||||
| Constant | 0.355*** | -0.004 | 0.394*** | -0.002 |
| (0.016) | (0.003) | (0.016) | (0.004) | |
| Observations | 404,577 | 404,577 | 404,577 | 404,577 |
| R2 | 0.390 | 0.390 | 0.390 | 0.390 |
| Adjusted R2 | 0.390 | 0.390 | 0.390 | 0.390 |
| Residual Std. Error | 1.738 (df = 404559) | 0.781 (df = 404559) | 1.738 (df = 404557) | 0.781 (df = 404557) |
| F Statistic | 15,185.870*** (df = 17; 404559) | 15,185.870*** (df = 17; 404559) | 13,598.870*** (df = 19; 404557) | 13,598.870*** (df = 19; 404557) |
| Note: | *p<0.1; **p<0.05; ***p<0.01 | |||
p<-interact_plot(model3, pred =log_content_distance, modx = knowledge_granularity,
data = data, interval = TRUE,int.width = 0.8,
x.label = TeX("Knowledge Spanning (log)"), y.label = TeX("Appeal of Questions"),
main.title = "", legend.main = TeX("Granularity"),)
#p+xlim(0,1)+ylim(-1000,4000)
p
p<-interact_plot(model3, pred =log_content_distance, modx = knowledge_granularity,
modx.values = c(mean(data$knowledge_granularity)-2*sd(data$knowledge_granularity),
mean(data$knowledge_granularity)-sd(data$knowledge_granularity),
mean(data$knowledge_granularity),
mean(data$knowledge_granularity)+sd(data$knowledge_granularity),
mean(data$knowledge_granularity)+2*sd(data$knowledge_granularity)),
data = data, interval = TRUE,int.width = 0.8,
x.label = TeX("Knowledge Spanning (log)"), y.label = TeX("Appeal of Questions"),
main.title = "", legend.main = TeX("Granularity"),)
#p+xlim(0,1)+ylim(-1000,4000)
p
p<-interact_plot(model3, pred =log_content_distance, modx = knowledge_granularity,
modx.values = c(0, 2, 4, 6, 8, 10, 12, 14),
data = data, interval = TRUE,int.width = 0.8,
x.label = TeX("Knowledge Spanning (log)"), y.label = TeX("Appeal of Questions"),
main.title = "", legend.main = TeX("Granularity"),)
#p+xlim(0,1)+ylim(-1000,4000)
p
p<-interact_plot(model3, pred =log_content_distance, modx = knowledge_granularity,
modx.values = c(0, 2, 4, 6, 8, 10, 12, 14),
data = data, interval = TRUE,int.width = 0.8,
x.label = TeX("Knowledge Spanning (log)"), y.label = TeX("Appeal of Questions"),
main.title = "", legend.main = TeX("Granularity"),)
#p+xlim(0,1)+ylim(-1000,4000)
p
# save figures as pdf
pdf(file="interaction_all_latex.pdf", width = 5, height = 5)
p
dev.off()
p<-interact_plot(model3, pred =log_content_distance, modx = knowledge_granularity,
modx.values = c(0, 2, 4, 6, 8, 10, 12, 14),
data = data, interval = TRUE,int.width = 0.8,
x.label = "Knowledge Spanning (log)", y.label = "Appeal of Questions",
main.title = "", legend.main = "Granularity",)
#p+xlim(0,1)+ylim(-1000,4000)
p
pdf(file="interaction_all2.pdf", width = 5, height = 5)
#p+xlim(0,1)+ylim(-1000,4000)
p
dev.off()
library('effects')
e1.lm1 <- predictorEffect("log_content_distance", model3, xlevels=5)
plot(e1.lm1)
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