library(sjPlot)
df <- read.csv(file="new_var_intadded.csv", header=T)
df2 <- read.csv(file="data.csv", header=T)
df3 <- merge(df, df2, by = "id")
reg1 <- lm(soc_mis ~ soc_acc + condition + soc_mis_int + ders_nonacceptance, data=df3)
summary(reg1)
##
## Call:
## lm(formula = soc_mis ~ soc_acc + condition + soc_mis_int + ders_nonacceptance,
## data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.76318 -0.51939 -0.03138 0.48685 2.09724
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.81433 0.33823 2.408 0.018010 *
## soc_acc 0.29453 0.08602 3.424 0.000916 ***
## conditionm -0.06034 0.15539 -0.388 0.698652
## soc_mis_int 0.49068 0.08949 5.483 3.5e-07 ***
## ders_nonacceptance -0.13857 0.06322 -2.192 0.030861 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7602 on 94 degrees of freedom
## (85 observations deleted due to missingness)
## Multiple R-squared: 0.3835, Adjusted R-squared: 0.3572
## F-statistic: 14.62 on 4 and 94 DF, p-value: 2.554e-09
plot_model(reg1, type="pred")
## Some of the focal terms are of type `character`. This may lead to
## unexpected results. It is recommended to convert these variables to
## factors before fitting the model.
## The following variables are of type character: `condition`
## $soc_acc
##
## $condition
##
## $soc_mis_int
##
## $ders_nonacceptance
reg2 <- lm(soc_mis ~ soc_acc + condition + soc_mis_int*ders_nonacceptance, data=df3)
summary(reg2)
##
## Call:
## lm(formula = soc_mis ~ soc_acc + condition + soc_mis_int * ders_nonacceptance,
## data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.76853 -0.51386 -0.02897 0.50789 2.00764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.11781 0.47823 2.337 0.021564 *
## soc_acc 0.29482 0.08611 3.424 0.000921 ***
## conditionm -0.05952 0.15555 -0.383 0.702875
## soc_mis_int 0.34261 0.18755 1.827 0.070944 .
## ders_nonacceptance -0.26726 0.15658 -1.707 0.091180 .
## soc_mis_int:ders_nonacceptance 0.06215 0.06916 0.899 0.371203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.761 on 93 degrees of freedom
## (85 observations deleted due to missingness)
## Multiple R-squared: 0.3888, Adjusted R-squared: 0.3559
## F-statistic: 11.83 on 5 and 93 DF, p-value: 7.23e-09
plot_model(reg2, type="int")
reg3 <- lm(soc_mis ~ soc_acc + condition + soc_mis_int + ders_strategies, data=df3)
summary(reg3)
##
## Call:
## lm(formula = soc_mis ~ soc_acc + condition + soc_mis_int + ders_strategies,
## data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.81880 -0.54447 -0.06544 0.51048 2.21265
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.58749 0.33817 1.737 0.085613 .
## soc_acc 0.30726 0.08767 3.505 0.000702 ***
## conditionm -0.05687 0.15951 -0.357 0.722231
## soc_mis_int 0.48872 0.09159 5.336 6.56e-07 ***
## ders_strategies -0.05700 0.06811 -0.837 0.404769
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7765 on 94 degrees of freedom
## (85 observations deleted due to missingness)
## Multiple R-squared: 0.3567, Adjusted R-squared: 0.3294
## F-statistic: 13.03 on 4 and 94 DF, p-value: 1.752e-08
plot_model(reg3, type="pred")
## Some of the focal terms are of type `character`. This may lead to
## unexpected results. It is recommended to convert these variables to
## factors before fitting the model.
## The following variables are of type character: `condition`
## $soc_acc
##
## $condition
##
## $soc_mis_int
##
## $ders_strategies
reg4 <- lm(soc_mis ~ soc_acc + condition + soc_mis_int*ders_strategies, data=df3)
summary(reg4)
##
## Call:
## lm(formula = soc_mis ~ soc_acc + condition + soc_mis_int * ders_strategies,
## data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.76224 -0.53390 -0.05788 0.51574 2.08888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.04046 0.51134 2.035 0.0447 *
## soc_acc 0.29628 0.08798 3.368 0.0011 **
## conditionm -0.06276 0.15926 -0.394 0.6944
## soc_mis_int 0.28516 0.19535 1.460 0.1477
## ders_strategies -0.24430 0.17278 -1.414 0.1607
## soc_mis_int:ders_strategies 0.08909 0.07556 1.179 0.2414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7749 on 93 degrees of freedom
## (85 observations deleted due to missingness)
## Multiple R-squared: 0.3662, Adjusted R-squared: 0.3321
## F-statistic: 10.75 on 5 and 93 DF, p-value: 3.581e-08
plot_model(reg4, type="int")
